* O \ KWJ *1 PRO^^ Technical Support Document (TSD) for Adoption of the Generic Reaction Set Method (GRSM) as a Regulatory Non-Default Tier-3 N02 Screening Option 1 ------- 2 ------- EPA-454/R-24-005 November 2024 Technical Support Document (TSD) for Adoption of the Generic Reaction Set Method (GRSM) as a Regulatory Non-Default Tier-3 N02 Screening Option U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 3 ------- Preface This technical support document (TSD) provides a review of the Generic Reaction Set Method (GRSM) NO2 option implementation and model performance in AERMOD version 23132. The purpose of this TSD is to support adoption of GRSM as a new regulatory non-default Tier 3 NO2 screening option in AERMOD. 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. This version of the TSD revises the previous October 2023 version (EPA-454/R-23-005) to include supplemental performance and sensitivity modeling evaluations in a new Appendix A. These supplemental evaluations assess and compare refinements made to GRSM in AERMOD version 23132 that were not included in the GRSM code previously implemented in AERMOD version 22112. The refinements made to the GRSM code in AERMOD version 23132 include improvements to model behavior and performance owing to more realistic treatment of building effects on instantaneous plume spread and ozone entrapment, better accounting of multiple plume effects, and mitigating GRSM over-predictions in the far-field (e.g., beyond approximately 0.5 to 3 km for typical point source releases) while enhancing concentrations in the near-wake and far-wake building downwash zones (e.g., 10's to 100's of meters downwind) for some stack and building configurations. All source code related to the formulation of the GRSM option in version 23132 has been preserved and is unchanged in version 24142. The requirement to include the BETA model option flag when using the GRSM option in version 23132 has been removed in version 24142. 4 ------- Contents Preface 4 Contents 5 Figures 6 Appendix 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 Appendix A - GRSM Code Updates and Testing for AERMOD v23132 25 A.l Model Performance Evaluation Results Summary 25 A.2 Sensitivity Modeling Results Comparison 32 5 ------- 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 Appendix Figures Figure A.l-1 - Pala'au Monitor Scatter Plot and Ranked Q-Q Plot 26 Figure A.l-2 - Empire Abo - North Monitor Scatter Plot and Ranked Q-Q Plot 27 Figure A.l-3 - Empire Abo - South Monitor Scatter Plot and Ranked Q-Q Plot 27 Figure A.l-4 - Balko - Field (north) Monitor Scatter Plot and Ranked Q-Q Plot 28 Figure A.l-5 - Balko - North Fence Monitor Scatter Plot and Ranked Q-Q Plot 28 Figure A.l-6 - Balko - East Fence Monitor Scatter Plot and Ranked Q-Q Plot 29 Figure A.l-7 - Balko - Tower (southeast) Monitor Scatter Plot and Ranked Q-Q Plot 29 Figure A.l-8 - Colorado - Pad 1 Monitors Scatter Plot and Ranked Q-Q Plot 30 Figure A.l-9 - Colorado - Pad 2 Monitors Scatter Plot and Ranked Q-Q Plot 30 Figure A.2-1 - Sensitivity modeling stack and complex building configuration 33 Figure A.2-2 - GRSM v23132 35-meter Tall Stacks Highest-8th-High 1-hour N02 36 Figure A.2-3 - GRSM v22112 35-meter Tall Stacks Highest-8th-High 1-hour N02 36 Figure A.2-4 - GRSM v23132 Minus v22112 35-meter Tall Stacks Highest-8th-High 1-hour N02 37 Figure A.2-5 - GRSM v23132 50-meter Tall Stacks Highest-8th-High 1-hour N02 37 Figure A.2-6 - GRSM v22112 50-meter Tall Stacks Highest-8th-High 1-hour N02 38 Figure A.2-7 - GRSM v23132 Minus v22112 50-meter Tall Stacks Highest-8th-High 1-hour N02 38 Figure A.2-8 - GRSM v23132 65-meter Tall Stacks Highest-8th-High 1-hour N02 39 Figure A.2-9 - GRSM v22112 65-meter Tall Stacks Highest-8th-High 1-hour N02 39 Figure A.2-10 - GRSM v23132 Minus v22112 65-meter Tall Stacks Highest-8th-High 1-hour N02 40 6 ------- 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 Table A.1.1 - GRSM v23132 and v22112 Model Performance Statistics Summary (ng/m3) 31 7 ------- 1. Introduction The proposed revisions to Appendix W to CFR 40 Part 51—Guideline 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 Technical Support Document (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 (2017) 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-rule 8 ------- 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 9 ------- 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 near- field (e.g., beyond approximately 0.5 km to 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. Please see Appendix A for model evaluation and sensitivity testing results comparisons between GRSM code implementations in AERMOD versions 22112 and 23132. The model performance evaluations of GRSM specific to AERMOD version 23132 and compared to other regulatory NO2 options are presented and discussed in the following sections. 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 (Pala'au), 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 (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 2 Denver-Julesburg Bason, Colorado N02 Evaluation Database: https://gaftp.epa.gov/Air/aqmg/SCRAM/models/preferred/aermod/eval databases/denver-iulesburg.zip 10 ------- 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). 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 11 ------- 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 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 NO2/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. 12 ------- 1993 Palaau Database All Wind Directions g200 o 2 C£ LLl < 100 Station ° Monitor (220m) 0 100 200 300 400 NOX Observed (ug/m3) Figure 1 - Pala'au NOx Ranked Q-Q Plot 1993 Palaau Database All Wind Directions N02_0ption ° ARM2 ^ OLMALL ~ PVMRM +¦ GRSM 50 100 150 200 N02 Observed (ug/m3) Figure 2 - Pala'au N02 Ranked Q-Q Plot 13 ------- 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 (ng/m3) 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 gas processing 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 14 ------- 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 otherTier 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 15 ------- June 1993 - June 1994 Empire Abo Database North Monitoring Station (1.6km) N02_Option • ARM2 *¦ OLM ¦ PVMRM -I- GRSM 50 100 150 200 N02 Observed (ug/m3) Figure 4 - Empire Abo NOz Ranked Q-Q Plot for the North Monitor June 1993 - June 1994 Empire Abo Database South Monitoring Station (2.4km) 150 o~r E zs CN1 2 100 > Q O (T LLI 50 < .ALL • ' "N =32516 / /, 1 jr. t jfo# / t M Jft N02_0ption • ARM2 * OLM ¦ PVMRM -I- GRSM 50 100 150 N02 Observed (ug/m3) Figure 5 - Empire Abo N02 Ranked Q-Q Plot for the South Monitor 16 ------- 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 - Empire Abo Model Performance Statistics Summary (pg/m3) 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 and NO2 predictions using OLM and GRSM trend below the 1:1 line at the upper end of the concentration distributions, but above the 1:2 line. NO2 predictions using ARM2 performs above the 2:1 line through the first half of the ranked 17 ------- distribution, converging to the 1:1 line at 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. Balko Balko ALL Winds ALL Winds chem o ARM2 OLMGRPALL PVMRM A GRSM N =/6968 300- 200- 100 0 100 200 300 N02_ugm3 CO E §> 1500 x x X CO IN > I Q O a: LU < 1000 500 CD 13 I X X X CO CM > I o o chem • NOx Figure 6 - Balko NOx and N02 Ranked Q-Q Plot for all monitors 500 1000 1500 NOx_ugm3 18 ------- CO E o o 00 CD 13 1 X 600- X X CO 400- C\J > n o 200- rr LU 0- < Field N / 567 / / 9 f/ jr.-'' // /• m ** 0 200 400 600 800 NOx_ugm3 chem • NOx chem • NOx CO o> 3 I X X X CO CM > I D O K LU < CO O) =3 I X X X CO CM > I Q O a: LU < 150 100- 50- 0 50 100 150 N02_ugm3 300- 200- 100- NF N =,$?76 / y / / ' \ \ \ ** chem o ARM2 OLMGRPALL PVMRM A GRSM chem o ARM2 OLMGRPALL PVMRM A GRSM 0 100 200 300 N02_ugm3 CO E §> 1500 x x X CO CM > Q 0 01 LU < 1000- 500 0 500 1000 1500 NOx_ugm3 N02_ugm3 CO E 1200 lib 13 X x x CO CN > I Q o or LU < 600 300- 300 600 900 1200 NOx_ugm3 N =?'232 / 800 co E ~CT> X1 600 X X CO CM 400 D O 200 Figure 7 - Balko NOx and N02 Ranked Q-Q Plot by monitor Tower chem • NOx 0 200 400 600 800 NOx_ugm3 150 O) 13 X X X CO 100 CM > 0 50 100 150 N02_ugm3 Tower chem o ARM2 ~ OLMGRPALL o PVMRM A GRSM 19 ------- 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 (ng/m3) 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, PIattevilie, 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 20 ------- 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 21 ------- 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. 22 ------- Table 4 - Colorado Model Performance Statistics Summary (ng/m3) 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. 23 ------- References Carruthers, D. S., Stocker, J. R., Ellis, A., Seaton, M. D., & Smith, S. E. (2017). Evaluation of an explicit NOx chemistry method in AERMOD. Journal of the Air & Waste Management Association, 67:6, 702- 712, DOI: 10.1080/10962247.2017.1280096. Cole, H. S., & Summerhays, J. E. (1979). A Review of Techniques Available for Estimating Short-Term N02 concentrations. J. Air Poll. Cont. Assoc., 29:8, 812-817. doi:10.1080/00022470.1979.10470866 Colorado Field Study Workgroup: ERM, A. A.-W. (2020). 2014 Colorado Oil and Gas Drill Rig Field Study Model Evaluation Database - Technical Support Document. Platteville. Hanrahan, P. L. (1999a). The Plume Volume Molar Ratio Method for Determining N02/NOx Ratios in Modeling - Part I: Methodology. J. Air & Waste Manage. Assoc., 1324-1331. Hanrahan, P. L. (1999b). The Plume Volume Molar Ratio Method for Determining N02/NOx Ratios in Modeling - Part II: Evaluation Studies. J. Air & Waste Manage. Assoc., 1332-1338. Panek, J. A. (2020). PRCI ambient N02 AERMOD performance assessment and model improvement project: Modeled to observed comparison. Journal of the Air & Waste Management Association, Vol. 70, No. 5, 504-521. Podrez, M. (2015). An update to the ambient ratio method for 1-h N02 air quality standards dispersion modeling. Atm. Env., 163-170. RTP Environmental Associates, Inc. (2013, March 3). Ambient Ratio Method Version 2 (ARM2)for use with AERMOD for 1-hr N02 Modeling. Retrieved from http://www.epa.gov/ttn/scram/models/aermod/ARM2_Development_and_Evaluation_Report- September_20_2013.pdf Stocker, J. R. (2022). #M023 Development and Development of GRSM for N02 Conversion in AERMOD. Guideline on Air Quality Models: Developing the Future (p. Conference Proceedings). Durham, NC: Air & Waste Management Association. Stocker, J. R., Seaton, M. D., Smith, S. E., O'Neill, J., Johnson, K., Jackson, R., & Carruthers, D. (2023). Evaluation of the Generic Reaction Set Method for N02 conversion in AERMOD. The modification of AERMOD to include ADMS chemistry. Cambridge Environmental Research Consultants (CERC) Technical Report. U.S. EPA. (1992). Protocol for Determining the Best Performing Model. EPA-454/R-92-025. Research Triangle Park, NC: Office of Air Quality Planning and Standards. U.S. EPA. (2015, December). Technical support document (TSD) for N02-related AERMOD modifications. Publication No. EPA-454/B-15-004. Research Triangle Park, North Carolina: Office of Air Quality Planning and Standards. 24 ------- Appendix A - GRSM Code Updates and Testing for AERMOD v23132 The GRSM code implemented as a beta option in AERMOD version 22112 (GRSM22112) was updated in AERMOD version 23132 (GRSM23132) to improve the performance and model behavior as dependent upon the following calculations: • Instantaneous plume spread • Building effects on plume spread in near-to-far wake downwash zones • Multiple plume effects combined with building effects This appendix provides clarifying supporting information on the improved performance and model sensitivities to these calculations as implemented in GRSM23132. The motivation for code revisions to GRSM in AERMOD version 23132 includes accounting for building effects on enhanced dispersion and entrainment of ozone and subsequent reaction with modeled NOx emissions. Treatment of building effects addresses overpredictions for some source and downwash configurations at receptors located between near and far-wake dispersion zones out to approximately l-3km downwind where building effected turbulence intensities approach ambient, non-building turbulence levels. Further discussion and details on the specific mathematical formulation changes and physics can be referenced in (Stocker J. R., 2022). A.l Model Performance Evaluation Results Summary The GRSM23132 code updates were compared to the previous GRSM22112 code for the four model performance evaluation NO2 databases covered in this TSD. Please refer to the text in the main body of the TSD for discussion of the specific details on each NO2 database. This section provides side-by-side model performance comparisons between the two GRSM formulations in terms of scatter plots, ranked Q-Q plots (Figures A.1-1 through A.l-9), and a model performance statistical summary shown in Table A.1-1. Each scatter and Q-Q plot show model results from GRSM23132 and GRSM22112 for each of the four NO2 databases to support implementation of the updated code implemented in AERMOD version 23132. Note that all four NO2 databases include building downwash with monitor receptors at distances varying between 50m and 2.4 km, thus, providing a basis for comparing updates to the building effects formulations in GRSM23132. The scatter plot and Q-Q plot for the Pala'au database are shown in Figure A.1-1. The scatter plot shows model-observation data pairs that are paired in time and space. This provides some indication of the general agreement between modeled and observed data while also illustrating combined uncertainties in terms of overall performance of dispersion and NO2 chemistry predictions. The scatter plot for Pala'au indicates a wider range of model predictions with GRSM23132, with the slope of the linear model increasing closer to unity than shown for GRSM22112 concentration pairs, suggesting GRSM22112 agreement with observed NO2 values may be lacking in statistical range. The larger statistical range of GRSM23132 concentrations carries forward to the ranked Q-Q plot, where ranked model-observation values are unpaired in time and space, and indicates better agreement between GRSM23132 predictions and observations, whereas GRSM22112 slightly underpredicts by comparison, especially at the upper range of the concentration distribution. Note the Pala'au monitor is located within the 25 ------- near wake building downwash zone and improved agreement with GRSM23132 predictions at that location indicates treatment of building effects is a contributing factor. Table A.1-1 statistics indicate more favorable RHC and FB results for GRSM23132 further indicating improved performance from the treatment of building effects on near wake zone dispersion and NO2 chemistry. o 00 E o O) O z Q O o ¦¦a- cr LU < Figure Error! No text of specified style in document..1-1 - Pala'au Monitor Scatter Plot and Ranked Q-Q Plot Figures A.1-2 and A.1-3 show the scatter plot and Q-Q plot for the Empire Abo NO2 database at the North and South Monitoring stations located 1.6km northeast and 2.4km southwest, respectively. Very little difference in scatter and Q-Q plot data pairs are shown for GRSM23132 and GRSM22112 NO2 predictions. This indicates that there is no significant difference between the performance of the GRSM code versions for the non-varying emission sources and building downwash characterizations modeled for Empire Abo at distances extending in the near field 1- 3km. Table A.1-1 indicates a slight increase in fractional bias for GRSM23132. Therefore, the Empire Abo database was not found to be overly sensitive to the code updates in GRSM23132. Pala'au - Northwest Monitor (220m) Scatter Plot Ranked 0 20 40 60 80 Observed N02 (|.ig/m3) 0 20 40 60 80 Observed N02 (^g/m3) O - GRSM23132 y = 0.69 x + 1.98 R2 = 0.394 -H-- GRSM22112 y = 0.56 x +1.91 R2 = 0.467 0 GRSM23132 + GRSM22112 26 ------- Empire Abo - North Monitor (1,6km) Scatter Plot Ranked Q-Q Plot Observed N02 (ng/m ) Observed N02 (ng/m ) Figure Error! No text of specified style in document..1-2 - Empire Abo - North Monitor Scatter Plot and Ranked Q-Q Plot Empire Abo - South Monitor (2.4km) Scatter Plot Ranked Q-Q Plot Figure Error! No text of specified style in document..1-3 - Empire Abo - South Monitor Scatter Plot and Ranked Q-Q Plot Scatter plot and Q-Q plot performance comparisons between GRSM23132 and GRSM22112 for the Balko natural gas compressor station N02 database are shown in Figures A.l-4, A.l-5, A.l-6, and A.l-7 for the Field, North Fence (IMF), East Fence (EF), and Tower monitoring stations, respectively. Generally, the scatter plots indicate a slight increase in GRSM23132 predictions compared to GRSM22112; however, Q-Q plots show slight improvements in performance in terms of GRSM23132 predictions approaching one-to-one agreement between model-observation data pairs at the upper-end of the 27 ------- concentration distribution values shown, most notably for the NF and EF monitoring receptor locations where near wake building downwash effects are expected to significantly influence model performance. This is consistent with the overall improved performance shown in Table A.l-1 for RHCs, FBs, RHC ratios, and RHC FBs at the NF, EF, and Tower receptor locations. Balko - Field (425 m) Scatter Plot Ranked Q-Q Plot o O o E D) O CO O CD O o CNJ o CN o o E D) o 00 o CD o "3- o CNJ ° GRSM23132 + GRSM22112 Observed N02 (ng/m ) Observed N02 (fig/m ) Figure Error! No text of specified style in document..1-4 - Balko - Field (north) Monitor Scatter Plot and Ranked Q-Q Plot Balko - North Fence (140 m) o o CN Scatter Plot GRSM23132 y = 0.39 x+ 14.95 R2 = 0.287 - + - GRSM22112 y = 0.38 x+ 15.61 R2 = 0.279 150 Observed N02 (ng/m3) o o CM ¦o O E in O z a O 5 cc LLI < ° GRSM23132 + GRSM22112 Ranked Q-Q Plot 150 Observed N02 (ng/m3) Figure Error! No text of specified style in document..1-5 - Balko - North Fence Monitor Scatter Plot and Ranked Q-Q Plot 28 ------- Figure Error! No text of specified style in document..1-6 - Balko - East Fence Monitor Scatter Plot and Ranked Q-Q Plot Balko - Tower (66 m) Scatter Plot Ranked Q-Q Plot Figure Error! No text of specified style in document..1-7 - Balko - Tower (southeast) Monitor Scatter Plot and Ranked Q-Q Plot Model performance improvements for the Colorado oil and gas well pad N02 database are indicated in comparison scatter plot and Q-Q plot Figures A.l-8 and A.l-9 for Pad 1 and Pad 2, respectively. GRSM23132 shows less tendency for underpredictions at the upper end of the concentration distribution in scatter plots and Q-Q plots for both Pad 1 and Pad 2. Statistics shown in Table A.l-1 indicate the most improvement of all four databases evaluated for Pad 1 and Pad 2 in terms of RHCs, FB, RHC FBs, and RHC ratios. The twelve monitor receptors at both Pad 1 and Pad 2 were located well within the cavity and near wake downwash zones of the well pad structures and short stacks modeled, and as 29 ------- GRSM23132 y = 0.42 x + 8.03 R2 = 0.404 O o h CvJ o GRSM23132 + GRSM22112 GRSM22112 y = 0.36 x + 7.67 R2 = 0.438 such, model performance improvements for these receptors support implementation of GRSM23132 treatment of building effects over the more simplistic treatments provided by GRSM22112. Colorado - Pad 1 (50~100m) Scatter Plot Ranked o - 50 100 150 Observed N02 (ng/m3) 50 100 150 200 Observed N02 (fjg/m3) o - Figure Error! No text of specified style in document..1-8 - Colorado - Pad 1 Monitors Scatter Plot and Ranked Q-Q Plot O O CO O 2 Q O o o CNJ 01 LU < Figure Error! No text of specified style in document..1-9 - Colorado - Pad 2 Monitors Scatter Plot and Ranked Q-Q Plot -®- GRSM23132 y= 0.26 x+ 13.01 R2 = 0.308 -+- GRSM22112 y = 0.24 x+ 12.09 R2 = 0.336 Colorado - Pad 2 Scatter Plot (50-100m) Ranked Q-Q Plot 0 100 200 300 Observed NOa (|ig/m3) 0 100 200 300 Observed N02 (ng/m3) ° GRSM23132 + GRSM22112 O - O o E "Bi 6 z Q O 2 Q1 LU < 30 ------- Table A.l-1 - GRSM v23132 and v22112 Model Performance Statistics Summary (|ig/m3) Statistics Pala'au Empire Abo Balko Colorado Northwest North South Field NF EF Tower Pad 1 Pad 2 N 7856 8418 8129 576 803 238 149 720 753 FB V23132 -0.902 0.314 0.016 -0.297 0.285 -0.157 0.196 0.526 0.723 FB V22112 -0.890 0.316 0.016 -0.243 0.277 -0.136 0.217 0.604 0.755 RHC Obs 91.0 130.3 72.6 98.3 217.7 106.3 121.7 196.3 388.9 RHC V23132 82.9 153.0 129.4 118.8 146.3 138.8 118.7 121.2 180.1 RHC V22112 64.1 151.4 128.6 105.8 138.4 147.6 104.4 94.1 153.4 RHC Ratio v23132 0.911 1.174 1.783 1.209 0.672 1.306 0.975 0.617 0.463 RHC Ratio v22112 0.704 1.163 1.773 1.077 0.636 1.389 0.857 0.479 0.394 RHC FB V23132 0.093 -0.160 -0.563 -0.189 0.392 -0.266 0.025 0.473 0.734 RHC_FB_v22112 0.347 -0.150 -0.557 -0.074 0.446 -0.326 0.153 0.704 0.868 In summary, comparisons between GRSM23132 and GRSM22112 for all four NO2 databases, as shown in scatter plots, Q-Q plots, and statistical metrics, indicate that improvements in modeled NO2 predictions range between a few micrograms to tens of micrograms per cubic meter in favor of GRSM23132 implementation as a Tier 3 screening option. Performance improvements were the most pronounced for the Pala'au and Colorado databases, suggesting the previous GRSM22112 formulation does not adequately account for building effects on enhanced ozone entrainment and downward mixing of NOx plumes in the cavity and near wake building downwash zones. Improvements from the GRSM23132 treatment of building effects was only slightly indicated for the Balko database, which suggests uncertainties in characterization of building downwash and emissions (for the single dominant short stack) play a role in model-observation comparisons independent of the GRSM23132 treatments. In conclusion, the code updates in GRSM23132 show improved performance over the previous GRSM22112 version, and therefore, the updated GRSM23132 code was implemented in AERMOD version 23132. 31 ------- A.2 Sensitivity Modeling Results Comparison Model behavior and sensitivity comparisons between GRSM23132 and GRSM22112 were conducted for five stacks modeled at release heights of 35-meters, 50-meters, and 65-meters with emissions modeled at 1,000 tpy (28.8 g/s) from each stack. The five stacks were positioned north-to-south centered along two adjacent multi-tiered buildings, thus, configured to illustrate differences between GRSM23132 and GRSM22112 treatments of building effected instantaneous ensemble plume dispersion and ground-level concentrations in the near-wake (or building downwash cavity), far-wake (adjacent to the near-wake) zones, and subsequent transition to the near-field distances between 1km and 3km downwind. Figure A.2-1 shows the positioning and multi-tier building complex modeled for the 35-meter, 50-meter, and 65-meter sensitivity scenarios; note the buildings and stacks represent a hypothetical installation and are located and centered at a coastal North Carolina (NC) airport for GRSM sensitivity modeling purposes only. The two adjacent buildings stretch end-to-end 196 meters with varying tier heights. The north building tier heights range 18-29 meters, and the south building tier heights range 11-18 meters. Stack parameters modeled for the 35-meter, 50-meter, and 65-meter scenarios include an exit temperature of 311 K, exit velocity of 7.35 m/s, and stack diameter of 5 meters. BPIPPRM (version 04274) was used to process building tier and stack location data to generate building downwash parameters for input to AERMOD. The building and stack configurations for the three scenarios represent stack height to building height ratios ranging between values of 1.2 and 6.5. This range of stack and building height combinations were included to assess model formulation sensitivities to building downwash and ozone entrainment behaviors under varying meteorological conditions and at varying receptor distances. Modeled receptors were located and oriented around the stacks and buildings to better identify model formulation sensitivities relevant to the building effects updates in GRSM23132. As such, receptors were positioned at 10-meter spacing surrounding the perimeter of the two buildings at the following distances: 10m, 15m, 20m, 30m, 50m, 75m, 110m, 170m, 250m, and 380m. Fine gridded receptors with 100-meter spacing extending 500m-3km. Medium gridded receptors were modeled with 500-meter spacing extending 3km-15km. Elevations for modeled receptors were based on 1-arc-second USGS terrain data and AERMAP (version 18081) default processing options; receptor elevations indicate relatively flat terrain and are consistent with the coastal study location. AERMET meteorological inputs to AERMOD were taken from a previous air quality modeling study given it was readily available and that the scope of the GRSM sensitivity focuses on building effects on NO2 chemistry and dispersion in the near-wake, far-wake, and near field. AERMET (version 19191) raw data inputs included hourly surface temperature, hourly surface wind data, and twice-daily upper air data from a coastal NC airport for a 3-year modeling period 2015-2017. Other AERMET raw data and pre-processing included ASOS 1-minute wind data processed with AERMINUTE (version 15272), 1992 National Landcover Cover Dataset (NLCD) processed with AERSURFACE (version 13016) to generate surface parameters, and AERMET 32 ------- regulatory default run options. GRSM NO2 mode! input options were configured the same for all scenarios. Hourly ozone for the 2015-2017 modeling period was developed from an isolated rural monitoring station located in Lee County, NC, The AERMOD default ozone value of 0.060 ppm was used for substitution of missing hourly ozone values. Hourly NOx values were derived from the same Lee County rural monitoring station, and based on an equilibrium ratio of 0.9 NCb/NOx, given that only hourly MO2 data was available; i.e., hourly NOx values were calculated equal to hourly NO2 divided by the 0.9 NO^/NOx equilibrium ratio. Note the Lee County monitor was located in a farm field greater than 10-20km away from any significant sources of mobile or stationary NOx emissions sources. Season-diurnal-hourly varying NO2 concentrations were developed based on the 3-year average, highest-3rd-highest seasonal values from the Lee County hourly NO2 values 2015-2017. An in-stack NO2/NQX emission ratio of 10% was modeled for all five stacks for the three sensitivity modeling scenarios. Figure Error! No text of specified style in document..2-1 - Sensitivity modeling stack and complex building configuration (graphics shown in Google Earth© and created with Lakes Environmental Software, Inc. AERMOD View©). Note: stacks shown are 30-meters tall. Figures A.2-2 through A.2-10 show model concentration contours and difference plots from GRSM23132 and GRSM22112 and for the three sensitivity modeling scenarios. The model results are represented with concentration isopleths derived from the highest-8th-highest 1- hour NO2 modeled concentrations and correspond with the model design value used in 1-hour NO2 NAAQS modeling demonstrations (using standard kriging in Surfer version 27.1.229 ®, Golden Software, LLC). Fine and medium gridded receptors are visible as small "+" symbols in each contour plot. Notably, ground level concentrations for the 35-meter stack scenario show 33 ------- modeled exceedances of the 1-hour NO2 NAAQS of 188 ng/m3 (indicated by a red isopleth) with both GRSM23132 and GRSM22112, whereas modeled concentrations for the 50-meter and 65- meter scenarios show a decrease, relative to the 35-meter scenario, ranging approximately 20%-50% (well below the NAAQS) at all receptors independent of the GRSM code version. Further discussion of model results for each scenario continues below. GRSM23112 predictions for the 35-meter stack scenario results depicted in Figure A.2-2 range between approximately 10 |-ig/m3 and 200 |-ig/m3 and show approximately 20 |-ig/m3 higher concentrations at receptors located within near-wake zone distances (~10-100 meters) as compared to GRSM22112. Similarly, GRSM22112 maximum concentrations depicted in Figure A.2-3 range between 10 |-ig/m3 and 200 |-ig/m3, but in contrast are roughly 20 ng/m3 higher at receptor distances extending from the far-wake (~100-1000 meters) to the near field and out to l-3km. These differences are spatially isolated and shown in Figure A.2-4 and indicate the GRSM23132 treatment of multiple building effected plumes lowers concentrations in the far wake and near field while enhancing concentrations by equal measure in the near-wake downwash zones. The 35-meter stack scenario stack height to building height ratio values range from 1.2 to 3.5, and thus, NOx plume size, ozone entrainment, and dispersion is heavily influenced by building downwash as indicated by the maximum concentrations at near and far- wake receptors. And notably, the GRSM23132 building effects treatment for the 35-meter stack and building configuration shows equal sensitivity to modeled concentrations in the near- wake, far-wake, and near field. The 35-meter stack scenario stack and building configuration, and thus, the heavy influence of downwash, is most comparable to the Pala'au, Colorado, and Balko NO2 evaluation database configurations as well as the distance to near-wake receptor monitor locations. As such, the enhanced concentration model behavior for near-wake receptors shown by the 35-meter stack scenario compliments the improved model performance for similarly configured NO2 databases while maintaining decreases or no change in modeled concentrations at far-wake and near field receptors. The 50-meter stack scenario shows less pronounced spatial differences between GRSM code versions than what is shown for the 35-meter stack scenario results at far-wake and near field receptor distances; however, enhanced concentrations at near-wake receptors are relatively more sensitive to GRSM23132 building treatments for the 50-meter stack scenario, albeit at concentrations approximately about 50% lower than those shown for the 35-meter stack scenario. Figure A.2-5 shows the GRSM23132 concentrations are nearly the same as GRSM22112 concentrations shown in Figure A.2-6 in the near field and far-wake downwash zones, and near-wake concentrations modeled with GRSM23132 are enhanced by the building effects treatment formulation. Concentration differences shown in Figure A.2-7 indicate GRSM23132 concentrations for the 50-meter stack scenario are enhanced in the near-wake downwash zone with increases on the order of 50 ng/m3. The enhanced concentrations predicted by GRSM23132 in the near-wake zone is complimented by similar enhanced concentrations modeled for several NO2 model performance databases with similar building effected monitor receptor exposure locations where improved model performance, as previously noted, was shown for Pala'au, Colorado, and some Balko monitors. The sensitivity of the 50-meter stack concentrations to GRSM23132 building effects treatments in the near-wake 34 ------- zone indicates vertical dispersion becomes relatively more important for taller stacks, or model setup configurations with stack height to building height ratios greater than approximately 1.5; however, this enhanced sensitivity is compensated by lower near-wake concentrations predicted from elevated plume releases and dampened building downwash effects on plume sizes and entrainment of ozone. The converse is observed for stack height to building height ratios below approximately 1.5, where the building effects treatment formulation behavior lowers the sensitivity of increased concentrations in the near-wake while decreasing concentrations due to enhanced lateral dispersion and plume sizes at receptors in the far-wake and near-field. The 65-meter stack scenario shows model sensitivities similar to the 50-meter stack scenario results, with enhanced concentrations in the near-wake and less differences at far-wake and near-field receptors between GRSM code versions. Again, this is most likely attributed to the more elevated release height of the plume relative to the building height and the GRSM23132 building effects formulation, with the 65-meter stack height to building height ratios ranging between values of approximately 2.1 and 6.5. Figure A.2-8 shows the GRSM23132 concentrations are nearly the same as GRSM22112 concentrations shown in Figure A.2-9 in the near field and far-wake downwash zones, and enhanced GRSM23132 hourly concentrations increasing on the order of 50 ng/m3 within the first hundred meters of the near and far-wake. Figure A.2-10 confirms that differences between GRSM23132 and GRSM22112 are relatively unchanged in the far-wake and near field and increased in the near-wake. Near-wake receptor performance improvements indicated by sensitivities shown for the 65-meter stack scenario are less certain than for the 35-meter and 50-meter stack scenarios given that the stack height to building height ratios for all NO2 databases are closer to values on the order of 1.1 or less, and thus, are not comparable to the 65-meter stack scenario. In summary, the code refinements evaluated and implemented in GRSM23132 are supported by improved model performance discussed in the previous section and expected code behavior shown by the sensitivity modeling analyses discussed here. GRSM23132 NO2 predictions show the building effects treatments are less apparent for the taller 50-meter and 65-meter stack scenarios with respect to modeled impacts at receptor distances in the far wake and near field; however, notable enhanced concentrations are shown in the near-wake. The building effects treatments are most pronounced for the 35-meter stack scenario at all receptor distances with lower concentrations modeled at receptors in the far-wake and near field, and equally higher concentrations modeled at receptors in the near-wake building downwash zone. The enhanced higher concentrations modeled for the 35-meter stack scenario at near-wake receptors compliments the improved model performance shown for similarly configured NO2 databases, and therefore, supports this model behavior. The near-wake behavior of GRSM23132 building effects treatment shows enhanced dispersion and entrainment of ozone that likely contributes to enhanced NO2 concentrations for shorter stacks and lower stack-to-building height ratios of approximately 1.5 or less. Ultimately, the behavior of the building effects treatment in GRSM23132 is shown to be sensitive to varying model inputs for stack and building heights, improves model performance, and is consistent with the motivation and expected range of predicted NO2 concentrations at receptors located at distances heavily influenced by building 35 ------- downwash. 3885000 3880000- 3875000 3870000 (Mg/m 300000 305000 310000 315000 320000 325000 Figure Error! No text of specified style in document..2-2 - GRSM v23132 35-meter Tall Stacks Highest-8th-High 1-hour NO2 36 ------- 3895000 3890000 3885000 3880000 3875000-1 3870000 (Hg/m 300000 305000 310000 315000 320000 325000 Figure Error! No text of specified style in document..2-3 - GRSM v22112 35-meter Tall Stacks Highest-8th-High 1-hour NO2 3895000 3890000 3885000 3880000 3875000 3870000 — 18 14 10 — 6 2 -2 1 6 i -10 -» ¦ -18 (f-ig/m3 300000 305000 310000 315000 320000 325000 37 ------- Figure Error! No text of specified style in document..2-4 - GRSM v23132 Minus v22112 35- meter Tall Stacks Highest-8th-High 1-hour NO2 3895000 3890000 3885000 3880000 3875000 3870000 — 100 — 90 — 80 — 70 - 60 - 50 1 40 1 30 L 20 ¦ 10 (|ag/m3 300000 305000 310000 315000 320000 325000 Figure Error! No text of specified style in document..2-5 - GRSM v23132 50-meter Tall Stacks Highest-8,h-High 1-hour NO2 38 ------- 3875000 3870000 10 (l-ig/m3 300000 305000 310000 315000 320000 325000 Figure Error! No text of specified style in document..2-6 - GRSM v22112 50-meter Tall Stacks Highest-8th-High 1-hour NO2 3895000 3890000 3885000- 3880000- 3895000 3890000 3885000 3880000 3875000 3870000 50 45 40 35 30 25 15 10 t -5 (Hg/m3) 300000 305000 310000 315000 320000 325000 39 ------- Figure Error! No text of specified style in document..2-7 - GRSM v23132 Minus v22112 50- meter Tall Stacks Highest-8th-High 1-hour NO2 3895000 3890000 3885000 3880000 3875000 3870000 — 80 — 75 — 70 — 65 — 60 55 50 — 45 40 — 35 n 30 - 25 - 20 - 15 ¦ 10 (Mg/m 300000 305000 310000 315000 320000 325000 Figure Error! No text of specified style in document..2-8 - GRSM v23132 65-meter Tall Stacks Highest-8th-High 1-hour NO2 40 ------- 3875000 3870000 300000 305000 310000 315000 320000 325000 Figure Error! No text of specified style in document..2-9 - GRSM v22112 65-meter Tall Stacks Highest-8th-High 1-hour NO2 3895000 3890000 3885000- 3880000- — 80 — 75 — 70 — 65 — 60 — 55 — 50 45 40 ¦ 35 n 30 ¦ 25 r 20 - 15 ¦ 10 (Mg/m3 41 ------- 3895000 3890000 3885000 3880000 3875000 3870000 — 48 — 43 — 38 — 33 - 28 - 23 18 r ¦ -2 (f-ig/m3 300000 305000 310000 315000 320000 325000 Figure Error! No text of specified style in document..2-10 - GRSM v23132 Minus v22112 65- meter Tall Stacks Highest-8tb-High 1-hour NO2 42 ------- United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-24-005 Environmental Protection Air Quality Assessment Division November 2024 Agency Research Triangle Park, NC 43 ------- |