* O \ w PRO^ Technical Support Document (TSD) for Adoption of the Generic Reaction Set Method (GRSM) as a Regulatory Non-Default Tier-3 NO2 Screening Option ------- ------- 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 ------- 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. ------- 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 ------- 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 ------- 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 ------- 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 ------- 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 ------- 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 ------- 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 ------- 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. ------- 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 ------- 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 ------- 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 ------- 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 N02 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# / r,''~ 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 ------- 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 ------- 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 ALL Winds D O cr LU < CO E §> 1500 I x £ 1000 CM > 500 ALL N =/^742 / / / / / ' //'' / / / S * «¦» 'As 500 1000 1500 NOx_ugm3 chem NOx Balko ALL Winds CO 13 300- x x ro 200 4 cm 1 > I O o a: LU < 100- 0- ALL N =/&968 / chem ' ^ / / / 0 ARM2 OLMGRPALL m' y!CtJ <" . / ~ PVMRM / / ^ // ~ A GRSM 100 200 300 N02_ugm3 Figure 6 - Balko NOx and N02 Ranked Q-Q Plot for all monitors ------- 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 /? ,/Z jr.-'' // / m ** 0 200 400 600 800 NOx_ugm3 CO E §> 1500 I x | 1000 H CM > I O 500 O ^ 0 LU u < NF N =#'794 / / / / / / / / / / / y " / >r * r «» / / * / / // ~ << / 0 500 1000 1500 NOx_ugm3 CO E 1200 lib 13 x1 900- X X 04 600 > 300- QT LU < EF N =?'232 // / {? / ' / / - // ~ 0 300 600 900 1200 NOx_ugm3 co E 800 =J x1 600 X X co C\J 400 > I § 200- LLJ 0 < Tower / / N =? 149 / / / / / / / / / - / / / / 7 * «¦» / / ~ . / ^ tW' x 0 200 400 600 800 NOx_ugm3 chem NOx chem NOx chem NOx chem NOx chem o ARM2 OLMGRPALL PVMRM A GRSM 50 100 150 N02_ugm3 300 CO E O) =5 X X X 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 ------- 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. ------- 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. 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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. ------- United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-23-009 Environmental Protection Air Quality Assessment Division October 2023 Agency Research Triangle Park, NC ------- |