* _ Vo
PRO"^
Modeled PM2.5 and 03 Impacts from Offshore
Wind Energy Project Leased and Planning Areas
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EPA-454/R/24/006
December 2024
Modeled PM2.5 and 03 Impacts from Offshore Wind Energy Project Leased and Planning Areas
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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BACKGROUND
This document is intended to provide information for permitting authorities and permit applicants
relating precursor emissions from offshore wind energy projects to ozone (03) and secondarily formed
particulate matter less than 2.5 microns in diameter (PM2.5) impacts. Primarily emitted PM2.5 should be
estimated with tools intended for that purpose. Section 4.2.2.3 of the Guideline on Air Quality Models
(the "Guidelinepublished as Appendix W to 40 CFR part 51) states that the impacts of offshore primary
pollutants should be modeled using the Offshore and Coastal Dispersion (OCD) model (or other case
specific alternative model approved by EPA) for distances out to 50 km (section 8.1.2) from the source.
The Guideline recommends a two-tiered approach for addressing single-source impacts on 03 and
secondary PM2.5 (U.S. Environmental Protection Agency, 2021). The first tier (or Tier 1) involves use of
appropriate and technically credible relationships between emissions and ambient impacts developed
from existing modeling studies deemed sufficient for evaluating impacts from a project. The second tier
(or Tier 2) involves more sophisticated case-specific application of chemical transport modeling (e.g.,
with an Eulerian grid or Lagrangian model) (U.S. Environmental Protection Agency, 2021).
The document is intended to provide relationships between precursors and maximum downwind
impacts of 03 and PM2.5 for the purposes of developing a technically credible Tier 1 demonstration tool
for sources offshore of the conterminous U.S. coast. Specifically, the emissions sources in this
assessment represent areas offshore of the United States that have been leased or planned for the
purpose of constructing wind energy projects. This approach is similar to Modeled Emission Rates for
Precursors (MERPs), which is also a Tier 1 demonstration tool (U.S. Environmental Protection Agency,
2019b) under the Prevention of Significant Deterioration (PSD) permitting program that provides a
simple way to relate maximum downwind impacts with a critical air quality threshold (e.g., a significant
impact level or SIL) (U.S. Environmental Protection Agency, 2018). These relationships between
emissions and downwind impacts were needed to represent the offshore chemical and physical
environment which was not reasonably reflected in the existing Tier 1 MERPs database. Relationships
between emissions and downwind impacts of primarily emitted PM2.5 are only provided for distances
beyond 50 km for permit related assessments where that information may be useful (e.g., Class I
increment for the PSD program).
Similar information has been previously provided by EPA for a smaller set of leased areas off the Atlantic
Coast (U.S. Environmental Protection Agency, 2022). This document provides information for more
areas and more types of emissions sources (Class 1 or 2 and Class 3 commercial marine vessels). Some of
the same areas have information in the previous assessment and new information provided in this
assessment that may be different. The previous assessment used a different year for meteorology,
different assumptions about offshore emission rates and stack parameters, and had a different
representation of other emissions sources in the region.
03 formation is a complicated, nonlinear process that depends on meteorological conditions in addition
to volatile organic compounds (VOC) and nitrogen oxides (NOx) concentrations (Seinfeld and Pandis,
2008). Warm temperatures, clear skies (abundant levels of solar radiation), and stagnant air masses (low
wind speeds) increase 03 formation potential (Seinfeld and Pandis, 2008). In the case of PM2.5, total
mass is often categorized into two groups: primary (i.e., emitted directly as PM2.5 from sources) and
secondary (i.e., PM2.5 formed in the atmosphere by precursor emissions from sources). PM2.5 sulfate,
nitrate, and ammonium are predominantly the result of chemical reactions of the oxidized products of
sulfur dioxide (S02) and NOx emissions and direct ammonia (NH3) emissions (Seinfeld and Pandis, 2008).
PM2.5 organic aerosol (primary and secondary), nitrate, and ammonium are also impacted by
1
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semivolatile partitioning that is influenced by both the meteorological conditions and the chemical
environment.
EPA believes that use of photochemical models for estimating single source secondary pollutant impacts
is scientifically appropriate (U.S. Environmental Protection Agency, 2019b, 2021). Photochemical models
treat emissions, chemical transformation and partitioning, transport, and deposition using time and
space variant meteorology. These modeling systems simulate primarily emitted species and secondarily
formed pollutants such as 03 and PM2.5 (Kelly et al., 2019; Simon et al., 2012). Even though single source
emissions are injected into a grid volume, photochemical transport models have been shown to
adequately capture single source impacts when compared with downwind in-plume measurements
(Baker and Kelly, 2014; Baker and Woody, 2017).
Some photochemical models have been instrumented with source apportionment capabilities which
tracks emissions from specific sources through chemical transformation, transport, and deposition
processes to estimate source-specific impacts to predicted air quality at downwind receptors (Kwok et
al., 2015; Kwok et al., 2013). Source apportionment has been used to differentiate the air quality impact
from single sources on model predicted 03 and PM2.5 (Baker and Foley, 2011; Baker and Kelly, 2014;
Baker et al., 2016; Baker and Woody, 2017). Photochemical grid model source apportionment and
source sensitivity simulation of single-source downwind impacts compare well against field study
primary and secondary ambient in-plume measurements (Baker and Kelly, 2014; Baker and Woody,
2017; ENVIRON International Corporation, 2012). This work indicates photochemical grid models using
source apportionment or source sensitivity approaches provide meaningful estimates of single source
impacts.
This document presents an overview of EPA photochemical modeling of hypothetical offshore emissions
sources with National Emission Inventory (NEI) based emissions representing other sources on
downwind 03 and secondary PM2.5. Ozone contributions were estimated using Ozone Source
Apportionment Technology and PM2.5 contributions using Particulate Source Apportionment Technology
as implemented in the CAMx photochemical model (Ramboll, 2022). The contribution from each of
these emissions sources to model predicted 03 and inorganic PM2.5 ions (sulfate, nitrate, ammonium)
were tracked using reactive tracers which track impacts of chemistry, atmospheric transport and
deposition in the photochemical model (Kwok et al., 2015; Kwok et al., 2013; Ramboll, 2022). Primary
emitted PM2.5 was tracked with inert tracers which track impacts of atmospheric transport and
deposition in the photochemical model. All precursor impacts on PM2.5 and 03 are tracked separately
(e.g., NOxto 03, VOCto 03, etc.).
MODEL CONFIGURATION & APPLICATION
Wind farm construction and operation includes the use of multiple categories of commercial marine
vessels. Category 1 (CI) and Category 2 (C2) vessels have marine diesel engines above 800 horsepower
(hp) with displacement less than 30 liters per cylinder. Category 3 (C3) engines are those at or above 30
liters per cylinder, typically these are the largest engines rated at 3,000 to 100,000 hp. C3 engines are
typically used for propulsion on ocean-going vessels. CI and C2 marine diesel engines typically range in
size from about 700 to 11,000 hp. These engines are used to provide propulsion power on many kinds of
vessels including tugboats, towboats, supply vessels, fishing vessels, and other commercial vessels in
and around ports. They are also used as stand-alone generators for auxiliary electrical power on many
types of vessels.
2
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The locations tracked for contribution are shown in Figures 1 to 5. These locations were selected based
on a review of areas offshore that were leased to private entities for the purpose of wind farm
construction and operation. Additional areas were tracked for contribution that may be used for wind
farm construction in the future (planning areas).
Annual emission totals in tons per year (tpy) were provided for total primarily emitted PM2.s, coarse
fraction PM, VOC, NH3, SO;., and NOx for each hypothetical source. Emission rates for each hypothetical
source in planning areas are shown in Table 1 for the Gulf region and Table 2 for other areas. Table 3
provides emissions information for the hypothetical sources located in leased areas off the Atlantic
coast. Each hypothetical source area was modeled separately with characteristics consistent with C3 and
C1/C2 types of vessels which results in 2 separate sets of air quality impacts for each location (one for C3
and one for C1/C2 types of sources). The C3-like hypothetical sources were assigned stack
characteristics consistent with C3 commercial marine vessels: 20 m stack height, 0.8 m stack diameter,
25 m/s exit velocity, and 555 K exit temperature. The Cl/C2~like hypothetical sources were assigned
stack characteristics consistent with C1/C2 commercial marine vessels: 0.5 m stack height, 0.3 m stack
diameter, 0.03 m/s exit velocity, and 294 K exit temperature. NOx, VOC, and primary PM emissions were
speciated consistent with profiles used for the commercial marine sector. Temporal profiles were
unique to each hypothetical source location and based on nearby commercial marine vessel activity
reported in the 2017 AIS database (NOAA Office for Coastal Management, 2022).
Figure 1. Tags used in this assessment and offshore planning areas in the Gulf of Mexico. There is no
tag 1 included in this assessment.
3
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Figure 2. Tags used in this assessment and offshore planning and leased areas off the Atlantic coast.
Leased Areas
Planning Areas
4
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Figure 3. Tags used in this assessment and offshore planning areas off the South Carolina coast.
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Figure 4. Tags used in this assessment and offshore planning and leased areas off the northern
Pacific (Oregon/California) coast.
6
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Figure 5. Tags used in this assessment and offshore planning and leased areas off the Pacific coast
(California).
7
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Table 1. Annual total emissions (tpy) assigned to hypothetical sources in the Gulf of Mexico region.
Tag Latitude Longitude NOX VOC PM2_5 SQ2 NH3 CO
2
26.400
-96.400
112.73
1
14
11.36
1
14
0.11
1
14
3
27.050
-96.950
112.73
1
14
11.36
1
14
0.11
1
14
4
27.050
-96.450
112.73
1
14
11.36
1
14
0.11
1
14
5
27.600
-96.700
112.73
1
14
11.36
1
14
0.11
1
14
6
27.600
-96.300
112.73
1
14
11.36
1
14
0.11
1
14
7
28.000
-96.150
112.73
1
14
11.36
1
14
0.11
1
14
8
28.000
-95.800
112.73
1
14
11.36
1
14
0.11
1
14
9
28.200
-95.500
112.73
1
14
11.36
1
14
0.11
1
14
10
28.600
-95.250
112.73
1
14
11.36
1
14
0.11
1
14
11
28.300
-95.000
112.73
1
14
11.36
1
14
0.11
1
14
12
28.650
-95.000
112.73
1
14
11.36
1
14
0.11
1
14
13
28.050
-94.700
112.73
1
14
11.36
1
14
0.11
1
14
14
28.850
-94.450
112.73
1
14
11.36
1
14
0.11
1
14
15
28.400
-94.450
112.73
1
14
11.36
1
14
0.11
1
14
16
28.050
-94.200
112.73
1
14
11.36
1
14
0.11
1
14
17
28.850
-94.000
112.73
1
14
11.36
1
14
0.11
1
14
18
28.850
-93.650
112.73
1
14
11.36
1
14
0.11
1
14
19
28.850
-93.200
112.73
1
14
11.36
1
14
0.11
1
14
20
28.850
-92.700
112.73
1
14
11.36
1
14
0.11
1
14
21
28.850
-92.300
112.73
1
14
11.36
1
14
0.11
1
14
22
28.850
-91.800
112.73
1
14
11.36
1
14
0.11
1
14
23
28.850
-91.400
112.73
1
14
11.36
1
14
0.11
1
14
24
28.400
-94.000
112.73
1
14
11.36
1
14
0.11
1
14
25
28.400
-93.650
112.73
1
14
11.36
1
14
0.11
1
14
26
28.400
-93.200
112.73
1
14
11.36
1
14
0.11
1
14
27
28.400
-92.700
112.73
1
14
11.36
1
14
0.11
1
14
28
28.400
-92.300
112.73
1
14
11.36
1
14
0.11
1
14
29
28.400
-91.800
112.73
1
14
11.36
1
14
0.11
1
14
30
28.400
-91.400
112.73
1
14
11.36
1
14
0.11
1
14
31
29.350
-93.650
112.73
1
14
11.36
1
14
0.11
1
14
32
28.000
-93.650
112.73
1
14
11.36
1
14
0.11
1
14
33
29.350
-93.200
112.73
1
14
11.36
1
14
0.11
1
14
34
28.000
-93.200
112.73
1
14
11.36
1
14
0.11
1
14
35
29.250
-92.550
112.73
1
14
11.36
1
14
0.11
1
14
36
29.250
-92.150
112.73
1
14
11.36
1
14
0.11
1
14
37
28.700
-91.000
112.73
1
14
11.36
1
14
0.11
1
14
38
28.700
-90.600
112.73
1
14
11.36
1
14
0.11
1
14
39
28.300
-91.000
112.73
1
14
11.36
1
14
0.11
1
14
40
28.300
-90.600
112.73
1
14
11.36
1
14
0.11
1
14
41
28.750
-90.300
112.73
1
14
11.36
1
14
0.11
1
14
42
28.300
-90.300
112.73
1
14
11.36
1
14
0.11
1
14
43
28.725
-90.000
112.73
1
14
11.36
1
14
0.11
1
14
44
28.250
-90.000
112.73
1
14
11.36
1
14
0.11
1
14
8
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Table 2. Annual total emissions (tpy) assigned to hypothetical sources in planning areas outside the
Gulf of Mexico region.
Tag Latitude Longitude NOX VOC PM2_5 SQ2 NH3 CO
45
35.150
-121.510
1240.00
12.50
125.00
12.50
1.25
12.50
46
35.150
-121.310
1240.00
12.50
125.00
12.50
1.25
12.50
47
35.650
-121.915
1240.00
12.50
125.00
12.50
1.25
12.50
48
35.550
-121.775
1240.00
12.50
125.00
12.50
1.25
12.50
49
33.475
-78.025
708.57
7.14
71.43
7.14
0.71
7.14
50
33.670
-78.700
708.57
7.14
71.43
7.14
0.71
7.14
51
33.600
-78.450
708.57
7.14
71.43
7.14
0.71
7.14
52
33.550
-78.750
708.57
7.14
71.43
7.14
0.71
7.14
53
33.400
-78.650
708.57
7.14
71.43
7.14
0.71
7.14
54
33.345
-78.800
708.57
7.14
71.43
7.14
0.71
7.14
55
33.175
-78.925
708.57
7.14
71.43
7.14
0.71
7.14
56
32.940
-79.190
992
10.00
100.00
10.00
1.00
10.00
57
32.800
-79.440
992
10.00
100.00
10.00
1.00
10.00
58
32.505
-79.310
992
10.00
100.00
10.00
1.00
10.00
59
32.740
-78.650
992
10.00
100.00
10.00
1.00
10.00
60
32.800
-78.500
992
10.00
100.00
10.00
1.00
10.00
61
33.440
-77.890
4960
50.00
499.99
50.00
5.00
50.00
62
38.600
-74.450
2480
25.00
249.99
25.00
2.50
25.00
63
38.000
-74.625
2480
25.00
249.99
25.00
2.50
25.00
64
38.000
-73.366
2480
25.00
249.99
25.00
2.50
25.00
65
38.100
-73.110
2480
25.00
249.99
25.00
2.50
25.00
66
37.766
-74.775
992
10.00
100.00
10.00
1.00
10.00
67
37.600
-74.666
992
10.00
100.00
10.00
1.00
10.00
68
37.250
-74.850
992
10.00
100.00
10.00
1.00
10.00
69
37.500
-74.020
992
10.00
100.00
10.00
1.00
10.00
70
37.140
-74.100
992
10.00
100.00
10.00
1.00
10.00
71
37.500
-73.800
1653.33
16.67
166.66
16.67
1.67
16.67
72
37.275
-73.700
1653.33
16.67
166.66
16.67
1.67
16.67
73
37.675
-73.500
1653.33
16.67
166.66
16.67
1.67
16.67
74
36.915
-75.000
992
10.00
100.00
10.00
1.00
10.00
75
36.200
-74.400
992
10.00
100.00
10.00
1.00
10.00
76
36.300
-74.200
992
10.00
100.00
10.00
1.00
10.00
77
36.600
-74.250
992
10.00
100.00
10.00
1.00
10.00
78
36.650
-74.050
992
10.00
100.00
10.00
1.00
10.00
79
43.350
-124.820
1240
12.50
125.00
12.50
1.25
12.50
80
43.580
-124.700
1240
12.50
125.00
12.50
1.25
12.50
81
43.800
-124.600
1240
12.50
125.00
12.50
1.25
12.50
82
43.750
-124.820
1240
12.50
125.00
12.50
1.25
12.50
83
42.075
-124.885
2480
25.00
249.99
25.00
2.50
25.00
84
42.220
-124.790
2480
25.00
249.99
25.00
2.50
25.00
85
40.980
-124.680
4960
50.00
499.99
50.00
5.00
50.00
86
40.633
-72.100
1653.33
16.67
166.66
16.67
1.67
16.67
87
40.590
-72.300
1653.33
16.67
166.66
16.67
1.67
16.67
88
40.433
-72.866
1653.33
16.67
166.66
16.67
1.67
16.67
9
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Table 3. Annual total emissions (tpy) assigned to hypothetical sources in the leased areas off the
Atlantic coast.
Tag Latitude Longitude NOX VOC PM2_5 SQ2 NH3 CO
2
36.908
-75.349
500.00
50.00
500.00
50.00
5.00
50.00
3
41.154
-71.080
500.00
50.00
500.00
50.00
5.00
50.00
4
40.985
-71.045
500.00
50.00
500.00
50.00
5.00
50.00
5
38.347
-74.761
500.00
50.00
500.00
50.00
5.00
50.00
6
36.891
-75.535
500.00
50.00
500.00
50.00
5.00
50.00
7
39.122
-74.242
500.00
50.00
500.00
50.00
5.00
50.00
8
39.273
-74.093
500.00
50.00
500.00
50.00
5.00
50.00
9
40.969
-70.793
500.00
50.00
500.00
50.00
5.00
50.00
10
41.043
-70.482
500.00
50.00
500.00
50.00
5.00
50.00
11
41.268
-71.436
500.00
50.00
500.00
50.00
5.00
50.00
12
36.339
-75.127
500.00
50.00
500.00
50.00
5.00
50.00
13
40.295
-73.315
500.00
50.00
500.00
50.00
5.00
50.00
14
41.089
-71.133
500.00
50.00
500.00
50.00
5.00
50.00
15
38.565
-74.665
500.00
50.00
500.00
50.00
5.00
50.00
16
40.824
-70.524
500.00
50.00
500.00
50.00
5.00
50.00
17
40.747
-70.416
500.00
50.00
500.00
50.00
5.00
50.00
18
40.682
-70.228
500.00
50.00
500.00
50.00
5.00
50.00
19
39.065
-74.379
500.00
50.00
500.00
50.00
5.00
50.00
20
40.895
-70.658
500.00
50.00
500.00
50.00
5.00
50.00
21
39.976
-72.740
500.00
50.00
500.00
50.00
5.00
50.00
22
39.718
-73.170
500.00
50.00
500.00
50.00
5.00
50.00
23
39.541
-73.304
500.00
50.00
500.00
50.00
5.00
50.00
24
39.361
-73.579
500.00
50.00
500.00
50.00
5.00
50.00
25
39.304
-73.461
500.00
50.00
500.00
50.00
5.00
50.00
26
40.242
-73.079
500.00
50.00
500.00
50.00
5.00
50.00
27
39.472
-74.004
500.00
50.00
500.00
50.00
5.00
50.00
Model Configuration
Annual 2016 photochemical model simulations were performed for a domain covering the contiguous
United States with 12 km sized grid cells (Figure 6). Each simulation tracked a different combination of
pollutants. All simulations were conducted using version 7.20 of the Comprehensive Air Quality Model
with Extensions (CAMx) photochemical grid model (www.camx.com) (Emery et al., 2024). This CAMx
application includes ISORROPIA inorganic chemistry (Nenes et al., 1998), gas phase reactions based on
the Carbon Bond (CB6r5) mechanism (Ramboll, 2016, 2022), and aqueous phase reactions (Ramboll,
2022). Chemical boundary inflow was extracted from a photochemical model simulation for 2016 with a
larger geographic domain covering the northern hemisphere (U.S. Environmental Protection Agency,
2019a).
A total of 35 layers were used to represent the vertical atmosphere to 50 mb with thinner layers nearer
the surface (the height of the layer closest to the surface is approximately 20 m). The meteorological
model configuration, application, and evaluation are available in a separate document (U.S.
10
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Environmental Protection Agency, 2019c). Baseline emissions include anthropogenic sources based on
the "2016gf" version of the 2016 emissions modeling platform (U.S. Environmental Protection Agency,
2023) and biogenic sources estimated with the Biogenic Emission Inventory System version 3.6.1 (Bash
et al., 2016). Mobile emissions were based on the MOVES2014b model. Wildland fire emissions were
day specific for 2016 (U.S. Environmental Protection Agency, 2020)
Figure 6. Photochemical model domain. The locations of offshore leased and planning areas are
shown on the left panel and climate zones used for model performance on the right panel.
Model Application
The photochemical model was applied for the entire year of 2016 at 12 km grid resolution. Additional
hypothetical offshore emissions sources were included in addition to the 2016 anthropogenic emissions
and tracked for contribution to air quality impacts using source apportionment. Table 4 shows the
relationship between precursor emissions and contribution to modeled primary and secondary
pollutants. The model was applied so that primary and secondary precursors were tracked for
contribution to modeled PM2.5 components. NOx emissions were tracked for contribution to PM2.5
nitrate ion, NH3 emissions were tracked for contribution to PM2.s ammonium ion, and S02 emissions
were tracked for contribution to PM25 sulfate ion.
Primarily emitted elemental carbon, organic aerosol, and crustal components were tracked to model
predicted PM25 emitted as primary only. Primarily emitted coarse PM components were tracked to
model predicted coarse PM. The contribution to PM25 nitrate does not include primarily emitted PM2 5
nitrate and the contribution to PM25 sulfate does not include primarily emitted PM2 5 sulfate.
Leased Areas
Planning Areas
ii
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Table 4. Relationship between emissions species and tracked primary and secondarily formed PM2.5
and 03 in the modeling system.
Precursor
Tagged Pollutant
Primary PM2 5
Coarse PM
VOC
S02
NOx
NH3
NOx
Secondarily formed PM2.5 sulfate ion
Secondarily formed PM2.5 nitrate ion
Secondarily formed PM2.5 ammonium ion
Primary PM25: FCRS, FPRM, PEC, POA
Primary coarse PM: CCRS, CPRM
Ozone
Ozone
MODEL PERFORMANCE EVALUATION
Particulate Matter
An operational model performance evaluation for the speciated components of PM2.5 (e.g., sulfate,
nitrate, elemental carbon, organic carbon, etc.) was conducted using 2016 monitoring data in order to
estimate the ability of the modeling system to predict ambient concentrations. The evaluation of PM2.5
component species includes comparisons of predicted and observed concentrations of sulfate (S04),
nitrate (N03), elemental carbon (EC), and organic carbon (OC). Chemically speciated PM2.5 ambient
measurements for 2016 were obtained from the Chemical Speciation Network (CSN) and the
Interagency Monitoring of PROtected Visual Environments (IMPROVE). The CSN sites are generally
located within urban areas and the IMPROVE sites are typically in rural/remote areas. The
measurements at CSN and IMPROVE sites represent 24-hour average concentrations. In calculating the
model performance metrics, the modeled hourly species predictions were aggregated to the averaging
times of the measurements.
Model performance statistics were calculated for observed/predicted pairs of all daily concentrations
measured in 2016 (Simon et al., 2012). Aggregated metrics and number (N) of prediction-observation
pairs are shown by chemical specie for each climate zone in Table 5. Metrics include mean observed,
mean model predicted, mean bias, mean error, normalized bias, normalized error, and correlation
coefficient. Model performance was compared to the performance found in recent regional PM2.5 model
applications for other assessments. Overall, the mean bias (bias) and mean error (error) statistics are
within the range or close to that found by other groups in recent applications (Kelly et al., 2019; Simon
et al., 2012; Wilson et al., 2019). Overall, the model performance results provide confidence that this
application of CAMx provides a scientifically credible approach for estimating PM2.5 concentrations for
the purposes of this assessment. More details about model performance for particulates is provided
elsewhere (Emery et al., 2024).
12
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Table 5. Aggregated model performance metrics for speciated components of PM2.5 for the
IMPROVE and CSN monitor networks.
Mean Mean
Climate Observed Predicted Mean Bias Mean Error Normalized Normalized
SPECIE
Zone
N
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
Bias (%)
Error (%)
r
PM2.5 elemental carbon
NE
5,187
0.4
0.5
0.1
0.2
11.7
47.7
0.7
PM2.5 elemental carbon
WEST
3,377
0.3
0.3
0.0
0.1
-12.0
43.4
0.7
PM2.5 elemental carbon
SE
3,492
0.4
0.3
-0.1
0.2
-18.6
44.7
0.5
PM2.5 elemental carbon
PLAINS
2,829
0.1
0.1
0.0
0.1
-10.6
66.6
0.3
PM2.5 elemental carbon
OV
3,098
0.5
0.4
0.0
0.2
-5.2
39.7
0.6
PM2.5 elemental carbon
UPMW
2,279
0.3
0.3
0.0
0.2
7.1
49.2
0.6
PM2.5 elemental carbon
NW
2,401
0.2
0.4
0.1
0.2
65.0
103.4
0.6
PM2.5 elemental carbon
SW
4,667
0.2
0.2
0.0
0.1
-16.4
48.5
0.7
PM2.5 elemental carbon
SOUTH
2,012
0.3
0.3
0.0
0.1
-8.5
44.1
0.7
PM2.5 nitrate ion
NE
5,174
0.7
1.4
0.7
0.8
110.0
127.9
0.7
PM2.5 nitrate ion
WEST
3,712
1.0
0.8
-0.2
0.6
-17.9
61.4
0.7
PM2.5 nitrate ion
SE
3,860
0.3
1.0
0.6
0.7
177.1
190.3
0.5
PM2.5 nitrate ion
PLAINS
2,860
0.3
0.3
0.1
0.2
35.5
86.6
0.7
PM2.5 nitrate ion
OV
3,123
1.0
1.7
0.8
1.1
79.7
111.0
0.5
PM2.5 nitrate ion
UPMW
2,117
1.0
1.6
0.6
0.9
61.1
92.3
0.7
PM2.5 nitrate ion
NW
2,569
0.3
0.4
0.1
0.3
40.5
107.7
0.3
PM2.5 nitrate ion
SW
4,850
0.3
0.2
-0.1
0.2
-38.4
67.9
0.5
PM2.5 nitrate ion
SOUTH
2,267
0.4
0.9
0.4
0.6
104.2
134.9
0.6
PM2.5 organic carbon
NE
5,206
1.5
2.3
0.8
1.0
56.5
69.4
0.7
PM2.5 organic carbon
WEST
3,478
1.5
1.3
-0.3
0.7
-16.3
44.9
0.6
PM2.5 organic carbon
SE
3,512
2.5
2.2
-0.3
1.5
-10.7
60.4
0.1
PM2.5 organic carbon
PLAINS
2,962
0.8
0.6
-0.2
0.5
-23.6
61.5
0.3
PM2.5 organic carbon
OV
3,102
1.7
2.3
0.6
0.9
34.7
53.8
0.5
PM2.5 organic carbon
UPMW
2,291
1.2
1.8
0.5
0.8
45.0
66.2
0.5
PM2.5 organic carbon
NW
2,516
1.0
1.4
0.4
0.8
37.2
82.1
0.5
PM2.5 organic carbon
SW
4,763
0.9
0.7
-0.2
0.5
-23.2
54.7
0.4
PM2.5 organic carbon
SOUTH
2,031
1.4
1.6
0.2
0.7
14.0
53.1
0.6
PM2.5 sulfate ion
NE
5,185
0.9
1.2
0.3
0.4
29.9
47.1
0.6
PM2.5 sulfate ion
WEST
3,719
0.6
0.6
-0.1
0.3
-9.1
49.1
0.5
PM2.5 sulfate ion
SE
3,568
1.1
1.2
0.1
0.4
13.6
40.9
0.5
PM2.5 sulfate ion
PLAINS
2,866
0.4
0.5
0.2
0.2
39.4
61.5
0.7
PM2.5 sulfate ion
OV
3,130
1.3
1.6
0.3
0.5
19.7
40.9
0.6
PM2.5 sulfate ion
UPMW
2,136
0.8
1.2
0.4
0.5
45.4
57.7
0.7
PM2.5 sulfate ion
NW
2,586
0.3
0.6
0.3
0.3
88.3
99.8
0.6
PM2.5 sulfate ion
SW
4,859
0.5
0.4
-0.1
0.2
-20.6
49.7
0.3
PM2.5 sulfate ion
SOUTH
2,271
1.2
1.2
0.0
0.5
-1.0
43.9
0.6
Model predictions and annual average bias for multiple species are provided in Figure 7. PM2.5 species
are shown for the IMPROVE, CASTNET, and CSN monitor networks. Ambient measurements of PM2.5
nitrate ion and sulfate ion are highest in the central and eastern U.S. The model tends to overpredict
PM2.5 nitrate ion in the upper midwest and has little regional bias for PM2.5 sulfate ion.
13
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Figure 7. Paired observations with model predictions. Comparisons shown for PM2.5 species: sulfate
ion and nitrate ion.
Ozone
An operational model performance evaluation for eight-hour daily maximum (MDA8) ozone was
conducted in order to estimate the ability of the modeling system to replicate the 2016 base year
concentrations. Ozone measurements were taken from 2016 monitoring site data in the Air Quality
System (AQS) The ozone metrics covered in this evaluation include eight-hour average daily maximum
ozone bias and error (Simon et al.; 2012). The evaluation principally consists of statistical assessments of
model versus observed pairs that are paired in time and space. Aggregated metrics and number (N) of
prediction-observation pairs are shown by climate zone in Table 6. Metrics include mean observed,
mean model predicted, mean bias, mean error, normalized bias, normalized error, and correlation
coefficient. Figure 8 shows prediction-observation pairs of MDA8 03 by climate zone. The plot is colored
by the density of prediction-observation pairs, with warmer colors representing situations where many
different prediction-observation pairs overlap. More details about model performance for 03 is provided
elsewhere (Emery et a!., 2024).
14
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Table 6. Aggregated model performance metrics for MDA8 03. Metrics are shown for all prediction-
observation pairs, pairs where the model predictions exceed 60 ppb, and pairs where the
observations exceed 60 ppb.
Mean
Mean
Mean
Normalize
Climate
Observed
Predicted
Mean
Error
Normalize
d Error
SPECIE
Zone
N
(ppb)
(ppb)
Bias (ppb)
(ppb)
d Bias (%)
(%)
r
MDA803
NE
35,479
44.1
47.3
3.2
6.9
7.3
15.6
0.8
MDA803
WEST
35,625
50.6
49.6
-1.0
6.8
-2.0
13.5
0.8
MDA803
SE
31,757
42.2
47.4
5.1
7.3
12.2
17.2
0.8
MDA803
PLAINS
10,120
44.0
42.2
-1.8
5.3
-4.0
12.1
0.7
MDA803
OV
40,058
45.0
50.9
5.9
7.7
13.0
17.1
0.8
MDA803
UPMW
19,269
41.7
43.1
1.4
5.9
3.3
14.2
0.8
MDA803
NW
4,238
37.3
38.3
1.0
6.2
2.7
16.5
0.7
MDA803
SW
23,650
51.7
51.5
-0.2
5.9
-0.4
11.5
0.6
MDA803
SOUTH
23,983
40.7
45.7
4.9
7.9
12.1
19.3
0.7
MDA8O3_MODgt60
NE
6,726
58.6
68.1
9.5
10.5
16.2
17.9
0.5
MDA8O3_MODgt60
WEST
6,275
68.5
66.9
-1.6
8.6
-2.4
12.5
0.4
MDA8O3_MODgt60
SE
5,104
56.3
66.5
10.2
10.6
18.2
18.8
0.5
MDA8O3_MODgt60
PLAINS
298
55.9
62.7
6.7
7.5
12.0
13.4
0.1
MDA8O3_MODgt60
OV
8,987
56.7
67.1
10.4
11.2
18.4
19.7
0.4
MDA8O3_MODgt60
UPMW
1,444
61.4
65.6
4.3
8.0
6.9
13.1
0.4
MDA8O3_MODgt60
NW
74
56.8
65.4
8.6
9.9
15.2
17.4
0.4
MDA8O3_MODgt60
SW
4,107
59.5
64.7
5.2
7.0
00
00
11.8
0.4
MDA8O3_MODgt60
SOUTH
2,545
56.0
65.9
9.9
10.7
17.7
19.0
0.4
M DA8O3_OBSgt60
NE
3,447
66.7
68.0
1.3
7.0
2.0
10.5
0.4
M DA8O3_OBSgt60
WEST
8,894
70.0
61.6
-8.5
9.8
-12.1
13.9
0.5
M DA8O3_OBSgt60
SE
1,866
65.0
67.7
2.7
6.2
4.1
9.5
0.5
M DA8O3_OBSgt60
PLAINS
240
63.0
54.4
-8.5
9.1
-13.5
14.5
0.1
M DA8O3_OBSgt60
OV
3,870
65.8
67.7
1.9
6.1
2.9
9.3
0.4
M DA8O3_OBSgt60
UPMW
1,351
66.8
62.2
-4.6
6.6
-6.9
9.9
0.5
M DA8O3_OBSgt60
NW
83
65.0
58.1
-6.9
10.0
-10.6
15.3
0.0
M DA8O3_OBSgt60
SW
3,744
64.7
59.8
-4.9
7.1
-7.6
11.0
0.3
MDA8O3_OBSgt60
SOUTH
1,179
65.0
64.1
-0.8
6.5
-1.3
10.0
0.4
Only prediction-observation pairs from April through October were included in the aggregated metrics.
This ozone model performance includes all prediction-observation pairs, a subset of prediction-
observation pairs where observed ozone exceeded 60 ppb, and a subset of prediction-observation pairs
where predicted ozone exceeded 60 ppb. This cutoff was applied to evaluate the model on days of
elevated ozone which are more policy relevant. Overall, the mean bias (bias) and mean error (error)
statistics are within the range or close to that found by other groups in recent applications (Simon et al.,
2012; Wilson et al., 2019). The model performance results provide confidence that this application of
CAMx provides a scientifically credible approach for estimating 03 mixing ratios for the purposes of this
assessment.
15
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Figure 8. Paired observations with model predictions. A scatter plot colored by density of
observation-prediction pairs is shown by climate zone.
03 (ppb) West
03 (ppb) Northwest
0 20 40 60 80
Measures
£p3 (ppb) Northnam RocWes/Plalnes
03 {ppb} southwest
Measure#
03 (ppb) Upper Midwest
Measured
03 (ppt>> Ohio Valley
40 60
Measures
03 (ppb) South
20 40 60 80
Measures
03 (ppb) Southeast
20 40 60
Measured
Measures
03 (ppb) Northeast
SOURCE IMPACT OVERVIEW
Ammonia reacts with sulfuric acid to form aerosol and with nitric acid to form ammonium nitrate when
meteorological conditions are favorable (Seinfeld and Pandis, 2008). Ammonia has been measured in
oceanic environments (Nair and Yu, 2020) but tends to be more abundant over land due to the larger
amounts of sources. Figure 9 shows the maximum 24-hr average normalized air quality impact from
each source tracked for contribution as part of this assessment. The air quality impacts were normalized
by annual emission rate used for each hypothetical source. PM2.s nitrate ion from NOx emissions are
shown in the top row, PM2.5 sulfate ion from S02 emissions are shown in the middle row, and primarily
emitted PM2.5 in the bottom row. Impacts from sources modeled as Class 3 CMV are on the left and Class
1 and 2 CMV on the right. This comparison includes all hypothetical sources modeled as part of this
assessment. Similar information is shown in Figure 10 for annual average PM2.5and Figure 11 for annual
maximum MDA8 03.
16
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Figure 9. Maximum daily average PM2.5 normalized by annual emission rate for Class 3 (left) and
Class 1/2 (right) types of sources for hypothetical single sources modeled offshore in this
assessment. Results are shown for PM2.5 nitrate ion (top row, PM25 sulfate ion (bottom row), and
primary PM2.5 (bottom row).
o Planning Area
O Leased Area
o Planning Area
o Leased Area
17
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Figure 10. Maximum annual average PM25 normalized by annual emission rate for Class 3 (left) and
Class 1/2 (right) types of sources for hypothetical single sources modeled offshore in this
assessment. Results are shown for PM25 nitrate ion (top row, PM2.5 sulfate ion (bottom row), and
primary PM2.5 (bottom row).
o Planning Area
o Leased Area
o Planning Area
o Leased Area
18
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Figure 11, Maximum MDA8 03 normalized by annual emission rate for Class 3 (left) and Class 1/2
(right) types of sources for hypothetical single sources modeled offshore in this assessment. Results
are shown for NOx (top row) and VOC (bottom row).
o Planning Area
o Leased Area
Peak impacts of precursors for secondary pollutants tend to decrease as distance from the shore
increases which is likely due to less favorable chemical conditions (less oxidants and less ammonia) and
meteorology (higher winds). Peak primary PM2.5 showed somewhat less of a tendency for peak impacts
to decrease as distance from the shore increases, especially in the Gulf of Mexico. Variability in
precursor efficiency to form 03 is noticeable from area to area and even within some of the regions with
extensive model coverage with hypothetical sources. This is likely due to the sparse nature of chemically
reactive VOC offshore compared to over land where biogenic VOC is typically abundant, especially in the
eastern U.S. which leads to sometimes highly localized areas where VOC or NOx is limiting 03 formation.
This same spatial variability is evident for secondary PM2.5 formation but to a lesser extent.
The PM2.5 n itrate/s u If ate/am m o n i u m system can be impacted by competing emissions sources such as
sea salt. Other anions (such as sodium or calcium) already in the aerosol phase can also react with nitric
acid resulting in nitrate condensing into the particle phase. This switching would have limited (or small)
influence on aerosol mass because the water content would not be identical for N03 and CI and the
molar mass of NO? and CI is different. This type of anion substitution can also lead to the release of
19
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sequestered hydrochloric acid that could participate in atmospheric reactions and increase the
anthropogenic contribution to aerosol at the expense of geogenic (e.g., sea salt) sources.
The maximum normalized impacts published previously for leased area off the Atlantic Coast (U.S.
Environmental Protection Agency, 2022) are generally consistent with those shown here. The values
previously published tend to be between the CMV C3 and CMV C1/C2 values shown here due to the
stack parameters resulting in plume rise (surface level stack height with higher exit velocity) resulting in
more dispersive emissions than resulting from the CMV C1/C2 stack parameters used in this assessment
but not as dispersive as the CMV C3 stack parameters. Other differences are likely due to the different
year of meteorology used in each assessment and different on and offshore emissions resulting in a
somewhat different chemical environment that may at times be more or less favorable for secondary
pollutant formation.
Oceanic emissions of halogens in aerosol and gas phase forms can influence the lifetime of NOx, 03, and
some inorganic particulates. Some of these processes are included as part of the chemical mechanism
developed for the photochemical model. However, others are more novel and have not been fully
implemented. The potential impacts of some chemical and physical processes related to oceanic
emissions on 03 and PM25 model predictions follow.
A chlorine mediated nitrate photolysis pathway could allow for NOx recycling in the marine environment
(Kasibhatla et al., 2018). This process is highly uncertain but could be a source of nitrous acid (HONO)
and NOx in remote areas (Zhang et al., 2020). Both iodine and bromine chemistry can affect NOx in the
marine environment but would not be expected to have a large impact on NOx.
NOx can react in the atmosphere to form N205 (Equation 1) and be converted to nitric acid (HN03)
(Equation 2) through heterogeneous chemistry and subsequently removed from participation in 03
formation reactions (McDuffie et al., 2018).
(1) no2 + no3-> n2o5
(2) N205 + H20(p) -> 2HN03
Another heterogenous N205 hydrolysis pathway (Equation 3) can act as a reservoir for N02 overnight
and potentially result in regenerated N02 at sunrise (Equation 4) (McDuffie et al., 2018).
(3) N2O5 + H+ + CI" -> HNO3 + CINO2
(4) CINO2 + hv -> N02 + CL"
In environments without chlorine, 2 nitric acid molecules form from N205 heterogenous reactions, which
have a short atmospheric lifetime. In marine environments with chlorine emissions, N205 heterogeneous
reactions make one nitric acid and one CIN02. The CIN02 photolyzes during the daytime to form CI" and
N02. These marine environment processes can act as a nighttime NOx reservoir which would then
regenerate N02 on sunrise (McDuffie et al., 2018).
20
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However, it is important to consider that the presence of chlorine speeds up the N205 hydrolysis
reaction (Equation 3) rate (Bertram and Thornton, 2009). Therefore, it is not clear how much N02 would
convert to HN03 rather than CIN02 in the presence of chlorine containing aerosol.
Photochemical modeling that incorporates the full suite of chemical reactions would be needed to fully
understand the implications of the various reactions and processes noted in this section. However,
important chemical and physical processes are included in the modeling system and the impacts
provided here are considered a reasonable representation of 03 and PM2.5 impacts from precursors for
the intended purpose of supporting permit program related demonstrations.
PRODUCTS
PM2.5 nitrate impacts were linked to secondary formation attributed to NOx emissions only and
therefore these outputs do not include the impacts from primarily emitted PM2.5 nitrate. PM2.5 sulfate
impacts were linked to secondary formation attributed to S02 emissions only and similarly do not
include the impacts from primarily emitted PM2.5 sulfate. PM2.5 ammonium impacts were linked to
secondary formation attributed to NH3 emissions only and do not include the impacts from primarily
emitted PM2.5 ammonium.
The modeled contributions from each source were processed to match metrics relevant for permit
program related demonstrations. PM2.5 impacts were estimated as annual average and annual maximum
daily average. 03 impacts were estimated as maximum daily average of 8-hr rolling averages for April
through October. The air quality impacts were then normalized by precursor emissions as a function of
distance bins from the hypothetical source to develop transfer coefficients. These transfer coefficients
were intended to be paired with project specific emissions to develop an approximation of project
specific air quality impacts by distance. Since the modeling was done using 12 km sized grid cells, no
information is available for source impacts at distances less than the size of the grid cell.
The relationship between estimated project impacts, project emissions, and transfer coefficients is
shown in Equation 1.
Equation (1) Air quality impact = Project Emissions x Transfer Coefficient
Where the screening level air quality impact would have units ng/m3 for PM2.5 and ppb for MDA8 03
calculations. The project emissions should be expressed as tons per year. The transfer coefficients are
provided in for each precursor to secondary pollutant in Tables 4 to 15. The transfer coefficients are
expressed as (ng/m3)/tpy for PM2.5 and ppb/tpy for 03 calculations.
It is expected that the screening level air quality impacts would be compared to appropriate SIL and
increment values (U.S. Environmental Protection Agency, 2018, 2019b, 2021). These screening level air
quality impacts could also be used in combination with appropriate estimates of ambient air and nearby
sources as part of a cumulative demonstration comparison with NAAQS levels (U.S. Environmental
Protection Agency, 2018, 2019b, 2021). Offshore projects should select the values from the table for
areas representative of that particular project. If a new offshore project has a location that was not
included in this assessment the applicant should consult with the appropriate Regional Office to discuss
how this information might be used for that situation.
21
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Transfer coefficients relating precursors to annual average and annual maximum 24-hr average PM2.5
and annual maximum MDA8 03 are provided separately in a single comma-delimited text file. If this
information is not readily available, please contact the appropriate Regional office.
REFERENCES
Baker, K.R., Foley, K.M., 2011. A nonlinear regression model estimating single source concentrations of
primary and secondarily formed PM2. 5. Atmospheric Environment 45, 3758-3767.
Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated with photochemical model source
sensitivity and apportionment approaches. Atmospheric Environment 96, 266-274.
Baker, K.R., Kotchenruther, R.A., Hudman, R.C., 2016. Estimating ozone and secondary PM2. 5 impacts
from hypothetical single source emissions in the central and eastern United States. Atmospheric
Pollution Research 7, 122-133.
Baker, K.R., Woody, M.C., 2017. Assessing model characterization of single source secondary pollutant
impacts using 2013 SENEX field study measurements. Environmental science & technology 51, 3833-
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Bash, J.O., Baker, K.R., Beaver, M.R., 2016. Evaluation of improved land use and canopy representation
in BEIS v3. 61 with biogenic VOC measurements in California. Geoscientific Model Development 9, 2191.
Bertram, T., Thornton, J., 2009. Toward a general parameterization of N 2 O 5 reactivity on aqueous
particles: the competing effects of particle liquid water, nitrate and chloride. Atmos. Chem. Phys. 9,
8351-8363.
Emery, C., Baker, K., Wilson, G., Yarwood, G., 2024. Comprehensive Air Quality Model with Extensions:
Formulation and Evaluation for Ozone and Particulate Matter over the US. J Atmosphere 15.
ENVIRON International Corporation, 2012. Evaluation of Chemical Dispersion Models using Atmospheric
Plume Measurements from Field Experiments. EPA Contract No: EP-D-07-102.
https://www.epa.gov/sites/default/files/2020-10/documents/plume eval final sep 2012v5 O.pdf.
Kasibhatla, P., Sherwen, T., Evans, M.J., Carpenter, L.J., Reed, C., Alexander, B., Chen, Q., Sulprizio, M.P.,
Lee, J.D., Read, K.A., 2018. Global impact of nitrate photolysis in sea-salt aerosol on NO x, OH, and O 3 in
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