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Air Quality Modeling Technical Support
Document for Regulatory Impact Analysis of the
Standards of Performance for New, Reconstructed,
and Modified Sources and Emissions Guidelines
for Existing Sources: Oil and Natural Gas Sector
Climate Review
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EPA-454/R-23-007
November 2023
Air Quality Modeling Technical Support Document for Regulatory Impact Analysis of the Standards
of Performance for New, Reconstructed, and Modified Sources and Emissions Guidelines for
Existing Sources: Oil and Natural Gas Sector Climate Review
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
1. Introduction 2
2. Air Quality Modeling Platform 2
2.1 Air Quality Model Configuration and Model Simulations 3
2.2 Meteorological Data for 2016 5
2.3 Initial and Boundary Concentrations 7
2.5 Air Quality Model Evaluation 8
3. Ozone Contribution Modeling and Methodology for Calculating Ozone Impacts of the
Final Rule 8
3.1 Description of 2026 State-by-State Oil and Natural Gas Sector Source Apportionment Modeling 9
3.2 Source Apportionment Scaling Method for Calculating the Ozone Impacts of the Final Rule 18
4. Evaluation of CAMx Model Ozone Predictions Against Ambient Ozone Measurements in
2016 26
5. Evaluation of Methods for Estimating Ozone Impacts based on Oil & Gas Source
Apportionment Modeling using Two Emissions Sensitivity Scenarios 36
6. Uncertainties and Limitations of the Air Quality Methodology 43
7. References 45
1
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1. Introduction
In this technical support document (TSD) we describe the air quality modeling performed to
support EPA's "Regulatory Impact Analysis of the Standards of Performance for New,
Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural
Gas Sector Climate Review." For this rule, EPA used photochemical modeling to create ozone
surfaces1 that were then used in air pollution health benefits calculations of three regulatory
alternatives: the final rule, a less stringent alternative and a more stringent alternative. The modeling-
based ozone surfaces are used to quantify ozone impacts resulting from changes to VOC emissions
from oil and natural gas sources affected by this rule. As described in this TSD, EPA performed air
quality modeling for a 2016 base year and a 2026 future year. Ozone source apportionment modeling
was performed for 2026 to characterize the relationship between state-level oil and natural gas-related
VOC emissions with ozone concentrations. The 2026 source apportionment modeling was then used
to create ozone surfaces for the baseline and regulatory alternatives in each of four years of analysis:
2024, 2027, 2028 and 2038.
The remaining sections of this TSD are as follows. Section 2 describes the air quality
modeling platform. Section 3 describes the 2026 source apportionment modeling and the procedures
for applying source apportionment modeling and projected emissions changes from the rule to
estimate ozone impacts. Section 4 describes the evaluation of 2016 model predictions of 8-hour (hr)
daily maximum (MDA8) ozone concentrations using measured (i.e., observed) data. Section 5
describes an assessment conducted to compare source apportionment-based estimated impacts to full-
scale air quality modeling for two emissions reduction scenarios relevant to the oil and natural gas
sector. Section 6 summarizes uncertainties and limitations of the ozone surface estimates.
2. Air Quality Modeling Platform
The EPA used a 2016-based air quality modeling platform to provide the foundational
model-input data sets for 2016 and the future years of analysis. These inputs include emissions for
2016 and 2026 developed for the "2016v2" emissions modeling platform as well as meteorology,
initial and boundary condition concentrations, and other inputs representative of the 2016 base year.
The 2016v2 emissions modeling platform is described in US EPA (2022a). The meteorological and
1 "ozone surfaces" refers to continuous gridded spatial fields using a 12 x 12 km resolution grid-cell resolution
2
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initial and boundary condition data used as input to the air quality modeling and the model
performance evaluation for MDA8 ozone are described below.
2.1 Air Quality Model Configuration and Model Simulations
The photochemical model simulations performed for this proposed rule used the
Comprehensive Air Quality Model with Extensions (CAMx version 7.10, Ramboll, 2021). CAMx is a
three-dimensional grid-based Eulerian air quality model designed to simulate the formation and fate
of oxidant precursors, primary and secondary particulate matter concentrations, and deposition over
regional and urban spatial scales (e.g., the contiguous U.S.). Consideration of the different processes
(e.g., transport and deposition) that affect primary (directly emitted) and secondary (formed by
atmospheric processes) pollutants at the regional scale in different locations is fundamental to
understanding and assessing the effects of emissions on air quality concentrations. For this
assessment, EPA used the CAMx Anthropogenic Precursor Culpability Analysis (APCA) technique2
to model ozone contributions that are then applied to estimate ozone impacts of this final rule as
described below in section 3.
The geographic extent of the modeling domains that were used for air quality modeling in this
analysis are shown in Figure 2-1. The large outer domain covers the 48 contiguous states along with
most of Canada and all of Mexico with a horizontal resolution of 36 x 36 km (i.e., 36 km domain).
The inner domain covers the 48 contiguous states along with adjacent portions of Canada and Mexico
at 12 x 12 km resolution (i.e., 12 km domain).
2As part of this technique, ozone formed from reactions between biogenic VOC and NOx with anthropogenic
NOx and VOC are assigned to the source of anthropogenic emissions.
3
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Figure 2-1. Air quality modeling domains.
CAMx requires a variety of input files that contain information pertaining to the modeling
domain and simulation period. These include gridded, hourly emissions estimates and meteorological
data, and initial and boundary concentrations. Separate emissions inventories were prepared for the
2016 base year and the 2026 future year. All other inputs (i.e., meteorological fields, initial
concentrations, ozone column, photolysis rates, and boundary concentrations) were specified for the
2016 base year model application and remained unchanged for the projection-year model
simulations.3
The 12 km CAMx model simulations performed for this proposed rule are listed in Table 2-1.
The simulation period for each run was preceded by a 15-day ramp-up period. Also, the 2026fj oil
and natural gas state-sector source apportionment model simulations, 20261] oilgassa, were
performed for April through September in order to provide sector contribution data that aligns with
the April through September average MDA8 concentration, which is the primary health-based metric
used to inform the ozone benefits analysis in the RIA.
3 EPA used the CAMx7. lchemparam.CB6r5_CF2E chemical parameter file for all the CAMx model runs described in
this TSD.
4
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Table 2-1. Model run name, case name and simulation period for each model run.
Year
Model Run
Case Name
Simulation Period
2016
2016 baseline
2016fj
Annual
2026
2026 baseline
2026fj
Annual
2026 state O&G contributions
2026fj_oilgassa
April - September4
2.2 Meteorological Data for 2016
This section describes the meteorological modeling that was performed to provide
meteorological data for 2016 for input to air quality modeling. The 2016 meteorological data were
derived from running Version 3.8 of the Weather Research Forecasting Model (WRF) (Skamarock, et
al., 2008). The meteorological outputs from WRF include hourly-varying horizontal wind
components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall
rates for each grid cell in each vertical layer. Selected physics options used in the WRF simulations
include Pleim-Xiu land surface model (Xiu and Pleim, 2001; Pleim and Xiu, 2003), Asymmetric
Convective Model version 2 planetary boundary layer scheme (Pleim 2007a,b), Kain-Fritsch cumulus
parameterization (Kain, 2004) utilizing the moisture-advection trigger (Ma and Tan, 2009), Morrison
double moment microphysics (Morrison et al., 2005; Morrison and Gettelman, 2008), and RRTMG
longwave and shortwave radiation schemes (Iacono et.al., 2008).
Both the 36 km and 12 km WRF model simulations utilize a Lambert conformal projection
centered at (-97,40) with true latitudes of 33 and 45 degrees north. The 36 km domain contains 184
cells in the X direction and 160 cells in the Y direction. The 12 km domain contains 412 cells in the
X direction and 372 cells in the Y direction. The atmosphere is resolved with 35 vertical layers up to
50 millibar (see Table 2-2), with the thinnest layers being nearest the surface to better resolve the
planetary boundary layer (PBL).
The 36 km WRF model simulation was initialized using the 0.25-degree GFS analysis and 3-
hour forecast from the 00 GMT, 06 GMT, 12 GMT, and 18 GMT simulations. The 12 km model was
initialized using the 12 km North American Model (12NAM) analysis product provided by National
Climatic Data Center (NCDC).5 The 40 km Eta Data Assimilation System (EDAS) analysis (ds609.2)
4 Because the model simulations ran in Greenwich Mean Time (GMT), the actual simulation period included October 1 in
order to obtain MDA8 ozone concentrations based on local time for September 30.
5 https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-mesoscale-forecast-SYStem-nam
5
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from the National Center for Atmospheric Research (NCAR) was used where 12NAM data was
unavailable.6 Analysis nudging for temperature, wind, and moisture was applied above the boundary
layer only. The model simulations were conducted continuously. The "ipxwrf' program was used to
initialize deep soil moisture at the start of the run using a 10-day spin-up period (Gilliam and Pleim,
2010). Land use and land cover data were based on the USGS for the 36NOAM simulation and the
2011 National Land Cover Database (NLCD 2011) for the 12US simulation. Sea surface
temperatures were ingested from the Group for High Resolution Sea Surface Temperatures
(GHRSST) (Stammer et al., 2003) 1 km SST data. Additionally, lightning data assimilation was
utilized to suppress (force) deep convection where lightning is absent (present) in observational data.
This method is described by Heath et al. (2016) and was employed to help improve precipitation
estimates generated by the model.
Table 2-2. Vertical layers and their approximate height above ground level.
WRF Layer
Height (m)
Pressure
(mb)
Sigma
35
17,556
5000
0.000
34
14,780
i9750
0.050
33
12,822
14500
0.100
32
11,282
19250
0.150
31
10,002
24000
0.200
30
8,901
1 28750
0.250
29
; 7,932
33500
0.300
28
7,064
38250
0.350
27
' 6,275
43000
0.400
26
1 5,553
[47750
0.450
25
4,885
52500
0.500
24
4,264
1 57250
0.550
23
3,683
62000
0.600
22
3,136
66750
0.650
21
2,619
71500
; 0.700
20
1 2,226
775300
0.740
19
1,941
78150
0.770
18
1,665
81000
0.800
17
1,485
82900
0.820
16
1,308
84800
0.840
15
1,134
86700
0.860
14
964
88600
0.880
13
; 797
90500
0.900
12
714
91450
0.910
6 https ://www. ready. noaa.gov/edas40 .php.
6
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II
632
92400
0.920
10
551
93350
0.930
9
470
94300
0.940
8
390
95250
0.950
7
3 1 1
96200
0.960
6
j 232
97150
0.970
5
154
98100
0.980
4
1 15
98575
0.985
3
77
99050
0.990
2
38
99525
0.995
1
19
99763
0.9975
Surface
0
100000
1.000
Details of the annual 2016 meteorological model simulation and evaluation are provided in US EPA
(2019).
The meteorological data generated by the WRF simulations were processed using wrfcamx
v4.7 (Ramboll 2021) meteorological data processing program to create 35-layer gridded model-ready
meteorological inputs to CAMx. In running wrfcamx, vertical eddy diffusivities (Kv) were calculated
using the Yonsei University (YSU) (Hong et al., 2006) mixing scheme. We used a minimum Kv of
0.1 m2/sec except for urban grid cells where the minimum Kv was reset to 1.0 m2/sec within the
lowest 200 m of the surface in order to enhance mixing associated with the nighttime "urban heat
island" effect. In addition, we invoked the subgrid convection and subgrid stratoform cloud options in
our wrfcamx run for 2016.
2.3 Initial and Boundary Concentrations
The lateral boundary and initial species concentrations for the 36 km simulations were derived
from outputs of a three-dimensional hemispheric atmospheric chemistry model, the Hemispheric
version of the Community Multi-scale Air Quality Model (H-CMAQ) version 3.1.1 which was run
for 20167. The H-CMAQ predictions were used to provide one-way dynamic boundary
concentrations at one-hour intervals and an initial concentration field for the 36 km CAMx
simulations for 2016 and 2023.
7 More information about the H-CMAQ model and other applications using this tool is available at:
https://www.epa.gov/cmaa/hemispheric-scale-applications. Note that EPA used the same initial and boundary conditions
for the 2016v2 air quality modeling as was used for the 2016vl air quality modeling.
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Air quality modeling for the 36 km domain was used to provide initial and boundary
conditions for the nested 12 km domain model simulations. Both the 36 km and 12 km modeling
domains have 35 vertical layers with a top at about 17,550 meters, or 50 millibars (mb). The model
simulations produce hourly air quality concentrations for each grid cell across each modeling domain.
Modeling for the 36 km domain was performed for 2016 and 2023. Outputs from the 2016 36 km
simulation were used to provide initial and boundary conditions for the 2016 12 km model
simulation. Outputs from the 2023 36 km simulation were used to provide initial and boundary
conditions for the 2026 12 km simulations.
2.5 Air Quality Model Evaluation
An operational model performance evaluation for ozone was conducted to examine the ability
of the CAMx modeling system to simulate 2016 measured MDA8 ozone concentrations. This
evaluation focused on graphical analyses and statistical metrics of model predictions versus
observations. Details on the evaluation methodology, the calculation of performance statistics, and
results are provided in Section 4. Overall, the ozone model performance statistics for the CAMx
20161] simulation are within or close to the ranges found in peer-reviewed applications (e.g., Simon
et al, 2012 and Emery et al, 2017). As described in Section 4, the predictions from the 2016v2
modeling platform correspond closely to observed concentrations in terms of the magnitude, temporal
fluctuations, and geographic differences for MDA8 ozone. Thus, the model performance results
demonstrate the scientific credibility of our 2016v2 modeling platform. These results provide
confidence in the ability of the modeling platform to provide a reasonable projection of expected
future year ozone concentrations and contributions.
3. Ozone Contribution Modeling and Methodology for Calculating Ozone Impacts
of the Final Rule
EPA performed state-by-state8 ozone source apportionment modeling for oil and
natural gas sources using the CAMx APCA technique for the 12 km resolution modeling
domain shown in Figure 2-1. This modeling provides data on the ozone contributions from
projected 2026 base case VOC emissions from oil and natural gas source types covered by
8 while we bundle some states and split up TX, we use term "state-by-state" for convenience
8
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this rulemaking in each state. The state-by-state oil and natural gas sector source
apportionment modeling is described in Section 3.1. In Section 3.2 we describe the method for
approximating the ozone impacts of the regulatory alternatives using these modeling outputs.
The basic methodology for determining air quality changes described in Section 3.2 is the
same as that used in the RIAs from multiple previous rules (U.S. EPA, 2019b, 2020a, 2020b,
2021, 2023a, 2023b, 2023c, 2023d).
3.1 Description of 2026 State-by-State Oil and Natural Gas Sector Source Apportionment
Modeling
In the state-by-state source apportionment model run, we tracked the ozone formed from
each of the following contribution categories (i.e., "tags"):
• Oil and Natural Gas State-Level Emissions - anthropogenic NOx and VOC emissions
from oil and natural gas source types covered by this rulemaking9 tracked individually
from each of the contiguous 48 states.10'11'12 Table 3-3 provides Annual NOx and VOC
emissions associated with each state-level oil and natural gas tag.
• Other13 - all other emissions in the 12 km domain including biogenic NOx and VOC
emissions domain-wide, combined emissions from wild and prescribed fires, all
anthropogenic NOx and VOC emissions from Canada and Mexico, all US anthropogenic
NOx and VOC emissions that were not included in one of the oil and natural gas state-level
tags;
• Initial and Boundary Concentrations - air quality concentrations used to initialize the 12
km model simulation and air quality concentrations transported into the 12 km modeling
9 Table 3-1 identifies SCCs associated with source types covered by this rulemaking for the purpose of this modeling
exercise
10 No tags were included for the following states that have no emissions from the source types listed in Table 3-1: CT, DE,
GA, IA, MA, ME, MN, NC, NH, RI, VT, WI,WA
11 Oil and gas emissions from the state of TX were split into 3 separate sub-state regional tags as described in Table 3-2
12 States with less than 100 tpy of VOC emissions from the source types listed in Table 3-1 were combined into multi-
state tags as shown in Table 3-3
13 Ozone contributions from separate tags tracking biogenic emissions, fire emissions, Canadian and Mexican
anthropogenic emissions combined, offshore emissions, and state-by-state total anthropogenic emissions are available for
the 2026fj case from modeling conducted to support the Federal Implementation Plan Addressing Regional Ozone
Transport for the 2015 Ozone National Ambient Air Quality Standards Proposed Rulemaking and are available in the
docket for that rulemaking.
9
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domain from the lateral boundaries
The tagged sources account for all ozone sources simulated by the model such that the sum of
tagged ozone contributions adds to the total modeled ozone at each hour and grid cell.
Table 3-1. List of Oil and Natural Gas Source Types Tracked in the State-Level Oil and Natural
Gas Tags
Source
Classification
Code (SCC)
31000101
31000130
31000132
31000133
31000151
31000152
31000153
31000207
31000212
31000213
31000214
31000220
31000225
31000231
31000233
31000235
Description
Industrial Processes;Oil and Gas Production;Crude Oil Production;Well
Completion;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Fugitives:
Compressor Seals;
Industrial Processes;Oil and Gas Production;Crude Oil
Production; Atmospheric Wash Tank (2nd Stage of Gas-Oil Separation):
Flashing Loss;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Storage
Tank;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Pneumatic
Controllers, Low Bleed;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Pneumatic
Controllers High Bleed >6 scfh;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Pneumatic
Controllers Intermittent Bleed;
Industrial Processes;Oil and Gas Production;Natural Gas Production;Valves:
Fugitive Emissions;
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Condensate Storage Tank;
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Produced Water Storage Tank;
Industrial Processes;Oil and Gas Production;Natural Gas Production;Natural
Gas Liquids Storage Tank;
Industrial Processes;Oil and Gas Production;Natural Gas Production;All
Equipt Leak Fugitives (Valves, Flanges, Connections, Seals, Drains;
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Compressor Seals;
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Fugitives: Drains;
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Pneumatic Controllers, Low Bleed;
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Pneumatic Controllers Intermittent Bleed;
10
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31000309
31000324
31000325
31000326
31088811
2310010200
2310010300
2310010700
2310011020
2310011500
2310011501
2310011502
2310011503
2310011504
2310011505
2310011506
2310021010
2310021300
2310021310
2310021500
2310021501
2310021502
2310021503
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Compressor Seals;
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Pneumatic Controllers Low Bleed;
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Pneumatic Controllers, High Bleed >6 scfh;
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Pneumatic Controllers Intermittent Bleed;
Industrial Processes;Oil and Gas Production;Fugitive Emissions;Fugitive
Emissions;
Industrial Processes;Oil and Gas Exploration and Production;Crude
Petroleum;Oil Well Tanks - Flashing & Standing/Working/Breathing
Industrial Processes;Oil and Gas Exploration and Production;Crude
Petroleum;Oil Well Pneumatic Devices
Industrial Processes;Oil and Gas Exploration and Production;Crude
Petroleum;Oil Well Fugitives
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Production;Storage Tanks: Crude Oil
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Production;Fugitives: All Processes
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Producti on; Fugi ti ve s: C on n ectors
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Production;Fugitives: Flanges
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Production;Fugitives: Open Ended Lines
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Production;Fugitives: Pumps
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Production;Fugitives: Valves
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Production;Fugitives: Other
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Producti on; Storage Tanks: Condensate
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Gas Well Pneumatic Devices
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Producti on; Gas Well Pneumatic Pumps
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Gas Well Completion - Flaring
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Producti on; Fugi ti ves: C onnectors
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Fugitives: Flanges
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Fugitives: Open Ended Lines
11
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2310021504
2310021505
2310021506
2310021509
2310021602
2310021603
2310023010
2310023300
2310023310
2310023509
2310023511
2310023512
2310023513
2310023515
2310023516
2310023600
2310023603
2310030220
2310030300
2310111401
2310111700
2310111701
2310121401
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Fugitives: Pumps
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Fugitives: Valves
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Fugitives: Other
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Fugitives: All Processes
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Gas Well Venting - Recompletions
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production;Gas Well Venting - Blowdowns
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Storage Tanks: Condensate
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Pneumatic Devices
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Pneumatic Pumps
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Fugitives
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Fugitives: Connectors
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Fugitives: Flanges
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Fugitives: Open Ended Lines
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Fugitives: Valves
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;Fugitives: Other
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;CBM Well Completion: All Processes
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed
Methane Natural Gas;CBM Well Venting - Blowdowns
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas
Liquids;Gas Well Tanks - Flashing & Standing/Working/Breathing,
Controlled
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas
Liquids;Gas Well Water Tank Losses
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Exploration;Oil Well Pneumatic Pumps
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Exploration;Oil Well Completion: All Processes
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil
Exploration;Oil Well Completion: Flaring
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Exploration;Gas Well Pneumatic Pumps
12
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2310121700
2310321010
2310321603
2310421010
2310421603
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Exploration;Gas Well Completion: All Processes
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production - Conventional;Storage Tanks: Condensate
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production - Conventional;Gas Well Venting - Blowdowns
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production - Unconventional;Storage Tanks: Condensate
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas
Production - UnconventionaliGas Well Venting - Blowdowns
Table 3-2. List of Counties Included in Each Texas Sub-Regional Tag
Texas
Sub-
Region
Name
Gulf
Central
Western
Subpart
W Basins
Eagle
Ford,
Western
Gulf
Bend
Arch-Fort
Worth
Basin, East
Texas
Basin,
Western
Gulf
Anadarko
Basin,
Bend
Arch-Fort
Counties
Aransas, Atascosa, Austin, Bastrop, Bee, Brazoria, Brazos,
Brooks, Burleson, Caldwell, Calhoun, Cameron, Chambers,
Colorado, DeWitt, Dimmit, Duval, Fayette, Fort Bend, Frio,
Galveston, Goliad, Gonzales, Grimes, Guadalupe, Hardin,
Harris, Hidalgo, Jackson, Jasper, Jefferson, Jim Hogg, Jim
Wells, Karnes, Kenedy, Kleberg, La Salle, Lavaca, Lee,
Liberty, Live Oak, Madison, Matagorda, Maverick,
McMullen, Milam, Montgomery, Newton, Nueces, Orange,
Polk, Refugio, San Jacinto, San Patricio, Starr, Trinity,
Tyler, Victoria, Walker, Waller, Washington, Webb,
Wharton, Willacy, Wilson, Zapata, Zavala
Anderson, Angelina, Archer, Bandera, Baylor, Bell, Bexar,
Blanco, Bosque, Bowie, Brown, Burnet, Callahan, Camp,
Cass, Cherokee, Clay, Coleman, Collin, Comal, Comanche,
Cooke, Coryell, Dallas, Delta, Denton, Eastland, Ellis,
Erath, Falls, Fannin, Franklin, Freestone, Gillespie,
Grayson, Gregg, Hamilton, Harrison, Hays, Henderson,
Hill, Hood, Hopkins, Houston, Hunt, Jack, Johnson,
Kaufman, Kendall, Kerr, Kinney, Lamar, Lampasas, Leon,
Limestone, Llano, Marion, Mason, McCulloch, McLennan,
Medina, Mills, Montague, Morris, Nacogdoches, Navarro,
Palo Pinto, Panola, Parker, Rains, Real, Red River,
Robertson, Rockwall, Rusk, Sabine, San Augustine, San
Saba, Shackelford, Shelby, Smith, Somervell, Stephens,
Tarrant, Throckmorton, Titus, Travis, Upshur, Uvalde, Van
Zandt, Williamson, Wise, Wood, Young
Andrews, Armstrong, Bailey, Borden, Brewster, Briscoe,
Carson, Castro, Childress, Cochran, Coke, Collingsworth,
Concho, Cottle, Crane, Crockett, Crosby, Culberson,
Dallam, Dawson, Deaf Smith, Dickens, Donley, Ector,
13
-------
Worth Edwards, El Paso, Fisher, Floyd, Foard, Gaines, Garza,
Basin, Glasscock, Gray, Hale, Hall, Hansford, Hardeman, Hartley,
Marathon Haskell, Hemphill, Hockley, Howard, Hudspeth,
Thrust Hutchinson, Irion, Jeff Davis, Jones, Kent, Kimble, King,
Belt, Palo Knox, Lamb, Lipscomb, Loving, Lubbock, Lynn, Martin,
Duro Menard, Midland, Mitchell, Moore, Motley, Nolan,
Basin, Ochiltree, Oldham, Parmer, Pecos, Potter, Presidio, Randall,
Permian Reagan, Reeves, Roberts, Runnels, Schleicher, Scurry,
Basin Sherman, Sterling, Stonewall, Sutton, Swisher, Taylor,
Terrell, Terry, Tom Green, Upton, Val Verde, Ward,
Wheeler, Wichita, Wilbarger, Winkler, Yoakum
Table 3-3. Annual Emissions Associated with Each Oil and Natural Gas Tag
i ag
Description
NOx (tons)
VOC (tons)
Alabama
6
8,596
Arizona
0
163
Arkansas
: 2
4,646
California
0
850
Colorado
353
55.627
Florida
1
403
ID+MT+OR
90
22,334
Illinois
12
50,174
Indiana
j 2
10,143
Kansas
29
65,092
Kentucky
4
32,649
Louisiana
90
49,586
MD+VA
1
6,439
Michigan
" \ 2
13,303
Mississippi
9
i 7.263
Missouri
0
841
Nebraska
0
1,843
Nevada
0
108
New Mexico
395
143,531
NJ+NY
j 2
4,624
North Dakota
3.872
72.735
Offshore
0
13,274
Ohio
63
13,827
Oklahoma
1,908
152,213
Pennsylvania
6,795
1 19,472
SC+TN
1
1,389
South Dakota
7
2,187
Texas Central
77
I 127,927
Texas Gulf
2,019
336,071
Texas West
1,509
441,349
-------
Utah
4
168
1,063
31,406
125,475
47,726
West Virginia
Wyoming
The source apportionment modeling provided contributions to ozone from anthropogenic
NOx and VOC oil and natural gas emissions in each tag, individually, to ozone concentrations in
each model grid cell within the 12 km domain. Examples of the magnitude and spatial extent of
tagged ozone contributions are provided in Figure 3-1 through Figure 3-3 for oil and natural gas
VOC emissions in ten states. These figures show how the magnitude and the spatial patterns of
contributions of oil and natural gas VOC emissions to ozone depend on multiple factors including
the magnitude and location of emissions as well as the atmospheric conditions that influence the
formation and transport of ozone. For instance, the magnitude of tagged oil and natural gas VOC
emissions are similar in Wyoming and Colorado, but ozone impacts from these emissions are
much larger in Colorado than Wyoming (Figure 3-1) due to much larger urban NOx emissions
near oil and natural gas sources in Colorado that can react with the oil and natural gas VOC
emissions to form ozone in the atmosphere. In New Mexico, the spatial extent of the ozone
impacts reflect oil and natural gas VOC emissions locations in two distinct regions of the state
(Figure 3-1). In Figure 3-2 it is apparent that the spatial extent of ozone contributions from the
three Texas sub-regional tags are distinct and impacted both by the location of the emissions
within those sub-regions and by prevailing winds which transport ozone into nearby states. The
magnitude of the ozone impact is larger from emissions in the Texas West tag and the Texas Gulf
tag than from emissions in the Texas Central tag which is consistent with substantially larger oil
and natural gas VOC emissions levels in those to subregions than the Texas Central subregion
(Table 3-3).
15
-------
Mpn-0.00E+0 SI (1.1). M#» = 0.399 «((124,111) Mln = 0.00E+0 #t(Llk M»» = 0.502 K (145.139)
Figure 3-1. Map of April-September Mean MDA8 ozone contributions (ppb) from tagged oil
and natural gas VOC emissions located in (a) Wyoming, (b) Utah, (c) New Mexico and (d)
Colorado.
16
-------
(a)J
(b)J
jw
p-*
) / tW r—-J—J/Tr
> /
V \
'^JJWUY
„
v-\ i \
•
Vj y
Mm* O.OOE+Oat(l.H Maj = 0.109 at (198.72)
O.OOE'O at (1.1), Ma* = 04.07 at (191,106)
(c)J
m
/ / \ /Vr
Af
m
, l —-—J it V r i. r"Ai
(YrQr~AViW3f
rrvT
v ;
/ w
V \ 1 i—y~v ! JvTf
\ / 1 t| \ br--^~r
\ / \\
so
TT v
Vi y
:
ppbv
Mm - O.OOE+O ac (1,1), Ma. - 0189 at (157,72)
Min - 0 OOE-O at (1.1). Mai - 0.198 at (170.30)
Figure 3-2. Map of April-September Mean MDA8 ozone contributions (ppb) from tagged oil
and natural gas VOC emissions located in (a) Texas Central, (b) Oklahoma, (c) Texas West and
(d) Texas Gulf.
17
-------
Mn=O.OOE'O at (1.1). Max = 0.110 at (267,130)
i - 0.006+0 at (1.1). Mai • <1132 at 1323.1531
(d)J
I V
rW
i Jr,
L
Mir » OOOE+O at (1,1). Mai = Oil? at (316,139]
ppbv
Figure 3-3. Map of April-September Mean MDA8 ozone contributions (ppb) from tagged oil
and natural gas VOC emissions located in (a) North Dakota, (b) Pennsylvania, (c) Illinois and
(d) West Virginia.
3.2 Source Apportionment Scaling Method for Calculating the Ozone Impacts of the Final
Rule
In this section we describe the method for creating spatial fields of the April-September mean
of MDA8 ozone (AS-M03) for baseline and regulatory alternatives based on the 2016 and 2026
modeling. The foundational data include (1) ozone concentrations in each model grid cell from the
2016 and 2026 modeling, (2) ozone contributions in 2026 of oil and natural gas emissions from each
state in each model grid cell,14 (3) 2026 emissions from covered oil and natural gas sources that were
used as inputs to the contribution modeling (Table 3-3) and (4) the oil and natural gas emissions for
baseline and regulatory alternatives in each state and year of analysis. The development of state-level
emissions for the baseline and regulatory alternatives are described in Section 2.2 of US EPA, 2023e.
The method to create spatial fields applies scaling factors to gridded source apportionment
14 Contributions from oil and natural gas sources were modeled using projected emissions for 2026. The resulting
contributions were used to construct spatial fields in 2024, 2027, 2028 and 2038.
18
-------
contributions based on emissions changes between 2026 modeled levels and the baseline and
regulatory alternatives. This method is described in detail below.
Spatial fields of ozone for 2026 modeled conditions were created based on "fusing" modeled
data with measured concentrations at air quality monitoring locations. To create the spatial fields for
each future emissions scenario these fused 2026 model fields are used in combination with 2026
state-by-state oil and natural gas source apportionment modeling and the oil and natural gas emissions
for each scenario and year of analysis. Contributions from each state-level oil and natural gas
contribution "tag" were scaled based on the ratio of emissions in the year/scenario being evaluated to
the emissions in the modeled 2026 scenario. Contributions from tags representing sources other than
oil and natural gas are held constant at 2026 levels for each of the scenarios and year. For each
scenario and year analyzed, the scaled contributions from all sources were summed together to create
a gridded surface of total modeled ozone. The process is described in a step-by-step manner below
starting with the methodology for creating AS-M03 spatial fields.
1. Create fused spatial fields of 2026 AS-M03 incorporating information from the air quality
modeling and from ambient measured monitoring data. The enhanced Voronoi Neighbor Average
(eVNA) technique (Ding et al., 2016; Gold et al., 1997; U.S. EPA, 2007) was applied to ozone
model predictions in conjunction with measured data to create modeled/measured fused surfaces
that leverage measured concentrations at air quality monitor locations and model predictions at
locations with no monitoring data.
1.1. The AS-M03 eVNA spatial fields are created for the 2016 base year with EPA's software
package, Software for the Modeled Attainment Test - Community Edition (SMAT-CE)15
(U.S. EPA, 2022b) using three years of monitoring data (2015-2017) and the 2016 modeled
data.
1.2. The model-predicted spatial fields (i.e., not the eVNA fields) of AS-M03 in 2016 were paired
with the corresponding model-predicted spatial fields in 2026 to calculate the ratio of AS-
M03 between 2016 and 2026 in each model grid cell.
1.3. To create a gridded 2026 eVNA surfaces, the spatial fields of 2016/2026 ratios created in step
1.2 were multiplied by the corresponding eVNA spatial fields for 2016 created in step 1.1 to
produce an eVNA AS-M03 spatial field for 2026 using (Eq-1).
15 SMAT-CE available for download at https://www.epa.gov/scram/photochemical-modeling-tools.
19
-------
eVNA,
g,2026
— (eVNAg 2oi6) x
Modelg,2026
ModeL,mfi
g,2016
Eq-1
• eVNAg 2026 is the eVNA concentration of AS-M03 in grid-cell, g, in the 2026 future year
• eVNAg 2016 is the eVNA concentration of AS-M03 in grid-cell, g, in 2016
• Modelg 2026 is the CAMx modeled concentration of AS-M03 in grid-cell, g, in the 2026
future year
• Modelg 2016 is the CAMx modeled concentration of AS-M03 in grid-cell, g, in 2016
2. Create gridded spatial fields of total oil and natural gas AS-M03 contributions for each
combination of scenario and year evaluated.
2.1. Use the oil and natural gas VOC emissions for the 2024 baseline and the corresponding 2026
modeled oil and natural gas VOC emissions (Table 3-3) to calculate the ratio of 2024
baseline emissions to 2026 modeled emissions for each oil and natural gas state contribution
tag (i.e., an ozone scaling factor calculated for each state). These scaling factors are provided
in Table 3-4.
2.2. Calculate adjusted gridded AS-M03 oil and natural gas contributions that reflect differences
in state-oil and natural gas VOC emissions between 2026 and the 2024 baseline by
multiplying the VOC scaling factors by the corresponding gridded AS-M03 ozone
contributions16 from each state-by-state oil and natural gas tag.
2.3. Add together the adjusted AS-M03 contributions for each oil and natural gas-state tag to
produce spatial fields of adjusted oil and natural gas totals for the 2024 baseline.17
2.4. Repeat steps 2.1 through 2.3 for all 2024 regulatory alternatives (i.e. the final rule scenario,
the less stringent alternative, and the more stringent alternative) and for the baseline and
regulatory alternatives for each additional year of analysis. All scaling factors for the baseline
scenario and the regulatory alternatives are provided in Tables 3-4 through 3-7.
16 The source apportionment modeling provided separate ozone contributions for ozone formed in VOC-limited chemical
regimes (03V) and ozone formed in NOx-limited chemical regimes (03N). The VOC emissions scaling factors are
multiplied by the corresponding 03 V gridded contributions to MDA8 concentrations. Since there are no predicted
changes in NOx emissions in the control scenarios, the 03N contributions remain unchanged.
17 The contributions from the unaltered 03N tags are added to the summed adjusted 03 V oil and natural gas tags.
20
-------
3. Create a gridded spatial field of AS-M03 associated with oil and natural gas VOC emissions for
the 2024 baseline by combining the oil and natural gas AS-M03 contributions from step 2.3 with
the corresponding contributions to AS-M03 from all other sources. Repeat for each of the oil and
natural gas contributions created in step 2.4 to create separate gridded spatial fields for the
baseline and regulatory alternatives for all other years of analysis.
Steps 2 and 3 in combination can be represented by equation 2:
AS-M03g^y = eVNAg2026
(Cg,BC Cgj0ther \ ' Co&GNOx,g,t \ 1 Co&GVOC,g,t ^i/oc,t,i,y
Cg,Tot Cg.Tot Aj CgXot L-^ CgTot
Eq-2
• AS-M03g i y is the estimated fused model-obs AS-M03 for grid-cell, "g," scenario, "i,"18 and
year, "y;"19
• eVNAg 2026 is the 2026 eVNA AS-M03 concentration for grid-cell "g" calculated using Eq-1.
• Cg Xot is the total modeled AS-M03 for grid-cell "g" from all sources in the 2026 source
apportionment modeling
• Cg,bc is the 2026 AS-M03 modeled contribution from the modeled boundary inflow;
• Cg,other is the 2026 AS-M03 modeled contribution from all natural and anthropogenic
emissions within the modeling domain other than those from oil and natural gas sources;
• C0&GVOC,g,t's 2026 AS-M03 modeled contribution from oil and natural gas emissions of
VOCs from state, "t";
• C0&GNOx,g,t *S 2026 AS-M03 modeled contribution from oil and natural gas emissions of
NOx from state, "t"; and
• SVoc,Uy is the oil and natural gas VOC scaling factor for state, "t," scenario, "i," and year,
18 Scenario "i" can represent the baseline scenario, the final rule scenario, the less stringent alternative or the more
stringent alternative
19 Year "y" can represent 2024, 2027, 2028 or 2038
21
-------
Table 3-4. 2024 ozone scaling factors for oil and natural gas tags in the baseline and regulatory
alternatives based on ratios of VOC emissions in those scenarios to the 2026 modeled scenario
for each state-by-state oil and natural gas tag.
Less
More
State
Baseline
Stringent
Stringent
Final Rule
Alternative
Alternative
Al.
1 52
1 51
1 51
1.51
AU
4 3S
4 35
4 35
4 35
AZ
1 43
1 43
I 43
1 43
CA
7 1 1
7 1 1
7 1 1
7 1 1
CO
2 OS
2.OS
2 OS
2.0S
II.
1 i)2
1 i)2
1 i)2
1 i)2
II.
i) i)|
0 o |
0 01
0 01
IN
OO.i
0 0.1
0 0.1
0 0.1
KS
1 73
I 71
1 71
1 71
KY
0 (i()
0 51)
0 51)
0 51)
I.A
1 o]
1 5S
1 57
1 57
Ml
1 20
1 25
1 25
1 25
MO
i) 24
i) 24
0.24
i) 24
MS
2.77
2 OS
2 OS
2 OS
M l +ID+W A+OU
1 57
1 50
1 5o
1 5o
\l>
2 ol
2 5o
2 5o
2 5o
NK+IA
2 on
2.54
2 54
2 54
NY+N.I+CT+UI+MA+YT+NII+MK
2 N
2 17
2 17
2 17
NM
I 4l)
1 44
1 44
1 44
NY
0 3S
0 3S
0 3S
0 3S
OH-SI IK
| 00
| 00
| 00
| 00
Oil
3 5X
3 4o
3 4o
3 4o
OK
1 Sl)
1 S3
I S3
1 S3
PA
o 7X
0.75
0.75
0.75
SD-.M N-\YI
o S4
0 S4
0 S4
0 S4
TN+CA+NC+SC
o 2X
0 2S
0 2S
0 2S
IXC
o.K4
o.S2
0 S2
0 S2
TXN
o S4
0 S2
0 S2
0 S2
TX\Y
1.77
1 0l)
1 ol>
1 ol>
IT
1 00
1 i)4
1 i)4
1 i)4
\ A+MD+DI.
1 34
1 32
I 32
I 32
WV
0.52
0.52
0.52
0.52
\YY
2.77
2.73
2.73
2.73
'Texas Gulf and Texas Central VOC emissions were based on estimates of VOC emissions in the Gulf Coast NEMS region within Texas. The proportion
of those emissions allocated to the Texas Gulf tag versus the Texas Central tag is based on the relative VOC emissions magnitudes within each of the 2026
tags such that 72.4% of the VOC emissions from the Gulf Coast NEMS were allocated to the Texas Gulf tag and 27.6% of the VOC emissions from the
Gulf Coast NEMS were allocated to the Texas Central Tag.
22
-------
2VOC emissions in the Texas West region is the sum of estimated VOC emissions in the Texas Midcontinent NEMS region and the Texas Southwest
NEMS region.
Table 3-5. 2027 year ozone scaling factors for oil and natural gas tags in the baseline and
regulatory alternatives based on ratios of VOC emissions in those scenarios to the 2026 modeled
scenario for each state-by-state oil and natural gas tag.
Less
More
State
Baseline
Stringent
Stringent
Final Rule
Alternative
Alternative
AL
1 35
1 32
1.32
1 32
AU
3 oo
3 S4
3 S4
3 S4
AZ
1 4i)
1 37
1 37
I 37
CA
o 45
o 45
o 45
o 45
CO
2.70
1 J
2.70
2.70
II.
1 <>l
| 00
| 00
| 00
II.
i) i)|
0 0 |
0 0 |
0 0 |
IN
003
003
003
003
KS
1 44
1 30
1 35
1 35
KY
i) 54
ii 52
i)5l
i)5l
I.A
1 4X
1 34
I 34
1.34
Ml
1 00
1 i)4
1 i)4
1 i)4
MO
i) 21
o 2<)
o 2<)
o 2o
MS
2 35
2.07
2.07
2.07
M l +ID+W A+OU
1 32
1 20
1 20
1 20
\l>
2 oo
1 J
--J
:
1 J
--J
1 J
--J
NI.+IA
2 15
1 i)3
1 02
1 02
NY+N.I+CT+UI+MA+YT+NII+MK
2 oo
1 lJ3
1 02
1 02
YM
1 53
1 30
1 30
1 30
NY
4 47
4 47
4 47
4 47
OKI-SI IK
| 00
| 00
| 00
| 00
Oil
3 5 1
3 12
3 12
3 12
OK
1 SO
1 05
1 05
1 05
PA
0.75
o 07
() 00
() 00
SD-MN-W 1
o.os
o 05
o 05
o o5
TN+CA+NC+SC
o 2X
o 2S
o 2S
o 2S
IXC
o SS
o.So
0 so
o so
TXN
o SS
o So
o so
o So
TX\Y
1 SO
1 01
1 oo
] 00
IT
o 07
o SO
0 SO
0 SO
\ A+MD+DI.
1 3D
1 24
I 22
1 22
\YY
o 50
i)47
i)47
0 47
\YY
2 (0
2.40
2 45
2.45
23
-------
'Texas Gulf and Texas Central VOC emissions were based on estimates of VOC emissions in the Gulf Coast NEMS region within Texas. The proportion
of those emissions allocated to the Texas Gulf tag versus the Texas Central tag is based on the relative VOC emissions magnitudes within each of the 2026
tags such that 72.4% of the VOC emissions from the Gulf Coast NEMS were allocated to the Texas Gulf tag and 27.6% of the VOC emissions from the
Gulf Coast NEMS were allocated to the Texas Central Tag.
2VOC emissions in the Texas West region is the sum of estimated VOC emissions in the Texas Midcontinent NEMS region and the Texas Southwest
NEMS region.
Table 3-6. 2028 year ozone scaling factors for oil and natural
regulatory alternatives based on ratios of VOC emissions in those
scenario for each state-by-state oil and natural gas tag.
gas tags in the baseline and
scenarios to the 2026 modeled
State
Baseline
Less
Stringent
Alternative
More
Stringent
Alternative
Final Rule
Al.
1 20
i)47
().3I
0.32
AU
3 S5
r i
o K2
0 SO
AZ
i 3 s
i) 5
o.32
o 32
CA
2 111)
1 05
1 so
1 SO
CO
1 72
1 72
1 03
1 04
II.
1 1 1
i) 02
o 50
0 (il)
II.
i) i)|
0 00
0 00
0 00
IN
1)0.1
0 o |
0 o I
0 0 I
KS
1 37
0 OS
o 40
0.50
KY
ii 52
o 2<)
o 1 1
o 12
I.A
1 45
o.5(i
o 45
o 40
Ml
| 00
o 40
o 31
o 32
MO
o 2d
0 00
o OS
o.os
MS
1 J
1 J
' J\
1 2d
1 13
1 13
M l +ID+W A+OU
1 27
o.N4
0.75
0.75
\l>
O.i
2 43
2 30
2 30
NI.+IA
2 04
1 15
| 00
1 o|
NY+N.I+CT+UI+MA+YT+NII+MK
1 lJ5
0.75
o 3S
o 40
YM
1 30
o X4
0.77
o 7S
NY
4 i)7
3 3o
3 3o
OH-SI IK
| 00
| 00
| 00
| 00
Oil
3 4X
1 45
1 00
I 00
OK
1 ^2
o 05
o.S4
o S5
PA
o 05
i) 23
o 13
o 13
SD-.M V\YI
11 03
i)4l
* '..>o
i \ i>
1 1 iS
TN+CA+NC+SC
o 2X
0 0(1
0 ()(t
0 ()(t
IXC
0 00
i)47
0 44
0 44
TXN
0 00
i)47
0 44
0 44
TX\Y
1 SS
1 12
1 07
I 07
UT
| 0.95
| 0.37
| 0.30
| 0.31
24
-------
YA+MD+DK \ 2^ 0.53 n 24 n 20
\YY ii 50 i) |i) | | 0 12
\YY 2 5l> I 34 121 123
'Texas Gulf and Texas Central VOC emissions were based on estimates of VOC emissions in the Gulf Coast NEMS region within Texas. The proportion
of those emissions allocated to the Texas Gulf tag versus the Texas Central tag is based on the relative VOC emissions magnitudes within each of the 2026
tags such that 72.4% of the VOC emissions from the Gulf Coast NEMS were allocated to the Texas Gulf tag and 27.6% of the VOC emissions from the
Gulf Coast NEMS were allocated to the Texas Central Tag.
2VOC emissions in the Texas West region is the sum of estimated VOC emissions in the Texas Midcontinent NEMS region and the Texas Southwest
NEMS region.
Table 3-7. 2038 year ozone scaling factors for oil and natural gas tags in the baseline and
regulatory alternatives based on ratios of VOC emissions in those scenarios to the 2026 modeled
scenario for each state-by-state oil and natural gas tag.
Less More
State Baseline Stringent Stringent Final Rule
Alternative Alternative
Al.
o i)2
i) 30
0.11)
0.20
AU
1 J
I) ss
0 5(i
0 51)
AZ
1 34
1) 34
i) 32
i) 32
CA
1 5X
1 54
1 5')
1 5'i
CO
1 X4
1 X4
1.77
1 7S
II.
0 51)
r 1
i) 21
i) 21
II.
i) (>|
0 do
i) do
0 00
IN
0 04
0 01
i) i)|
0 01
KS
0
0 3S
0 25
11.20
KY
i)4l
i) 15
0 OS
0 Oi)
I.A
1 35
i)4l
D.32
i) 32
Ml
0 (15
0 2X
0 10
i) 17
MO
0 IS
0 07
0 0(i
0 0(i
MS
1
0 X4
0.77
0.77
M l +ID+W A+OU
0 7X
0 31)
i) 33
i) 33
\l>
3 N
2 2<)
2 17
2 17
M.+IA
1 5i)
0 5X
D.47
i)47
NY+N.I+CT+UI+MA+YT+NII+MK
1 53
0.57
i) 32
i) 34
YM
1 44
0.SO
0 SO
0 SI
NY
1 33
I) SI
0 7i)
0 7i)
OH-SI IK
| do
1 1)1)
| 00
| 00
Oil
3 35
1 31
1 00
1 OS
OK
2 lo
0 iJ2
0 S2
11 rO
PA
i) (-.3
0 I1)
i) 12
i) 12
SD-.M N-\YI
i)4l
0 2<)
i) 17
i) 17
TN+GA+NC+SC
0.29
| 0.07
| 0.07
0.07
25
-------
t\(;
TXN
TXW
2 os
() SI
0 4^
0
1 OS
0 I1)
0.2.1
II
o 2o
0 50
\ A+MD+DI.
\\ V
W'Y
o
o lo
0 i;2
'Texas Gulf and Texas Central VOC emissions were based on estimates of VOC emissions in the Gulf Coast NEMS region within Texas. The proportion
of those emissions allocated to the Texas Gulf tag versus the Texas Central tag is based on the relative VOC emissions magnitudes within each of the 2026
tags such that 72.4% of the VOC emissions from the Gulf Coast NEMS were allocated to the Texas Gulf tag and 27.6% of the VOC emissions from the
Gulf Coast NEMS were allocated to the Texas Central Tag.
2VOC emissions in the Texas West region is the sum of estimated VOC emissions in the Texas Midcontinent NEMS region and the Texas Southwest
NEMS region.
4. Evaluation of CAMx Model Ozone Predictions Against Ambient Ozone
Measurements in 2016
An operational model evaluation was conducted for the 2016 base year CAMx v7.10 simulation
performed for the 12 km U.S. modeling domain. The purpose of this evaluation is to examine the
ability of the 2016 air quality modeling platform to represent the magnitude and spatial and temporal
variability of measured (i.e., observed) maximum daily average (i.e., MDA8) ozone concentrations
within the modeling domain. Reasonable model performance in 2016 builds confidence the
credibility of using this modeling platform to project ozone to the 2026 timeframe. The evaluation
presented here is based on model simulations using the 2016v2 emissions platform (i.e., scenario
name 2016fj). The model evaluation for ozone focuses on comparisons of 8-hour daily maximum
(i.e., MDA8) ozone concentrations to the corresponding observed data at monitoring sites in the EPA
Air Quality System (AQS). The locations of the ozone monitoring sites in this network are shown in
Figure 4-1.
26
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CIRCLE=AQS_Daily;
Figure 4-1. Location of ozone monitoring sites.
This evaluation includes statistical measures and graphical displays of model performance
based upon model-predicted versus observed concentrations. The evaluation focuses on model
predicted and observed MDA8 ozone concentrations that were paired in space and time. Model
performance statistics were calculated for several spatial scales for the April-September 2016 time
period. Statistics were calculated in aggregate for monitoring sites within each of nine climate regions
of the 12 km U.S. modeling domain as well as nationally. The regions include the Northeast, Ohio
Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies & Plains, Northwest, and
West20, which are defined based upon the states21 contained within the National Oceanic and
Atmospheric Administration (NOAA) climate regions (Figure 4-2)22 as defined in Karl and Koss
(1984).
211 Note most monitoring sites in the West region are located in California (see Figures A-l and A-2), therefore the
statistics for the West region will be mostly representative of model performance in California ozone.
21 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY, PA, RI,
and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN, and WI;
Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; Southwest includes
AZ, CO, NM, and UT; Northern Rockies & Plains includes MT, NE, ND, SD, WY; Northwest includes ID, OR, and WA;
and West includes CA and NV.
22 NOAA, National Centers for Enviromnental Information scientists have identified nine climatically consistent regions
within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php.
27
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U.S. Climate Regions
Figure 4-2. NOAA climate regions (source: htti)://www.nc(lc.noaa.gov/monitoring-references/mai)s/us-
climate-regions.i)hi)#references)
Statistics were created using data on all April-September days with valid observed data during
this period. In addition, this section provides maps that show the mean bias and normalized mean bias
for April through September at individual monitoring sites and time series plots (April through
September) of observed and predicted MDA8 ozone concentrations for each region. The Atmospheric
Model Evaluation Tool (AMET) was used to calculate the model performance statistics used in this
document (Gilliam et al.. 2005). For this evaluation we have selected the mean bias, mean error,
normalized mean bias, normalized mean error and correlation to characterize model performance,
statistics which are consistent with the recommendations in Simon et al. (2012) and EPA's
photochemical modeling guidance (U.S. EPA, 2018). For formulas listed below, P represents CAMx
predicted MDA8 ozone, 0 represents observed MDA8 ozone and n represents the number of model-
observation data pairs
• Mean bias (MB) is the average of the difference (predicted - observed) divided by the total
number of replicates. Mean bias is given in units of ppb and is defined as:
MB = -I?(P-0)
n
• Mean error (ME) calculates the absolute value of the difference (predicted - observed) divided by
the total number of replicates. Mean error is given in units of ppb and is defined as:
n
ME=-ViP-°\
1
28
-------
• Normalized mean bias (NMB) is the average the difference (predicted - observed) over the sum of
observed values. NMB is a useful model performance indicator because it avoids over inflating
the observed range of values, especially at low concentrations. Normalized mean bias is given in
percentage units and is defined as:
2?(P - 0)
NMB=^mr*100
• Normalized mean error (NME) is the absolute value of the difference (predicted - observed) over
the sum of observed values. Normalized mean error is given in percentage units and is defined as:
NME=^kor*100
• Correlation (r) is a measure of the ability of the model to capture spatial and temporal variation in
the observations. Correlation values range from -1 to +1. A correlation of+1 represents a perfect
linear correlation between the observations and the model values and a correlation of -1 represents
perfectly anticorrelated datasets. A correlation value of 0 would represent datasets whose
relationship is completely random. Correlation is calculated as:
1.(0, - 0) x (P, - P)
V£(oi-o)2Z(pi-/i)2
As described in more detail below, the model performance statistics indicate that the MDA8
ozone concentrations predicted by the 2016v2 CAMx modeling platform closely reflect the
corresponding MDA8 observed ozone concentrations in each region of the 12 km US modeling
domain. The acceptability of model performance was judged by considering the 2016v2 CAMx
performance results in light of the range of performance found in recent regional ozone model
applications (Emery et al., 2017; NRC, 2002; Phillips et al., 2007; Simon et al., 2012; U.S. EPA,
2005; U.S. EPA, 2009; U.S. EPA, 2010). These other modeling studies represent a wide range of
modeling analyses that cover various models, model configurations, domains, years and/or episodes,
chemical mechanisms, and aerosol modules. In particular, Emery et.al. extend the results of Simon
et.al., to include performance results from a few more recent photochemical model applications. The
results in the former paper indicate that about a third of the top performing past applications have
normalized mean bias and a normalized mean error statistics for MDA8 ozone of less than ±5 percent
and less 15 percent, respectively. In addition, two-thirds of past applications have normalized mean
bias less than ±15 percent and normalized mean error less than 25 percent. These "criteria" are not
intended to represent "rigid pass/fail tests" but rather as "simple references to the range of recent
29
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historical performance" that can be used to understand where the performance results of a particular
application "fall in the spectrum of past published results."
Overall, the ozone model performance results for the 2016v2 CAMx simulation are generally
within the range found in other recent peer-reviewed and regulatory applications. The model
performance results, as described in this document, demonstrate that the predictions from the 2016v2
modeling platform correspond closely to observed concentrations in terms of the magnitude, temporal
fluctuations, and geographic differences for MDA8 ozone concentrations.
The MDA8 ozone model performance bias and error statistics for the period April-September
for each climate region are provided in Table 4-1. The model performance statistics provided in Table
4-1 show that regional mean bias is less than ±5 ppb23 and the mean error is between 6 and 7.5 ppb
during the April through September in each region. Normalized mean bias is less than ± 5% in the
Northeast, Ohio Valley, Southeast, South, and Northwest as well as nationally. In the Upper Midwest,
Southwest, Northern Rockies & Plains, and West, normalized mean bias is between 7% and 10%.
Normalized mean error is less than 15% in the Ohio Valley, Southeast, Northern Rockies & Plains,
Southwest and West and less than 20% in the other regions. Correlation was between 0.7 and 0.8 for
all regions except for the Southwest and Northwest where correlation was 0.62 and 0.67 respectively.
Table 4-1. Model Performance Statistics for April-September MDA8 O3 at AQS monitors
Region
1MB
NMB
ME
: NME
Cor
Northeast
-1.3
: -2.8
6.8
15.3
0.77
Ohio Valley
! -0.5
-1.1
6.4
14.1
j 0.73
Upper Midwest
-3.9
-9.3
6.8
16.3
= 0.75
Southeast
0.9
2.1
6.0
14.2
0.81
South
-0.6
-1.5
6.6
16.1
0.73
Northern Rockies & Plains
-4.1
-9.4
6.4
14.7
0.71
Southwest
-4.4
-8.4
1 7.0
13.6
0.62
Northwest
! -0.3
; -0.7
6.6
17.7
| 0.67
West
-3.5
-6.9
7.4
14.5
: 0.80
National
-1.7
-3.8
6.7
14.8
: 0.77
Spatial plots of the April-September MDA8 ozone mean bias and normalized mean bias and
error at individual monitors nationwide are shown in Figures 4-3 and 4-4. Mean bias, as seen in
Figure 4-3, is within ± 5 ppb at many sites from portions of Texas northeastward to the Northeast
Corridor. At monitors in this area the normalized mean bias is generally within ±10 percent. At most
23 Note that "within +5 ppb" includes values that are greater than or equal to -5 ppb and less than or equal to 5 ppb.
30
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monitoring sites across the remainder of the East the model under predicts by 5 to 10 ppb, the
normalized mean bias is between -10 and -20 percent. In Arizona, Colorado, New Mexico, and Utah,
there is notable spatial heterogeneity in mean bias. For example, in Denver there are some sites with
mean bias within ± 2 ppb while at relatively near-by monitors the model is low-biased by 5 to 10 ppb.
In California, the model overpredicts April through September MDA8 ozone at coastal sites by 2-8
ppb and underpredicts April through September MDA8 in the central valley by 10-20 ppb. For most
monitoring sites in the West, the normalized mean bias is -20 percent or less, except for central
California where the normalized mean bias is between -20 and -30 percent.
03_8hrmax MB (ppb) for run CAMx_2016fj_v710_CB6r5_12US2 for 20160401 to 20160930
• AQS Daily
Figure 4-3. Map of April-September Mean Bias (ppb) for MDA8 ozone at AQS monitoring
locations
31
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03 8hrmax NMB {%) for run CAMx 2016fi v710 CB6r5 12US2 for 20160401 to 20160930
Jflits = %
coverage limit = 75%
>45
40
35
30
25
20
15
10
5
0
I -5
-10
-15
-20
-25
I -30
-35
| -40
= -45
AQS Daily
Figure 4-4. Map of April-September Normalized Mean Bias (%) for MDA8 ozone at AQS
monitoring locations
Time series plots of observed and predicted MDA8 ozone during April through September
2016 for each region and are provided in Figures 4-5 through 4-7 respectively. The plots in this figure
show that the modeled concentrations closely track the corresponding observed values in terms of
day-to-day fluctuations and the general magnitude of concentrations. Comparing the plots for the nine
regions reveals that there are large differences in the day-to-day variability among the regions. For
example, the degree of temporal variability in MDA8 ozone concentrations in the Northeast, Upper
Midwest, and Ohio Valley is much greater than in the Southeast, South, Southwest, and Northern
Rockies & Plains. The modeling platform captures regional differences in the degree of temporal
variability in MDA8 ozone concentrations. The model performs equally as well in eastern and
western regions in terms of replicating the relative magnitude of concentrations and day-to-day
variability that are characteristic of observed MDA8 ozone concentrations in each region.
There are some differences in the model performance noticeable throughout different portions
of the April through September time period. In the Northeast and the Ohio Valley, the model
underprediets in April, May, and June followed by over prediction July. In the Upper Midwest, the
observed concentrations are underpredicted in April, May and June, MDA8 values of the model
generally align with the corresponding observed data in July, August, and September. In the
32
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Southeast, the predictions generally correspond well with that of the observed concentrations in April,
May and June with overprediction during the remainder of the ozone season. In the South, the
predicted concentrations tend to be close to that of the observed data in most months with a tendency
for underprediction of peak values in April and May. In the Southwest, the modeled values are
underpredicted in the early part of the season but align better with observations in August and
September. In the Northern Rockies and Plains, the model underprediets in April through July, but
closely captures the distribution of observed concentrations in August and September. In the
Northwest modeled MDA8 ozone underprediets the observed values in April through June, but then
more closely tracks the observed values in July, August, and September. In the West region, the
observed ozone is underpredicted in April through September with a tendency to under predict peak
values.
CAMx_2016fj_v710_CB6r5_12US2 03_8hrmax for AQS Daily for 20160401 to 20160930
CAMx_2016fj_v710_CB6r5_12US2 03_8hrmax for AQS Daily for 20160401 to 20160930
CAMx_2016fj_v710_CB6r5_12US2 03_8hrmax for AQS Dally for 20160401 to 20160930
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
. _ .. #of Sites: 28
AQS Daily
CAMx_2016fj_v710_CB6r5 _12US2
—— # of Sites: 205
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
—1111111111111111111111 ii 11111111111111111111 ii t 11111111 n 1111111111111111111 r 11111111111 ii 11111111111111111111111111111111111111 ii 111111111111111 m 1111111111111 n n 1111 ii 111111 ii 11111
Apr 01 Apr 11 Apr 21 May 01 May 11 May 21 May 31 Jun10 Jun 20 Jun 30 Jul 09 Jul 18 Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
—111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111 ii i
Apr01 April Apr21 May 01 May11 May 21 May31 Jun 10 Jun20 Jun30 Jul09 Jul 18 Jul27 Aug 06 Aug 16 Aug26 Sep05 Sep15 Sep25
Date
—1111111II111111111111111111111111111111II1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111
Apr 01 April Apr 21 May 01 May 11 May 21 May 31 Jun 10 Jun 20 Jun 30 Jul 09 Jul 18 Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
33
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Figure 4-5. Timeseries of observed and modeled daily MDA8 ozone (ppb) from April to
September averaged across monitors in the Northwest (top), West (middle), and Southwest
(bottom).
Figure 4-6. Timeseries of observed and modeled daily MDA8 ozone (ppb) from April to
September averaged across monitors in the Northern Rockies & Plains (top), Upper Midwest
(middle), and South (bottom).
—— # of Sites: 110
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
CAMx_2016fj_v710_CB6r5_12US2 03_8hrmax for AQS Daily for 20160401 to 20160930
# of Sites:
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
11111111111111111111111 ll 1111111111111111111111111111111111111111111111111111111111II11111111(1111111111111111i 111111111111111111111111111111111111111111111111111111111111111111111111
Apr 01 April Apr 21 May 01 May 11 May 21 May 31 Jun 10 Jun20 Jun30 Jul 09 Jul IB Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
CAMx_2016f|_v710_CB6r5_12US2 03_8hrmax for AQS Daily for 20160401 to 20160930
AQS Daily
CAMx_2016fi_v710_CB6r5_12US2
CAMx_2016f|_v710_CB6r5_12US2 Q3_8hrmax for AQS Daily for 20160401 10 20160930
iiiiiiniiiiii i iiiiiiiii Milium i mm limn i ii iHniiiiiiiiiiiiiiiiiiiiiii mi minimi Minimi i iiiiiiiiiiiiiiiiiiiiiiniiiiiiiiii iiiii Minimi iii iiiiiiiiiiiiiiii illinium
Apr 01 April Apr 21 May 01 May 11 May 21 May 31 Jun 10 Jun 20 Jun 30 Jul 09 Jul 18 Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
111111111111111111111111111111111M 1111III i 111M l i 111IIIM111111II111II11111111M111111111111111II111111111M11111II111f1111111111111111111111111111111II1111111IIII11M111111111II11II
Apr 01 April Apr 21 May 01 May 11 May 21 May 31 Jun 10 Jun 20 Jun 30 Jul 09 Jul 18 Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
34
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CAMx_2016fj_v710_CB6r5_12US2 03_8hrmax for AQS Daily lor 20160401 to 20160930
Figure 4-7. Timeseries of observed and modeled daily MDA8 ozone (ppb) from April to
September averaged across monitors in the Northeast (top), Ohio River Valley (middle), and
Southeast (bottom).
CAMx_2016fj_v710_CB6r5_12US2 03_8hrmax for AQS Dally for 20160401 to 20160930
—mini i iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii illinium mi linn iiiiiiiiii i nil iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
Apr 01 April Apr 21 May 01 May 11 May 21 May 31 Jun 10 Jun20 Jun 30 Jul 09 Jul 18 Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
—iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiimiiiiiiiiiiiiiiiiiiniiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
Apr 01 Apr 11 Apr 21 May 01 May 11 May 21 May 31 Jun 10 Jun 20 Jun 30 Jul 09 Jul 18 Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
~7777~ #of Sites: 192
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
"77TT # of Sites: 241
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
~~7777™" # of Sites: 186
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
CAMx_2016f|_v710_CB6r5_12US2 03_8hrmax for AQS Dally for 20160401 to 20160930
III11111111111111 ¦ ii 11m111111111ii11111111111111iiimiii 11 m 1111111 ii i ii 1111111111111111111111111 ii 1111111111111111111111 (III11II11111111111111111111111111111111111111111111111111111
Apr 01 Apr 11 Apr 21 May 01 May 11 May 21 May 31 Jun 10 Jun 20 Jun 30 Jul 09 Jul 18 Jul 27 Aug 06 Aug 16 Aug 26 Sep 05 Sep 15 Sep 25
Date
In summary, the ozone model performance statistics for the CAMx 2016fj (2016v2)
simulation are within or close to the ranges found in other recent peer-reviewed applications (e.g.,
Simon et al, 2012 and Emery et al, 2017). The predictions from the 2016v2 modeling platform
generally correspond closely to observed concentrations in terms of the magnitude, temporal
fluctuations, and geographic differences for MDA8 ozone concentrations. Thus, the model
performance results demonstrate the scientific credibility of our 2016v2 modeling platform. These
results provide confidence in the ability of the modeling platform to provide a credible basis for use
in estimating future year benefits associated with this rule.
35
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5. Evaluation of Methods for Estimating Ozone Impacts based on Oil & Gas Source
Apportionment Modeling using Two Emissions Sensitivity Scenarios
The methods described in section 4 have been used for previous EPA rulemakings focusing
on electric generating units (EGUs) (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2023a, 2023b, 2023c,
2023 d) and were developed to address the challenge of rapidly generating spatially resolved air
quality impacts to support EPA rulemakings whose schedules do not allow the time necessary to
simulate the final regulatory alternatives using full photochemical air quality modeling simulations.
The purpose of this analysis is to provide further information on the accuracy of the methods used to
estimate ozone impacts in this rulemaking by comparing estimates of ozone impacts derived from
these methods against results to those that would be obtained from a photochemical modeling
simulation. Specifically, we provide comparisons of results for emissions scenarios focusing on the
oil and natural gas sector, while previous applications have analyzed emissions scenarios for the EGU
sector.
As described below, we construct two hypothetical oil and natural gas emissions scenarios
with which to demonstrate the capabilities for the state-level oil and natural gas source apportionment
dataset and methods describe in Section 4. Note that these hypothetical emissions scenarios are not
regulatory alternatives but rather emissions perturbations that were designed for the purpose of testing
the ozone changes that would result from changes in emissions from the oil and natural gas sector.
Ozone impacts for these two hypothetical emissions scenarios were simulated with the CAMx model
and serve as a basis against which to compare impacts derived using the source apportionment scaling
methodology described in Section 4 with state-by-state oil and natural gas emissions tags.
The steps for comparing the source apportionment scaling methodology to CAMx model
results are as follows:
1. Create gridded 12 km ozone impacts using the CAMx model
1.1. Simulate the 20261] base case emissions using the CAMx Model
1.2. Simulate the hypothetical emissions cases using the CAMx Model
1.3. Create gridded eVNA surfaces of AS-M03 for model simulations in steps 1.1 and 1.2 using
eq 1 from Section 3.2
1.4. Using the gridded eVNA surfaces, create gridded 12 km ozone impacts for each of the
hypothetical emissions scenarios compared to the 20261] base case
36
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2. Create eVNA gridded 12km ozone impacts using the source apportionment scaling method
described in Section 3.2 for the hypothetical emissions cases.
2.1. Subtract surfaces of the 2026fj basecase from each hypothetical emissions case to create
gridded surfaces of ozone impacts for each emissions case.
3. Compare gridded ozone impacts from step 1 and step 2, using the CAMx simulations as the
benchmark
The first hypothetical oil and natural gas emissions scenario was a 35% across-the-board
reduction in the 20261] sector-wide VOC emissions. The emissions reductions were simulated for the
entire sector as defined in the 2016v2 EPA emissions modeling platform (US EPA, 2022a) and
includes both sources that are regulated under this rulemaking and sources that are not regulated
under this rulemaking. The second hypothetical emissions scenario involved replacing 2026 oil and
natural gas projected NOx and VOC emission with 2023 projections from the v2 version of EPA's
2016 emissions platform (US EPA, 2022a). VOC emissions changes in these scenarios compared to
the 2026fj base case are provided in Table 5-1. Because the source apportionment tags described in
Section 4 tracked emissions from SCCs expected to be impacted by this rulemaking there is a
mismatch between the emissions tagged in the source apportionment modeling and the emissions
sources impacted in the 35% VOC cut hypothetical emissions scenario. In states where the magnitude
of VOC emissions reductions for this scenario (Table 5-1) are larger than the total emissions tracked
for the state (Table 3-3), the source apportionment scaling methodology using the dataset designed
for this rulemaking cannot be used to simulate the full impact of the 35% across-the-board VOC cut.
Table 5-1. Emissions changes for two hypothetical oil and natural gas emissions scenarios in
relation to the 2026fj base case
State Emissions changes for the 35% Emissions changes for the 2023
O&G VOC emissions cut scenario O&G emissions scenario (tpy)
(tpy)
NOx
VOC
NOx
VOC
Alabama
| 0
-3,844
104
76
Arizona
: 0
-133
-9
-7
Arkansas
o
-2,013
191
278
California
: 0
-2,128
505
1,046
Colorado
o
-27,303
332
0
Florida
0
-455
-268
-43
ID+MT+OR
: o
-10,363
17
-10
Illinois
0
-19,080
-85
-153
Indiana
0
-3,907
-171
-33
37
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Kansas 0
-26,844
821
1,608
Kentucky 0
-12,348
317
-36
Louisiana 0
-27,968
69
-3,436
MD+VA 0
-2,570
81
64
Michigan 0
-5,578
257
-33
Mississippi 0
-3,584
26
-55
Missouri 0
-404
-319
-25
IA+NE 0
-1,031
-595
-57
Nevada 0
-74
2
0
NJ+NY 0
-2,102
-57
-85
New Mexico 0
-80,177
140
0
North Dakota 0
-109,431
663
0
Ohio 0
-6,558
-229
-989
Oklahoma 0
-72,020
4,554
10,013
Pennsylvania 0
-47,931
-644
-6,523
SC+TN 0
-1,036
-424
-108
SD+MN+WI 0
-1,858
-236
-55
Texas Central 0
-52,662
2,017
926
Texas Gulf 0
-132,985
2,473
-5,727
Texas West 0
-174,978
-260
-23,548
Utah 0
-24,062
29
0
West Virginia 0
-49,888
-1,114
-8,154
Wyoming 0
-24,144
33
0
Offshore 0
-16,910
0
0
US Total 0
-946,371
8,219
-35,066
Table 5-2 and Figure 5-1 compare ozone concentrations changes estimated using the source
apportionment scaling approach to those simulated with the CAMx photochemical model for the 35%
oil and natural gas sector VOC cut emissions scenario. Source apportionment scaling ozone impacts
matched CAMx ozone impacts very well except in locations with very small impacts (the Northwest
and West climate regions both had average ozone impacts less than 0.001 ppb as predicted by
CAMx). For all other climate regions, normalized mean bias was at or below ±26% and correlation
coefficients were in the range of 0.76-0.98. When calculated for the US as a whole, the normalized
mean bias of ozone impacts was 16% and the correlation coefficient was 0.96. This indicates that
overall, the source apportionment scaling methodology slightly underestimate the magnitude of the
CAMx ozone impacts but generally replicates the spatial nature of these impacts for this emissions
scenario.
Some of the locations visible in Figure 5-2 where the source apportionment scaling method
underestimates the CAMx ozone impacts are due to the emissions mismatch described above between
38
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the source apportionment tags and the emissions reductions for this scenario. For instance, only
72,735 tons per year of North Dakota oil and natural gas VOC emissions were tracked in the South
Dakota source apportionment modeling tag but 109,431 tpy of VOC reductions were simulated
through CMAx from North Dakota in the hypothetical emissions scenario. Similarly, the source
apportionment tag for offshore oil and natural gas sources included 12,274 tpy of VOC emissions but
the hypothetical emissions scenario reduced offshore oil and natural gas VOC emissions by 16,910.
The darker green shades in North Dakota and in the Gulf of Mexico are therefore due to the fact that
the emissions scaling method only accounted for 66% and 73% of the VOC emissions reductions
modeled by CAMx in those locations. Even so, the mean bias of predicted ozone impacts for the
Northern Rockies and Plains climate region is only 25.8%.
39
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Table 5-2. Comparison of ozone impacts estimated from source apportionment scaling versus
CAMx for hypothetical emissions scenario with 35% VQC cuts across all oil and natural gas
sources. In regions with ozone reductions predicted by CAMx (shown as negative changes), a
negative bias indicates the SA scaling method overpredicts impacts and a positive bias indicates
the SA scaling method underpredicts impacts.
Region
Change in 03 from
Emissions Sensitivity
(ppb)
MB (ppb)
NMB (%)
Correlation
Coefficient
(r)
SA scaling
CAMx
Northeast
-0.012
-0.014
0.002
13.2
0.98
Northern Rockies & Plains
-0.019
-0.025
0.007
25.8
0.90
Northwest
-0.001
0.001
-0.002
-273.5 -0.09
Ohio Valley
-0.024
-0.027
0.004
13.0
0.93
South
-0.037
-0.046
0.009
19.3
0.97
Southeast
-0.007
-0.006
-0.001
-14.4
0.76
Southwest
-0.022
-0.024
0.002
7.3 0.98
Upper Midwest
-0.017
-0.022
0.005
24.2
0.98
West
-0.001
-2.6E-05
-0.001
-5552.6 0.35
US
-0.018
-0.022
0.004
16.0
0.96
Figure 5-1. Maps comparing of ozone impacts estimated from CAMx (left) to estimates based
on source apportionment scaling (center) for the hypothetical emissions scenario with 35%
VOC cuts across all oil and natural gas sources. Absolute ozone differences between CAMx
estimates and source apportionment scaling estimates are shown on the right panel.
40
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For the second hypothetical emissions scenario involved replacing 2026 oil and natural gas
projected NOx and VOC emission with 2023 projections, Table 5-1 shows that NOx emissions were
projected to be higher in 2023 than in 2026 (by about 8,000 tpy) but VOC emissions were projected
to be lower in 2023 than in 2026 (by over 35,000 tpy). As shown in Figure 5-2, these emissions
changes resulted in spatially heterogeneous changes in CAMx ozone predictions with increases
modeled in California and parts of the central US but decreases in ozone modeled near the Texas-
New Mexico border and in the Northeastern US. Because the SCCs tagged in the source
apportionment modeling did not include many of the oil and natural gas NOx sources, the source
apportionment scaling method was only partially able to replicate this scenario. NOx impacts on
ozone were not quantified for any states where tagged NOx emissions were less than 100 tpy and
therefore the source apportionment scaling methodology did not capture an ozone impacts from NOx
changes in states other than Colorado, New Mexico, North Dakota, Oklahoma, Pennsylvania, Texas,
West Virginia, and Wyoming. Specifically, there were 3 states without sufficient tagged NOx
emissions to scale which had NOx emissions changes larger than 500 tpy in this emissions scenario:
California, Kansas and New Jersey. In West Virginia, the total NOx decreases in the 2023 projections
versus the 2026 projections were larger than the total tagged NOx emissions for that state. VOC tags
were sufficient to fully capture simulated VOC emissions change in all states with the source
apportionment scaling methodology. Ozone impacts were similar across the US with a normalized
mean bias of 24.9% and a spatial correlation of 0.82. Ozone impacts in the regions with the largest
CAMx-predicted ozone impacts were generally captured by the source apportionment scaling
methodology as shown by statistics in Table 5-3 for the South, Northern Rockies and Plains and
Northeast regions. Additionally Figure 5-2 shows similar bands of ozone increases in this scenario
across Texas, Oklahoma and Northward as well as decreases in ozone in Western Pennsylvania. The
source apportionment scaling methodology misses the ozone increases predicted by CAMx in
California, likely as a result of not having sufficient tagged NOx emissions to scale in California. A
larger magnitude of normalized mean bias is apparent in Table 5-3 for several regions with very small
modeled ozone impacts: Southeast, Ohio Valley and Upper Midwest.
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Table 5-3. Comparison of ozone impacts estimated from source apportionment scaling versus
CAMx for hypothetical emissions scenario with 2023 oil and natural gas projections. In regions
with ozone reductions predicted by CAMx (shown as negative changes), a negative bias
indicates the SA scaling method overpredicts impacts and a positive bias indicates the SA
scaling method underpredicts impacts.
Region
Change in 03 from
Emissions Sensitivity
(PPb)
MB (ppb)
NMB (%)
Correlation
Coefficient (r)
SA scaling
CAMx
Northeast
-0.006
-0.007
0.001
19.9
0.69
Northern Rockies & Plains
0.010
0.007
0.003
52.3
0.92
Northwest
0.0003
0.0005
-0.0002
-31.6
0.23
Ohio Valley
0.003
0.001
0.003
533.9
0.80
South
0.033
0.031
0.002
6.7
0.77
Southeast
0.001
-0.003
0.004
133.5
0.38
Southwest
0.005
0.003
0.002
74.5
0.24
Upper Midwest
0.008
0.004
0.004
117.2
0.73
West
0.001
0.006
-0.006
-88.1
0.54
US
0.009
0.008
0.002
24.9
0.82
r
ppb ppb ppb
Figure 5-2. Maps comparing of ozone impacts estimated from CAMx (left) to estimates based
on source apportionment scaling (center) for hypothetical emissions scenario with 2023 oil and
natural gas emissions projections versus 2026 oil gas emissions projections. Absolute ozone
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differences between CAMx estimates and source apportionment scaling estimates are shown on
the right panel.
Overall, for these test cases using hypothetical emissions scenarios the source apportionment
scaling methodology using the state-by-state oil and natural gas tags provided reasonable
approximations of photochemical model-based ozone impacts. The largest discrepancies between the
modeled impacts and the scaling method can be explained by discrepancies in how emissions were
treated in the hypothetical case modeling and the source apportionment tags used in the scaling
method. Since the source apportionment tags were designed explicitly to track the subset of sources
impacted by this rulemaking those types of emissions discrepancies are not present in the rule
analysis.
6. Uncertainties and Limitations of the Air Quality Methodology
One limitation of the scaling methodology for creating ozone surfaces associated with the
baseline or regulatory alternatives described above is that the methodology treats ozone changes from
the tagged sources as linear and additive. It therefore does not account for nonlinear atmospheric
chemistry and does not account for interactions between emissions of different pollutants and
between emissions from different tagged sources. The method applied in this analysis is consistent
with how air quality impact analysis has been performed in several prior regulatory analyses U.S.
EPA, 2019b, 2020a, 2020b, 2021, 2023a, 2023b, 2023c, 2023d).
We note that air quality is calculated in the same manner for the baseline and regulatory
alternatives, so any uncertainties associated with these assumptions is propagated through results for
both the baseline and regulatory alternatives in the same manner. While ozone formation often
responds nonlinearly to changes in NOx emissions, previous studies have shown that ozone
concentrations generally respond more linearly to changes in VOC emissions (Hakami et al, 2003;
Hakami et al., 2004). A second limitation is that the source apportionment contributions are informed
by the spatial and temporal distribution of the emissions from each source tag as they occur in the
2026 modeled case. Thus, the contribution modeling results do not allow us to consider the effects of
any changes to spatial distribution of oil and natural gas emissions within a state between the 2026
modeled case and the baseline and regulatory alternatives in this RIA. While oil and natural gas
facilities may stop operating over time, new facilities are established. In general, in the timeframe of
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the analysis for this rulemaking, new facilities are likely to be located in the same major basins as
facilities that stop production. The analysis provided in Section 5 quantitatively assess the impacts for
these two uncertainties and found that for the two hypothetical emissions cases analyzed, the
combined effects of simplifying assumptions of linear scaling and static spatial distribution of
emissions within the tagged sources resulted in mean biases of less than 25% compared to full-form
CAMx modeling which accounted for the more complex treatment of chemistry and emissions
changes. Finally, the 2026 CAMx-modeled concentrations themselves have some uncertainty. While
all models have some level of inherent uncertainty in their formulation and inputs, the base-year 2016
model outputs have been evaluated against ambient measurements and have been shown to
adequately reproduce spatially and temporally varying concentrations as described in section 4.
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-23-007
Environmental Protection Air Quality Assessment Division November 2023
Agency Research Triangle Park, NC
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