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Technical Support Document (TSD) for
Adoption of the Generic Reaction Set Method
(GRSM) as a Regulatory Non-Default Tier-3
NO2 Screening Option
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EPA-454/R-23-009
October 2023
Technical Support Document (TSD) for Adoption of the Generic Reaction Set Method (GRSM)
as a Regulatory Non-Default Tier-3 NO2 Screening Option
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Preface
This technical support document (TSD) provides a review of the GRSM NO2 option model
performance and implementation in AERMOD version 23132. The TSD presents and
summarizes GRSM model performance based on four NO2 model evaluation databases used to
determine appropriate application of NO2 screening options as part of the regulatory default
version of AERMOD. The purpose of this TSD is to support adoption of GRSM as a new
regulatory non-default Tier 3 NO2 screening option in AERMOD.
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Contents
Preface 4
Contents 5
Figures 6
Tables 7
1. Introduction 8
2. Background 8
2.1 The 3-Tiered Approach for AERMOD N02 Modeling Demonstrations 8
3. Current Regulatory Status and Features of GRSM 9
4. GRSM Implementation in AERMOD 9
5. Model Evaluation of GRSM 10
5.1 Pala'au, Hawaii N02 Database 11
5.2 Empire Abo, Artesia, New Mexico N02 Database 14
5.3 Balko, Oklahoma N02 Database 17
5.4 Denver-Julesburg Basin, Platteville, Colorado N02 Database 20
6. Summary 23
References 24
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Figures
Figure 1 - Pala'au N0X Ranked Q-Q Plot 13
Figure 2 - Pala'au N02 Ranked Q-Q Plot 13
Figure 3 - Empire Abo NOx Ranked Q-Q Plot 15
Figure 4 - Empire Abo N02 Ranked Q-Q Plot for the North Monitor 16
Figure 5 - Empire Abo N02 Ranked Q-Q Plot for the South Monitor 16
Figure 6 - Balko NOx and N02 Ranked Q-Q Plot for all monitors 18
Figure 7 - Balko NOx and N02 Ranked Q-Q Plot by monitor 19
Figure 8 - Colorado NOx and N02 Ranked Q-Q Plots for Pad 1 22
Figure 9 - Colorado NOx and N02 Ranked Q-Q Plots for Pad 2 22
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Tables
Table 1 - Pala'au Model Performance Statistics Summary (ng/m3) 14
Table 2 - Empire Abo Model Performance Statistics Summary (ng/m3) 17
Table 3 - Balko Model Performance Statistics Summary (ng/m3) 20
Table 4 - Colorado Model Performance Statistics Summary (ng/m3) 23
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1. Introduction
The proposed revisions to Appendix W to CFR 40 Part 51Guideline on Air Quality Models
(Guideline), includes a new version of AERMOD (23132)1. This new version of AERMOD includes
a proposed regulatory non-default Tier 3 NO2 screening option, i.e., the Generic Set Reaction
Method (GRSM; (Carruthers, Stocker, Ellis, Seaton, & Smith, 2017); (Stocker, et al., 2023)). This
TSD reviews the scientific merit, implementation of the GRSM formulation, and summarizes
selected model evaluations to support the application of GRSM as a Tier 3 NO2 screening option
for use as part of the proposed regulatory version of AERMOD.
2. Background
The chemistry, regulatory status, and performance evaluations of all existing AERMOD NO2
screening options are discussed in the U.S. EPA TSD for N02-Related AERMOD Options and
Modifications (U.S. EPA, 2015, December). This TSD will discuss the chemistry, proposed
regulatory status, and model behavior and performance of GRSM. Following the 2015 TSD,
selected graphical and statistical (U.S. EPA, 1992) comparisons between GRSM and other NO2
regulatory options are presented.
2.1 The 3-Tiered Approach for AERMOD NO2 Modeling Demonstrations
Section 4.2.3.4 of Appendix W details a 3-tiered approach for evaluating the modeled impacts
of NOx emission sources. These tiers assume increasing levels of conservatism (i.e.,
conservation of air quality as a resource for protecting public health) in the assessment of
hourly and annual average NO2 impacts from point, volume, and area sources for the purposes
of supporting the PSD program, SIP planning, and transportation general conformity. The
3-tiered approach addresses the co-emissions of NO and NO2 and the subsequent conversion of
NO to NO2 in the atmosphere. The tiered levels include:
Tier 1 - assuming that all emitted NO is converted to NO2 (full conversion),
Tier 2 - using the Ambient Ratio Method 2 (ARM2), which applies an assumed
equilibrium ratio of NO2 to NOx, based on analysis of and correlation with nationwide
hourly observed ambient conditions (Podrez, 2015), and
Tier 3 - applying the Ozone Limiting Method (OLM; (Cole & Summerhays, 1979)) and
Plume Volume Molar Ratio (PVMRM; (Hanrahan, P.L., 1999a and 1999b)) screening
options based on site-specific hourly ozone data and source-specific NO2 to NOx in-stack
ratios.
As discussed in section 4.2.3.4(e), regulatory application of Tier 3 screening options shall occur
in consultation with the EPA Regional Office and appropriate reviewing authority.
1 For more information on the proposed revisions to Appendix W and updates to AERMOD, please reference:
https://www.epa.gov/scram/2023-appendix-w-proposed-ruje
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3. Current Regulatory Status and Features of GRSM
As part of the 2023 proposed revisions to the Guideline, the EPA is proposing to include the
GRSM as a regulatory non-default Tier 3 NO2 screening option in AERMOD version 23132.
Following peer-reviewed publication (Carruthers, Stocker, Ellis, Seaton, & Smith, 2017), GRSM
was added to AERMOD as an alpha option in version 21112 and later updated to a beta option
in version 22112. The GRSM option is proposed to be adopted as a beta option in AERMOD
version 23132, and later advanced to a full regulatory NO2 screening option upon release of the
2024 version of AERMOD.
The primary motivation behind the formulation and development of the GRSM NO2 screening
option is to address photolytic conversion of NO2 to NO and to address the time-of-travel
necessary for NOx plumes to disperse and convert the NO portion of the plume to NO2 via
titration and entrainment of ambient ozone. The current regulatory non-default Tier 3 NO2
screening options, PVMRM and OLM, do not address or provide for treatment of these
photolysis and time-of-travel mechanisms, and have been shown to over-predict for some
source characterizations and model configurations at project source ambient air boundaries
and within the first 1-3 km. (Stocker, et al., 2023) and as presented in this TSD.
4. GRSM Implementation in AERMOD
The functionality of the GRSM code implementation in AERMOD is similar to that of the
PVMRM and OLM schemes, with exception to some additional input requirements necessary
for treatment of the reverse NO2 photolysis reaction during daytime hours. Modeled source
inputs for GRSM require NO2/NOX in-stack ratios, with similar assumptions as applied to
PVMRM and OLM pursuant to section 4.2.3.4 of the Guideline. Ambient model inputs for GRSM
require hourly ozone concentrations taken from an appropriately representative monitoring
station or selection of monitoring stations for varying upwind sector concentrations. GRSM also
requires hourly NOx concentration inputs to resolve the daytime photolysis of NO2 reaction in
equilibrium with ozone titration conversion of the NO portion of the NOx plume. Hourly NOx
and NO2 concentrations input to AERMOD when using the GRSM method can also vary by
upwind sector concentration, as appropriate. Background NO2 concentrations are accounted
for in the GRSM daytime equilibrium NO2 concentration estimates based on the chemical
reaction balance between ozone entrainment and NO titration, photolysis of NO2 to NO, and
ambient background NO2 participation in titration and photolysis reactions. Nighttime NO2
estimates from GRSM are based on ozone entrainment and titration of available NO in the NOx
plume, and by default, AERMOD sets nighttime ozone concentrations to 40 parts per billion
(ppb) unless the NOMIN03 model option is specified. Note that all hourly ozone and NOx
ambient inputs to GRSM must coincide with the hourly meteorological data records for the
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period of the modeling analysis (i.e., minimum of 1-year for on-site data, 3 years of prognostic
data, and 5 years of airport data (i.e., meteorological data collected by either the National
Weather Service (NWS) or the Federal Aviation Administration (FAA), typically at airport
locations).
Updates to the GRSM formulation in AERMOD version 22112 were completed in late 2022 to
address more realistic building effects on instantaneous plume spread, accounting for multiple
plume effects on entrainment of ozone, and the tendency of GRSM to over-predict in the far-
field (e.g., beyond approximately 3 km for typical point source releases). Sensitivity testing and
model performance evaluations of these updates to GRSM in AERMOD version 23132 have
shown consistent or improved model behavior and performance. The model performance
evaluations are presented and discussed in the following section.
5. Model Evaluation of GRSM
Statistical evaluation of GRSM NO2 model performance was conducted based on four source-
oriented ambient ozone and NO2 monitoring databases assuming rural dispersion conditions.
Two legacy (1993) databases reflect the ARM2, OLM, and PVMRM evaluations presented in the
2015 TSD for NO2 modeling options (U.S. EPA, 2015, December). These legacy databases
included 1-year datasets developed for a power plant located on the island of Moloka'i, Pala'au,
Hawaii, and for a gas processing plant located in Artesia, New Mexico (Empire Abo). Details of
the Pala'au and Empire Abo databases are discussed at length in a 2013 technical report (RTP
Environmental Associates, Inc., 2013). The other two evaluation databases were developed
more recently in the 2014-2016 time period and include a 1-year field study at a gas
compressor station facility located near Balko, Oklahoma (Balko), and a six-week field study at
an oil and gas drill rig installation located in the Denver-Julesburg Basin near Platteville,
Colorado. The Balko database included ozone and NO2 data collected at four monitoring
stations from December 2015 through December 2016. Details on the development of the
Balko database were published in March of 2020 (Panek, 2020) along with model observation
comparisons with ARM2, PVMRM, and OLM. The Colorado database included data collected at
a total of 12 monitoring locations upwind and downwind of two oil and gas well drilling pads for
a five-week period October 10th through November 16th, 2014. Further details on the Colorado
database are available at EPA's SCRAM website and documented in a separate TSD (Colorado
Field Study Workgroup: ERM, 2020).2 All four model evaluation databases included site-specific
meteorological data collected at the site, re-processed with AERMET version 23132.
Summaries of all databases and performance model evaluations are also discussed in a
technical report authored by the developers of GRSM for AERMOD (Stocker, et al., 2023).
2 Denver-Julesburg Bason, Colorado N02 Evaluation Database:
https://gaftp.epa,gov/Air/aqme/SCRAM/models/preferred/aermod/eval databases/denver-iulesburg.zip
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As discussed previously in Section 4.3 of (RTP Environmental Associates, Inc., 2013), conversion
of NO2 and NOx measurements in ppb to micrograms per cubic meter (|ag/m3) requires careful
consideration of the actual and standard meteorological conditions as well as the separate
contributing constituent components of the NOx plume in terms of NO and NO2 ppb by volume.
Conversion of ppb to |-ig/m3 from actual to standard temperature and pressure conditions
generally increases measurement concentrations by approximately 10% depending on season,
climatology, and elevation. Additionally, this conversion, typically based on the conventional
assumption that all NOx is NO2 with a molecular weight of 46 grams/mole, would increase the
l-ig/m3 measurement estimates by 20-30% for some shorter source-receptor distances,
especially between 10's to 100's of meters and possibly as far away as approximately 3 km
depending on dispersion conditions. The "true" NOx plume at these shorter distances is
composed of mostly NO (e.g., 50-95% NO/NOx by volume) and therefore, would contain less
mass given the 30 g/mole molecular weight of NO, which accounts for the 20-30% conservative
estimates of NOx as NO2 emission inputs typically used in AERMOD NO2 demonstrations. As
such, and based on the most current information on the four field datasets considered in this
TSD, the NOx as NO2 assumption was applied to all NOx emissions inputs, thereby introducing a
conservative bias in the modeled mass emission rates. From a regulatory perspective, the
performance of this conservative NOx as NO2 emissions assumption when compared to actual
measured NOx concentrations was considered because a regulatory modeling result would
need to show some level of performance as it pertains to the Appendix W requirement that the
model does not show bias to underpredict. Therefore, all input emissions assume NOx as NO2
(based on most current understandings of emission factors applied), and any dispersion
performance indicated from NOx modeled compared to NOx measured assumes no change to
the modeled |-ig/m3 concentrations whereas measured NOx represents the actual |-ig/m3
concentrations (at standard temperature and pressure; STP) as would be the case for any
regulatory modeling or monitoring demonstration. Note that AERMOD NOx and NO2
concentrations in |-ig/m3 are calculated internally based on standard temperature and pressure
(i.e., 298.15 K and 1013.25 mb). Chemistry performance was assessed in terms of modeled
l-ig/m3 NO2 at STP compared to measured |-ig/m3 NO2 (after conversion from ppb to |-ig/m3 at
STP).
5.1 Pala'au, Hawaii NO2 Database
The Pala'au hourly NO2 and ozone data were collected at a monitoring station located
approximately 220 meters northwest of the facility. Hourly varying ozone data was developed
from the on-site monitoring data (93% complete). The annual ozone substitution value was set
to the default 40 ppb for all NO2 models. A single annual NOx value was set to 2.5994 |-ig/m3 for
the model simulations using GRSM. Background NO2 assumed 0.69838 ppb for all conversion
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methods. NOx emissions were assumed to be non-varying for the entire 1993 study period, and
included six diesel engines and one combustion turbine with emission rates ranging from an
average of 12.6 Ib/hr to a maximum of 27 Ib/hr; total NOx emissions of 88.3 Ib/hr. All sources
assumed NC^/NOx in-stack ratios of 10%. Stack heights at the power plant were relatively
short, and range from 24-32 feet and were modeled assuming flat terrain. All NO2 model
outputs were based on 1-hour averages as predicted at the single monitor receptor location.
AERMOD performs well at Pala'au as indicated in the Q-Q ranked plot shown in Figure 1 where
the modeled versus observed NOx concentrations track the 1:1 line throughout the ranked
distribution. As previously discussed, modeled mass emission rates assumed all NOx was NO2,
thus, introducing a conservative emission estimate bias that could be influencing the
agreement between observed and modeled NOx concentrations. Another emissions
uncertainty for Pala'au, which could inadvertently bias model-observation agreement during
non-operating periods, is the non-varying emission rates assumed for the 1-year evaluation
period. However, given the proximity of the monitoring station located 220 meters northwest
of the power plant, and the relatively consistent distribution of the NOx concentrations
throughout the monitoring period, altogether, these factors would indicate that the power
plant operated continuously at a normal demand load for the entire year. Note that no filtering
of the NOx observations was conducted (e.g., by downwind sector) to determine the final set of
7,856 model-observation pairings shown in Figure 1.
Figure 2 shows a ranked Q-Q plot of modeled versus observed NO2 concentrations for modeling
scenarios that use the proposed GRSM NO2 Tier 3 option as well as the other Tier 2 (ARM2) and
3 (OLM, PVMRM) regulatory NO2 options available in AERMOD. The ARM2 option performs as
intended the most conservatively, whereas OLM becomes the less conservative option by
comparison. PVMRM shows some slight underprediction whereas GRSM maintains a slightly
conservative performance trend just above the 1:1 line for most of the ranked distribution.
GRSM performs consistently compared to the other AERMOD NO2 options, and shows no
unacceptable bias to underpredict peak concentrations for the Pala'au database.
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1993 Palaau Database
All Wind Directions
Figure 1 - Pala'au NOx Ranked Q-Q Plot
1993 Palaau Database
All Wind Directions
Figure 2 - Pala'au N02 Ranked Q-Q Plot
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Table 1 shows a summary of fractional bias (FB) and robust highest concentration (RHC) model
performance statistics for the NOx and NO2 model option scenarios evaluated for Pala'au. The
FB shows decreasing conservative agreement between observations and model outputs for
NOx, ARM2, OLM, PVMRM, and GRSM model options; note that negative FB indicates a
conservative bias for the option, or overprediction. The RHC ratio and RHC FB results show
similar conservative hierarchy across the NO2 option evaluations, with increasing conservatism
shown for GRSM, PVMRM, OLM, ARM2, and full conversion NOx runs.
Table 1 - Pala'au Model Performance Statistics Summary (pg/im3)
Model Opt.
FB
RHC_Obs
RHC_Mod
RHC_ratio
RHC_FB
NOx
-1.01763
456.5125
471.9136
1.033737
-0.03318
ARM2
-1.22483
90.9536
237.2565
2.608545
-0.89152
OLM
-1.00706
90.9536
103.8257
1.141523
-0.13217
PVMRM
-0.79509
90.9536
98.17854
1.079436
-0.0764
GRSM
-0.90174
90.9536
82.87393
0.911167
0.092962
5.2 Empire Abo, Artesia, New Mexico NO2 Database
The Empire Abo hourly NO2, ozone, and meteorological data were collected at two monitoring
stations located approximately 1.6 km northeast (north station) and 2.4 km southeast of the
facility from June 11, 1993 through June 10, 1994. Model inputs for hourly ozone and
background NO2 and NOx were based on two wind sectors starting at 100 and 280 degrees,
which AERMOD interprets as downwind or flow vector wind directions. The first sector (winds
blowing towards 100-280 degrees) used upwind hourly ozone and NO2 concentrations from the
north station, while the second sector (winds blowing towards 280-100) used upwind data from
the south station. Substitution values for missing hourly ozone and NOx data were taken from
season-hourly varying maximum, while NO2 season-hourly values were developed from highest-
3rd-high observed values. The highest-3rd-high was selected for NO2 substitution values in order
to reflect a median value between unreasonably high maximum NO2 values and the 1-hour
NAAQS highest-8th-high. Similar to Pala'au, NOx emissions for Empire Abo were assumed to be
non-varying for the entire study period, and included 21 combustion sources with emission
rates ranging from an average of 20.2 Ib/hr to a maximum of 69.4 Ib/hr, and with a facility total
of 423 Ib/hr. All sources assumed NO2/NOX in-stack ratios of 20%. Stack heights at the power
plant from dominant sources averaged about 30 feet and all sources were modeled assuming
flat terrain. All NOx and NO2 model outputs were based on 1-hour averages as predicted at the
north and south monitor receptor locations.
As shown in the ranked Q-Q plot in Figure 3, modeled NOx concentrations at the north and
south monitors tend to overpredict; note the north monitor shows some underprediction for
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the lower half of the ranked distribution. As previously mentioned, the overpredictions may be
in part due to the NOx mass emission rates that assume all NOx has converted to the mass of
NO2. Given the 1.6 km and 2.4 km distances to the north and south monitors, respectively, this
assumption may be valid for most worst-case scenarios; however, the ambient monitoring data
at these stations indicates the inner quartile range of the ambient NCh/NOx ratios varies
between 66-86%. The non-varying hourly emissions from Empire Abo dispersed over these
longer distances may also play a role in overestimating NOx concentrations. Note that pre-
filtering of the NOx observations was not conducted (e.g., by downwind sector, or other
parameter) to determine the final set of 16,547 model-observation pairings shown in Figure 3.
Figures 4 and 5 show ranked Q-Q plots of modeled versus observed NO2 concentrations at the
north and south monitors, respectively, for modeling scenarios that use the proposed GRSM
NO2 Tier 3 option as well as the other Tier 2 (ARM2) and 3 (OLM, PVMRM) regulatory NO2
screening options available in AERMOD. Similar to the results at Pala'au, the ARM2 option
performs the most conservatively, whereas OLM and GRSM modeled concentrations track
closely together and are more conservative than PVMRM. GRSM performs consistently
compared to the other AERMOD NO2 options, and shows no unacceptable bias to underpredict
peak concentrations for the Empire Abo database.
June 1993 - June 1994 Empire Abo Database
North (1.6km) and South (2.4km) Monitors
Figure 3 - Empire Abo NOx Ranked Q-Q Plot
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June 1993 - June 1994 Empire Abo Database
North Monitoring Station (1.6km)
N02_Option
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N02 Observed (ug/m3)
Figure 4 - Empire Abo N02 Ranked Q-Q Plot for the North Monitor
June 1993 - June 1994 Empire Abo Database
South Monitoring Station (2.4km)
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Table 2 shows summary model performance statistics for NOx and NO2 at the Empire Abo north
and south monitors. The FB shows conservative agreement between observations and model
outputs. The RHC ratio and RHC FB show conservative bias for the north monitor and south
monitor, with the most conservatism shown for NOx and ARM2, whereas OLM, PVMRM, and
GRSM display consistent conservative bias for the north and south monitors.
Table 2 - Em pdei Performance Statistics Summary (pg/im3)
Station
Model Opt.
N
FB
RHC_Obs
RHC_Mod
RHC_ratio
RHC_FB
North
(1.6km)
NOx
8418
0.519675
356.2944
477.27
1.339538
-0.29026
ARM2
8418
0.289203
130.2695
204.0459
1.566337
-0.44136
OLM
8418
0.292575
130.2695
152.6254
1.171613
-0.15805
PVMRM
8418
0.354584
130.2695
141.475
1.086018
-0.08247
GRSM
8418
0.314298
130.2695
152.9922
1.174428
-0.16044
South
(2.4km)
NOx
8129
0.364385
323.4253
392.8127
1.214539
-0.19375
ARM2
8129
-0.00239
72.57853
172.0086
2.369966
-0.81304
OLM
8129
0.000409
72.57853
128.2316
1.766798
-0.55429
PVMRM
8129
0.045474
72.57853
121.1714
1.669522
-0.5016
GRSM
8129
0.01555
72.57853
129.4047
1.782962
-0.56268
5.3 Balko, Oklahoma NO2 Database
The 1-year of hourly NO2 observations records for each of the four monitoring stations were
reduced by excluding values collected during hours when NOx concentrations were below 20
ppb and when the downwind direction from the source to the monitoring receptor location was
more than approximately 20-30 degrees (i.e., assuming a 40-60-degree downwind sector of
influence). As such, non-missing hourly modeling results were paired in time with the reduced
observations (total N pairs = 1742) to generate ranked Q-Q plots and summary statistics. In
brief, the monitoring stations, distances, and downwind directions from the sources were:
Field 425 m, 360 deg; North Fence (NF) 140 m, 360 deg; East Fence (EF) 101 m, 68 deg; and
Tower 66 m, 246 deg. NOx emissions at Balko were dominated by two of the large 2-stroke
cycle lean-burn natural gas-fired engines with combined maximum hourly NOx emission rates
on the order of 120 Ib/hr and NO2/NOX in-stack ratios of 10%. Relatively short stacks for these
units were modeled at 10 m and 20 m, with adjacent dominant building heights at 11-13 m. For
further details on the Balko field study and model configurations see (Panek, 2020).
The NOx and NO2 Q-Q plots shown in Figure 6 represent all model-observation data pairs from
the four monitoring stations. Both NOx, OLM, and GRSM NO2 predictions trend below the 1:1
line at the upper end of the concentration distributions, but above the 1:2 line. ARM2 performs
above the 2:1 line through the first half of the ranked distribution, converging to the 1:1 line at
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the upper end of the distribution. The negative bias shown in the NOx performance suggests
dispersion assumptions such as downwash and stack parameters could be refined for some
monitoring locations. The negative bias in NOx is largely driven by the model performance and
higher observed NOx impacts at the North Field (NF) monitor, which is well within the near
wake zone of two adjacent end-to-end, long buildings. As shown in Figure 7, all other monitor
locations show more conservative, or positive bias for NOx.
The ranked Q-Q plot panels for NOx and NO2 shown in Figure 7 are presented for each monitor
location. NOx model predictions at the Field and East Fence show conservative bias with peak
value data pairs falling between the 2:1 and 1:1 lines. NO2 model predictions at these stations
ranging from 175 |-ig/m3 to 200 |-ig/m3 show a consistent conservative hierarchy across the NO2
options decreasing in order of the ARM2, OLM, PVMRM, and GRSM options. The NOx
predictions at the meteorological Tower monitor fall mostly along the 1:1 line; however, PVRM
is the least conservative performing NO2 option at this predominantly southwesterly, upwind
location. NOx predictions at the North Fence follow the 1:1 line with negative bias trends
starting at 750 |-ig/m3 and ending above the 1:2 line at about 1600 |-ig/m3. The negative bias at
the upper part of the distribution is most likely influenced by uncertainties in source and
building downwash characterizations at what is a relative short downwind 140-meter distance
from the dominant stack. The conservative hierarchy shown for NO2 predictions at the North
Fence is similar to the other monitor locations; however, the overly conservative PVMRM
predictions for the last three data pairs suggest further uncertainties in instantaneous plume
and building downwash formulations coded for PVMRM. GRSM does not mimic this behavior.
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o 200-
CM I
>
I
g 100
a:
LU
<
0-,
NF
N =,$?76 /
chem
/ 0 y'
/
0 ARM2
OLMGRPALL
PVMRM
A GRSM
100 200 300
N02_ugm3
chem
o ARM2
OLMGRPALL
PVMRM
A GRSM
50 100 150 200
N02_ugm3
chem
o ARM2
OLMGRPALL
PVMRM
A GRSM
50 100 150
N02_ugm3
Figure 7 - Balko NOx and N02 Ranked Q-Q Plot by monitor
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Table 3 provides FB and RHC statistics calculated for all monitor locations and for all NO2 model
options evaluated including NOx, ARM2, OLM, PVMRM, and GRSM. The table is sorted by
model option and decreasing RHC ratio values. In all, the GRSM RHC ratio and RHC FB results
indicate more consistent, logical model behavior when compared with modeled NOx
performance at the four monitors. With exception to the uncertainties discussed at the NF
monitor, GRSM performance statistics show less conservative bias than the other NO2 model
options.
Table 3 - Balko Model Performance Statistics Summary fpg/im3)
Station
Model Opt.
N
FB
RHC_Obs
RHC_Mod
RHC_ratio
RHC_FB
EF
NOx
232
-0.30893
785.1347
1449.214
1.845816
-0.59443
Field
NOx
567
-0.33405
481.5579
637.3523
1.323521
-0.27848
Tower
NOx
149
0.050238
825.8741
719.8355
0.871604
0.137204
ALL
NOx
1742
-0.13172
1884.88
1162.402
0.616698
0.474179
NF
NOx
794
0.030388
1884.88
1069.941
0.567644
0.551599
EF
ARM2
232
-0.5797
106.2721
210.4881
1.980653
-0.65801
Tower
ARM2
149
-0.23291
121.7104
211.9201
1.741183
-0.54078
Field
ARM2
567
-0.67069
104.2684
171.3581
1.643433
-0.48682
ALL
ARM2
1742
-0.44312
220.7673
199.6457
0.904326
0.100481
NF
ARM2
794
-0.28016
220.7673
191.5234
0.867535
0.141861
EF
OLMGRPALL
232
-0.51368
106.2721
164.5996
1.548851
-0.43067
Field
OLMGRPALL
567
-0.59014
104.2684
130.0644
1.2474
-0.22017
Tower
OLMGRPALL
149
-0.16588
121.7104
137.5235
1.129924
-0.122
ALL
OLMGRPALL
1742
-0.34007
220.7673
172.0811
0.779468
0.247863
NF
OLMGRPALL
794
-0.14346
220.7673
169.5039
0.767794
0.262707
EF
PVMRM
232
-0.14628
106.2721
192.0816
1.807451
-0.57522
NF
PVMRM
794
0.17654
220.7673
269.9143
1.222619
-0.20032
ALL
PVMRM
1742
0.048435
220.7673
259.2444
1.174288
-0.16032
Field
PVMRM
567
-0.11716
104.2684
110.9297
1.063887
-0.06191
Tower
PVMRM
149
0.299099
121.7104
92.84917
0.76287
0.269028
EF
GRSM
232
-0.11145
106.2721
147.6259
1.389132
-0.32575
Field
GRSM
567
-0.26831
104.2684
105.8487
1.015156
-0.01504
Tower
GRSM
149
0.217114
121.7104
104.3644
0.857481
0.153454
ALL
GRSM
1742
0.034885
220.7673
142.8789
0.647192
0.428375
NF
GRSM
794
0.259959
220.7673
138.3798
0.626813
0.458796
5.4 Denver-Julesburg Basin, Platteville, Colorado NO2 Database
The Colorado NO2 database is comprised of twelve monitors deployed for roughly six weeks
(October 10 - November 16, 2014) at two adjacent oil and gas drilling installations, Pads 1 and
-------
2 outside Platteville, CO. At Pad 1, the six upwind (southeast) and six downwind (northwest)
monitors were positioned around the pad on which the emission sources included a drill rig,
five generators, and one small boiler. Similarly, at Pad 2, six monitors upwind and six
downwind were located around the pad with the same emission sources. The monitors were
re-positioned at Pad 2 on three separate occasions to capture NOx emissions during changing
wind patterns for the last three weeks of the monitoring period. Monitor locations for both
Pad 1 and 2 were placed approximately 50-100 m away from the drill rig and support
generators. Hourly varying NOx emissions were modeled for the five diesel-fired support
generators and standby boiler at Pads 1 and 2 during the six-week study period. NOx hourly
emission rate totals at both pads range from roughly 10 Ib/hr to 20 Ib/hr with stack release
heights at 18 ft just at or above the 18 ft high drill rig and 30 total adjacent and nearby
buildings. Non-missing hourly modeling results were paired in time with the available
observations at Pads 1 and 2 (total N pairs = 1473) to generate ranked Q-Q plots and summary
statistics. All five generators show relatively equal contribution to total NOx emissions when
operating at Pads 1 and 2. The standby boiler contributions to total NOx emissions appear to be
negligible. The generator operating loads varied between approximately 50-100% load
throughout the study periods at Pad 1 and 2. For further details on the Colorado field study
monitor locations, hourly emission rates and operating scenarios, background hourly ozone and
NOx data wind sector filtering, and other model configurations see (Colorado Field Study
Workgroup: ERM, 2020). Note that there were more than a dozen small adjacent buildings
located within downwash near wake zones that extend between source and monitor locations
at Pads 1 and 2. Downwash effects from these collections of buildings as well as non-varying in-
stack NO2/NOX ratios assumed for the five generators likely influence model performance
biases and uncertainties for this database.
Figures 8 and 9 show ranked Q-Q plots of AERMOD versus observed NOx and NO2
concentrations at Pads 1 and 2, respectively. NOx model predictions at Pad 1 compare well
with observations with exception to the last three data pairs showing some underprediction.
At Pad 2, roughly half of the upper distribution of NOx predictions show negative bias trending
toward the 1:2 line, suggesting AERMOD may be overestimating dispersion; however, NOx
emissions and/or NO2/NOX ratios inputs may be underestimated perhaps related to
uncertainties in the assumed non-varying in-stack NO2/NOX ratios during varying genset
operating loads. Uncertainties in source characterization of building downwash may also be
contributing to the model estimates at both Pads 1 and 2, especially attributable to movement
of monitoring equipment at three different times around Pad 2. The conservative bias
hierarchy for NO2 options at Pads 1 and 2 is similar to NO2 option performance discussed for
the other three databases with exception to PVMRM. The unreasonable conservative bias
shown for PVMRM at Pads 1 and 2 as it compares to ARM2 may be related to similar model
-------
uncertainties discussed for the North Fence monitor at Balko, where enhanced downwash and
entrainment effects on the ensemble plume are overestimated by PVMRM in the immediate
vicinity of recirculation cavities and near wake downwash zones.
NOX Observed (ug/m3) N02 Observed (ug/m3)
Figure 8 - Colorado NOx and M02 Ranked Q-Q Plots for Pad 1
CO
g 1000
O)
D
CNJ
CO
750
500
CO
CN
>
O 250
a:
LU
<
o
Pad2
N =/753 /
/
/
/
/ +
/
/
-
/ /
/ /
' /
chem
NOx
0 250 500 750 1000
NOX Observed (ug/m3)
o>
d
CO
CO
CNJ
>
D
O
2
DC
IXJ
<
300-
200-
100-
0-
Pad2
N =/^012 /
' /
' /L
' A
/
/
/ A
/ M
tJi ^
/
chem
¦ ARM2
OLMGRPALL
± PVMRM
~ GRSM
0 100 200 300
N02 Observed (ug/m3)
Figure 9 - Colorado NOx and N02 Ranked Q-Q Plots for Pad 2
Table 4 provides summary FB and RHC statistics for all NO2 modeled options at Pads 1, 2, and
both Pads 1 and 2, sorted by model option and conservative RHC ratio. GRSM shows refined
performance consistent with the ARM2 and OLM, with underpredictions most likely
attributable to source characterization uncertainties. No extreme underprediction or
overprediction is indicated in the RHC fractional bias values shown for GRSM at Pads 1 or 2. In
general, performance for all NO2 options seems more degraded at Pad 2 as compared to Pad 1.
-------
Table 4- Colorado Model Performance Statistics Summary (pg/im3)
Pad
Model Opt.
N
FB
RHC_Obs
RHC_Mod
RHC_ratio
RHC_FB
Padl
NOx
720
0.315616
840.7064
734.6718
0.873874
0.134615
ALL
NOx
1473
0.405245
1250.35
731.5125
0.585046
0.523586
Pad2
NOx
753
0.490945
1289.554
743.2198
0.576338
0.537526
Padl
ARM2
720
-0.02909
196.3466
185.0584
0.942509
0.059192
Pad2
ARM2
753
0.181117
388.8654
253.4506
0.65177
0.421645
ALL
ARM2
1473
0.07837
388.8343
247.755
0.637174
0.443235
Padl
PVMRM
720
0.423036
196.3466
216.9619
1.104995
-0.09976
ALL
PVMRM
1473
0.438573
388.8343
337.1309
0.86703
0.142441
Pad2
PVMRM
753
0.45343
388.8654
335.9631
0.863957
0.145972
Padl
OLMGRPALL
720
0.081058
196.3466
119.3968
0.608092
0.48742
Pad2
OLMGRPALL
753
0.294795
388.8654
188.1758
0.48391
0.695581
ALL
OLMGRPALL
1473
0.19032
388.8343
184.8962
0.475514
0.71092
Padl
GRSM
720
0.525857
196.3466
121.2005
0.617279
0.473291
Pad2
GRSM
753
0.723
388.8654
180.1383
0.463241
0.733658
ALL
GRSM
1473
0.626637
388.8343
157.2032
0.404293
0.848408
6. Summary
Four NO2 model evaluation databases were used to assess the comparative model behavior
and statistical performance of GRSM. These databases represent a broad range of NOx source
characterizations, ozone and NOx model inputs, ozone and NO2 monitoring networks, and local
and regional NOx chemistry and meteorological environments. All database evaluations
included comparisons between GRSM and all existing AERMOD Tier 1 (NOx), Tier 2 (ARM2), and
Tier 3 (OLM and PVMRM) AERMOD NO2 regulatory screening options. Based on the ranked
Q-Q plots showing NOx and NO2 model versus observation concentrations, and with exception
to previously discussed uncertainties and degraded model performance at Pad 2 for Colorado,
GRSM performs within a factor of +/- 2 range of the NO2 observations, and thus, demonstrates
no unacceptable under or over prediction biases. The statistical summaries of RHC and
fractional biases for all NO2 databases further demonstrate GRSM behaves and performs
consistently in comparison with the other existing AERMOD NO2 screening options.
-------
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-23-009
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Agency Research Triangle Park, NC
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