Development of a NOx Chemistry Module for EDMS/AEDT to Predict NO2 Concentrations (2017)

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6  presumably by the oxidation of NO resulting from the elevated peroxy radicals concentrations in the aircraft engine plume. 2.4 Airport Dispersion Modeling   Compared to other sources of emissions (e.g., industrial, manufacturing, etc.) airports are particularly unique in ways that can have significant effects on how dispersion modeling is conducted and how accurate the results are expected to be. These factors include (but are not limited to) the following:  Source location ‐ Since an airport can encompass a relatively large area (some major airports may be 20 square miles or more in size), it can be difficult to accurately characterize the location of each source. Along with meteorological data, the proper characterization of each source’s position information can be critical. Incorrect receptor location can also significantly affect concentration predictions as the receptors may be impacted by different densities of a plume (e.g., may under- or over-predict based on the location of the receptor in relation to the emission point).  Multiple  Sources  ‐ Unlike a single stationary source or even an industrial facility, the diversity of sources at an airport and the aforementioned spatial distribution over a large area provides a modeling challenge. Even with the aggregation of some sources into groups (e.g., GSE grouped into area sources, aircraft modeled as part of taxiway and runway area sources, etc.), the number of sources for an airport can number in the thousands for a full airport assessment (i.e., based on using the modeling scheme implemented within EDMS/AERMOD).  Moving Sources ‐ Airports also include moving sources that present a challenge in how simulate their source type(s) within a computer modeling framework. For example, aircraft may be modeled as part of segmented taxiway and runway area sources, while GSE may be modeled as area sources around airport terminal gate areas. Since the emissions are uniform within each spatial configuration, any variation to the emissions within the source type is difficult to accurately model. AERMOD is considered a “state-of-the-art” dispersion model, but it is mainly intended for application to stationary sources (e.g., point source stacks). Moreover, the Gaussian plume formulation within AERMOD is designed to simulate emissions, plume meandering, meteorology, etc. averaged over a one-hour period. Although finer temporal resolutions are possible (e.g., 15-minutes), one-hour is generally the minimum resolution used for such modeling efforts. As such, sub-hour impacts of airport operations and meteorology are difficult to determine. A time-varying Gaussian puff method can better simulate moving sources if each moving source is treated as a discrete source with temporally- and spatially-varying emissions release points. While the literature is abundant in terms of using Gaussian plume models to assess air quality impacts from stationary sources (e.g., power plant stacks), there are comparatively few that assess the use of AERMOD (as well as Gaussian puff models) with mobile sources and even fewer involving airport sources. Further and more detailed information on these models is included in Section 3.2. 3.0 Assessments of Models/Methods  Presently, there are a number of available models that have been devised, or can be used, for computing NO2 concentrations in the ambient air, although none of them are specifically designed for airport applications. The models (and the supporting methods for EDMS/AEDT  The FAA replaced the EDMS with the  AEDT in May 2015, but most of the  modeling for this Research Project  was conducted using EDMS.  Nevertheless, the end‐products of  the research are expected to be  applicable to both models.   Modeling Airports  Unlike stationary sources, the large  number and diversity of NOx  emission sources at an airport,  combined with their spatial  distribution over a large area,  presents significant modeling  demands.  

7  computing NO2 concentrations) evaluated in support of this Research Project are described in the following material. 3.1 EDMS/AEDT and AERMOD  At the onset of this Research Project, FAA’s EDMS was then used to compute airport-related emissions (including emissions of NOx) and applied AERMOD to predict the resultant concentrations of NO2. Importantly, FAA’s AEDT2b replaced both the Integrated Noise Model (INM) and EDMS in May 2015, again incorporating AERMOD in the new model. Insofar as the majority of the modeling for this research was completed prior to that date, EDMS/AERMOD was used. However, the recommended Preferred Method is expected to be applicable also to AEDT2b/AERMOD. By way of background and simply stated, airport dispersion modeling is conducted with either model based on the outputs of an emissions inventory, supplemented with meteorological data and the designation of receptor sites. Once the necessary data are input, EDMS or AEDT internally calls for AERMOD to perform the specified dispersion calculations. The estimated NO2 concentrations are displayed in tabular formats, charts and/or diagrams (or maps) by receptor number or name or as contour lines. The U.S EPA has provided three methods of increasing complexity to compute NO2 concentrations using AERMOD. These methods are termed “Tiers” and are summarized as follows:  Tier 1, Full Conversion - This “Default” method assumes complete (i.e., 100 percent) conversion of all emitted NOx to NO2 and is, therefore, considered to be the most conservative of the three Tiers. As mentioned previously in this report, the term “conservative” in this context means the method predicts at the high end of the range of reasonable results for NOx to NO2 conversion.  Tier 2, ARM/ARM2 ‐ The Ambient Ratio Method (ARM) is where model-predicted NOx concentrations are multiplied by a NO2/NOx “ambient ratio” derived from actual monitoring data. There are two versions of this method:  ARM - The ARM multiplies the AERMOD results by empirically-derived ambient NO2/NOx ratios, with 0.75 as the “default ratio” for annual conditions and 0.80 as the default ratio for 1- hour impacts. According to U.S. EPA, site-specific ambient NO2/NOx ratios derived from appropriate ambient monitoring data may also be used on a case-by-case basis and with proper justification.  ARM2 - This method incorporates a variable ambient ratio that is a function of AERMOD- predicted one-hour NOx concentrations and based on an analysis of hourly ambient NOx monitoring data from approximately 580 stations nationwide and over a 10-year period. Moreover, ARM2 “post‐processes” empirical data to define the NO2/NOx ambient ratios based on the NOx concentration and distance from the source for each location rather than applying a single ratio. The ARM2 is a non-default “Beta” option in AERMOD.  Tier 3, OLM/PVMRM  ‐ Under this Tier, detailed analyses are conducted of the NO2/NOx ratios taking into account ambient background O3 concentrations. Two approaches are available:  Ozone  Limiting Method  (OLM) - The OLM involves an initial comparison of the estimated maximum NOx concentration and the concurrent ambient O3 concentration to determine which one would be the limiting factor to NO2 formation. If the O3 concentration is greater than the maximum NOx concentration, total conversion is assumed. If the maximum NOx concentration is greater than the O3 concentration, the predicted formation of NO2 with this method would be limited by the ambient O3 concentration. AERMOD ARM2 Method  Qualitatively‐speaking, AERMOD  with the application of the ARM2  method seems to have the fewest  potential compatibility issues when  it comes to applying it to the  EDMS/AEDT models.   

8     Plume Volume Molar Ratio Method  (PVMRM)  ‐ The PVMRM takes into account the plume size and models the reactions along the length of the plume. Rather than concentrations, moles of O3 and NOx are used to determine the NO2/NOx ratio at each receptor location. A number of moles are calculated based on the size of the plume at a receptor location calculated using the plume dispersion parameters. If the number of O3 moles is less than NOx moles, then the moles of NO2 is set equal to O3 moles plus the initial NO2 present in the exhaust (e.g., 10 percent). If the number of O3 moles is greater, then the following photostationary reaction equation is used:  NO2/NOx = (K1/K3)O3 / [1+(K1/K3)O3] Where: K1 and K3 are the reaction rates and K3 is dependent on the zenith angle of the sun. Unlike OLM, PVMRM can handle multiple plumes where the dominant plume is enhanced based on distances to other plumes. Notably, neither the OLM nor PVMRM take into account other photochemical reactions (e.g., with VOCs). In comparison to Tiers 1 and 2, the Tier 3 Methods (e.g., OLM and PVMRM) require the most detailed level of data and analysis and are therefore expected to produce the least conservative (and presumably, the most representative) results. Key model inputs for both the OLM and PVMRM options are the “in-stack” ratios of NO2/NOx emissions and background O3 concentrations. The EPA has provided some guidance for conducting short-term (i.e., one hour) NO2 modeling using the Tier 3 methods. The following paraphrases EPA’s guidance:1,2  The recommendations regarding the annual NO2 modeling are also applicable to modeling for the new one-hour NO2 standard, but additional issues may need to be considered in the context of the latter. For example, certain input data requirements and assumptions for Tier 3 applications may be of greater importance for the one-hour modeling given the more localized nature of peak hourly vs. annual impacts. 3.2 Alternative Models and Methods  For the purposes of this Research, in addition to the methods described above using AERMOD, four alternative methods for predicting NO2 concentrations were also evaluated. The following provides brief summaries of these models:   CALINE4 - Termed the California Line Source Model, this Gaussian “line source” model was developed by the California Department of Transportation (CalTrans) to predict pollutant concentrations around roadways. The CALINE4 NO2 methodology employs a “discrete parcel method” similar in concept to the PVMRM, but assumes an initial mixing zone around the roadway where initial conditions are set. The same NO + O3 and NO2 photostationary state reactions described above are also modeled taking into account plume size.  CALPUFF - The California Puff Model is also a Gaussian model with the capability to model point, line, area, and volume sources. Its domain of usage is similar to that of AERMOD, except that it offers time- varying concentrations and other capabilities (e.g., PM speciation predictions). NO2 modeling is based on the use of pseudo-first-order chemistry with optional methods involving different chemical compounds.   By comparison to CMAQ (below), CALPUFF is less complex to use and requires less CPU time.                                                              1 EPA, Guidance Concerning the Implementation of the 1-hour NO2 NAAQS for the Prevention of Significant Deterioration Program, June 29, 2010. http://www.epa.gov/ttn/scram/ClarificationMemo_AppendixW_Hourly‐NO2‐NAAQS_FINAL_06‐28‐2010.pdf. 2 EPA, Additional Clarification Regarding Application of Appendix W Modeling Guidance for the 1-Hour NO2 National Ambient Air Quality Standard, March 1, 2011. http://www.epa.gov/ttn/scram/Additional_Clarifications_AppendixW_Hourly‐NO2‐NAAQS_FINAL_03‐01‐2011.pdf. EPA Guidance  EPA recognizes that potential  adjustments must be made when  modeling short‐term (i.e., one‐hour)  NO2 concentrations when compared  to predicting annual values.  

9   CMAQ  - The Community Multiscale Air Quality model is the U.S. EPA’s grid-based, regional air quality model. Unlike the Gaussian models, CMAQ uses a mass conservation principle with large scale grids (e.g., smallest grids are usually 4 km x 4 km) to model pollutant concentrations. CMAQ incorporates a full complement of photochemical reactions including reactions with O3 and VOCs (while this model can accept user-supplied O3, it does not require it). These reactions are grouped into modules including the Carbon Bond 2005 (CB05). Although these methods are incorporated in the grid model, they can potentially be implemented into a Gaussian plume environment. While the research determined early on that CMAQ is inherently not useable as the modeling system for the Preferred Method, its chemistry module was of interest. As the SCICHEM (discussed below) uses the same photochemistry treatment, that system was used in the research instead of CMAQ.  SCICHEM - This is a Lagrangian photochemical puff model with different options for gas and aerosol chemistry schemes, the most detailed of which are consistent with the mechanisms found in photochemical grid models. From the first version, developed in the late 1990s, through a major upgrade effort in 2011, the most current version (SCICHEM 3.0) was released in 2015. It features a CB05 gas-phase chemistry mechanism, aerosol and aqueous chemistry modules based on CMAQ 4.7.1, and can model the dispersion of primary pollutants and the formation of secondary pollutants, including the conversion of NO to NO2. SCICHEM is not as complex to use as CMAQ and it does not use as much CPU time as that model. For ease of review, Table 3 provides a summary listing and descriptions of the alternative models evaluated in support of this Research and Table 4 provides qualitative information on their characteristics, assets, and limitations (due to space limitations in the tables, some of the terms are abbreviated). 3.3 Model/Method Performance Characteristics  Drawing upon the information contained in Tables 3 and 4, the following discusses the relevant characteristics of the models/methods evaluated in support of the Research, with the emphasis on their potential applications involving EDMS/AEDT to predict NO2 levels in the vicinities of airports. The discussion of the model characteristics is presented in order of importance, with the most important considerations discussed first. Accompanying the discussion, an upcoming figure uses overlapping ovals to demonstrate desirable performance characteristics required to achieve the Preferred Method.  AEDT/AERMOD Compatibility - An important consideration in assessing candidate models/methods for potential incorporation into EDMS/AEDT-AERMOD are their comparative compatibilities. For example, CALINE4 is difficult to adopt because the computational method is dependent on the characteristics of the mixing zone around each line source. While the method could be modified for use with other dispersion methodologies, the current framework will not allow such use. The computational methods used in CALPUFF and SCICHEM are also challenging to implement within AERMOD due to significant differences in their modeling frameworks. In particular, it would be difficult to implement the time-varying methods in CALPUFF and SCICHEM into AERMOD’s static plume framework. While simplifications and assumptions could be made, it would require important changes to the AERMOD methods.  Agency acceptability - Each of the “non-full conversion” methods can be considered “good” in terms of model acceptability because each have been vetted through the U.S. EPA (or state) model evaluation processes. The AERMOD methods are a part of the U.S. EPA tiered methods for NO2 while the others are EPA-designated “Recommended/Alternative”.  Input data complexity - Input data complexity refers to the types and amount of input data required to use the methods/models. While simpler input requirements may be preferred, input data requirements may also be viewed as an indication of the level of modeling functionally (i.e., ability to take into account various factors). Except for the full conversion method and ARM/ARM2, the methods have similar input data complexities with options to use “default” or representative data versus more detailed, user-supplied information (e.g., a single ambient O3 concentration or hourly values).

 SCIC requ atmo  Sour for sourc of lin gridd mod is a c than shou allow quali  Trea II (A maki whil acco  User Full mod refle  Use  the o of so using mete prov  Mod be re of w limit mod make comp gene acco appli  User of ev This spec cond  Fines mod Gaus time resol depe HEM and C irements if t spheric data ce‐specific m the flexibilit es, except fo e sources) a ed areas, alt el some speci haracterizati the capabilit ld be applica greater de ty conditions tment of NO RM/ARM2) ng a photost e the ARM2 unts for it.  ‐supplied NO Conversion e of operation cts the flexib of monitorin verall fidelity me meteorol NO2/NOx a orological da iding better m eling source  cognized tha hich are tied of 200 sour eling. While it difficult utational co ral, all of the unt for varyi cation of eac ‐supplied am olving plume is also true f ified O3 co itions. t  time‐vary eled concentr sian plume frame wherea utions (e.g., nds on wheth MAQ can h he user cho under the “fu odeling - M y in model r CALINE4 nd CMAQ (w hough plum fic sources). on of the mo ies of the m ble to individ grees of flex . /NO2/O3 che , the evalua ationary stat method does 2/NOx ratios and the CAL . For those th ility of each m g data to imp of the mode ogical data in nd O3 conce ta and back odeling appl limitations - t some mode to computer ces, and CM it is not a re to properly mplexity an core NOx co ng NO2/NOx h model. bient O3 con s, the regres or the effects ncentrations, ing  concentr ation is also (AERMOD) s Gaussian p one-min, on er the input s ave the mos oses to supp ll chemistry” ost of the m ing specific (where sour hich models e-in-grid is a Importantly, deling frame ethods as ea ual sources w ibility in m mistry - With ted models/ e assumption not explicit  by source a INE4 metho at do allow t odel to allow rove backgr ling work thr OLM and P ntration data ground pollu ications. In consideri ls and modeli resources an AQ limits t flection of th test the NO d source spe nversion met ratios, but th centrations sed NO2 pred from ambie the regres ations - Th a function and Eulrian uff models h e-sec, etc.). ource charac 10  t complex ly detailed option. odels allow individual ces are part sources as vailable to this feature work rather ch method hich could odeling air the exceptio methods exp or directly ly model this nd operatio d) allow for his, it is nece modeling b ound air cha ough the use VMRM, the . In contrast tant concent ng the imple ng framewor d runtime is he number o e chemistry 2 modeling cifications o hods can acc e functionali - Although A iction equati nt O3 concen sion method e modeling of the model grid (e.g., ave the poten However, th teristics (e.g. ns of AERM licitly accou modeling th chemistry, t nal mode - A the use of e ssarily tied t y mode.  racteristics - of “supplem methods in , the other m rations to im mentation of ks may have sues. As exa f receptors t module’s cap capabilities. f NO2/NOx ount for diffe ty depends l RM2 also do on intrinsical trations. Alth ology essen time period (s)/method(s CMAQ) m tial to compu e usefulness , emission fa ME OD Tiers I ( nt for NO/N e chemical c he method’s ll of the me xhaust NO2/N o the “parent This criterio ental” air dat AERMOD a ethods can prove the m methods wit computation mples, CALP hat can be u abilities, CA This limita ratios by m rent source m argely on the es not explic ly takes into ough ARM2 tially accou (i.e., averag ) combinatio odels are lim te concentrat and fidelity ctors, locatio THOD EVALUAT Full Convers O2/O3 reac onversion. regression a thods (excep Ox ratios b ” model and t n refers to im a. With the e re mainly fo potentially u odeling resu hin models, ally limitatio UFF curren sed in plum LPUFF’s lim tion also pe ode of oper odes of ope design and itly model th account these does not all nts for bac ing period) ns. For exam ited to a o ions at very of temporal n, etc.), weat ION CRITERIA ion) and tions by Notably, lgorithm t for the y source herefore proving xception cused on se other lts, thus it should ns, some tly has a e-in-grid itations rtains to ation. In ration to intended e effects effects. ow user- kground for each ple, the ne-hour fine time features her data,

11    background concentrations, etc. also provide compatible resolution(s). Overall, finer time-varying frameworks are expected to provide more accurate modeling outcomes.  Plume  growth  &  multiple  plumes - Plume growth and accounting for changes in O3 concentrations within evolving plumes, as well as the ability to model the interactions of multiple plumes, are effects that improve the overall NO2 predictions. Methods (e.g., OLM) that do not explicitly model these effects may rely on assumptions that the impacts are small. While not explicitly taking into account these effects may not produce inaccurate results, it inherently reduces the capability of the method. Regarding OLM, the methodology as implemented in AERMOD does allow the use of an option where sources with overlapping plumes can be merged (i.e., “grouped”) so that the aggregate NO in the merged plume competes for available O3. This generally results in lower, potentially more accurate predicted NO2 concentrations.  Plume  segmentation - Plume segmentation and plume region modeling are similar to accounting for changes in plume characteristics (e.g., plume growth, O3 concentrations, etc.), but may be seen as further refinement. This allows for more explicitly modeling of evolving chemical characteristics of plumes and the conversions that occur at different locations along the plume.  Chemical species beyond NO/NO2/O3 - The methods within AERMOD are specific towards modeling NOx conversion and currently do not allow modeling of other reactive species. In contrast, the methods in some of the other models allow for modeling various other reactive chemicals. CMAQ and SCICHEM are two examples and are the most robust in allowing for full chemistry of the atmosphere. The ability to model other reactive species may be beneficial to airport modeling but issues of complexity and runtime would need to be considered.  Full chemistry challenges - While the full chemistry capabilities within SCICHEM and CMAQ may allow for greater model fidelity, the increased degree of control can also make the use of these capabilities more difficult as it requires a deeper understanding of the effects from each input parameter. Although region-specific, default background (concentration) data is available for the full chemistry option, modifying this default data for greater accuracy may be difficult due to obtaining the data and understanding the outputs relative to modeled NO2. In the following figure, the model characteristics considered most important by the Research Team are presented in the top row of the flow chart, with the flow chart arrows indicating the hierarchical order of importance. For each model characteristic, the models/methods considered are “scored” according to their anticipated performance relative to that characteristic.  Key Characteristics  As part of the Preferred Method  evaluation/development process,  the following “target” criteria were  identified as being important:   Reasonable Data Requirements   Practical Computational  Demands   Reasonable Accuracy   Technically Defensible   EDMS/AEDT/AERMOD‐ Compatible  See Preferred Method Key Targets  (Section 1.3). 

 Hierarchical Mappin 12  g of Models/Methods Preferences  

13  Table 3. Overview of Model Properties and Characteristics  Model EPA Classification Common Uses Underlying Method Pollutants NOx Chemistry Application Source Types Terrain Modeling Spatial Range Temporal Resolution AERMOD On EPA's Preferred / Recommended List Used for most regulatory point source assessments. Contained within EDMS Steady-State Gaussian Plume Any relatively stable, primary pollutant (e.g., CO, TSP, PM2.5, PM10), SO2, NO2 Full conversion, ARM/ARM2, OLM and PVMRM Point, area, volume Yes 10 m - 20 km 1 hr - 1 year (multi-year possible) averaging times CALPUFF On EPA's Preferred / Recommended List Used as an alternative to AERMOD, especially for "long range" transport Gaussian puff As above Pseudo-first- order Point, line, area, volume Yes 10 m to >100 km 1 hr to 1 year averaging times CALINE4 Not on U.S. EPA's Preferred / Recommended List (Mostly applicable in California) Gaussian line source model used for highway sources Steady-state Gaussian plume from line sources Any relatively stable primary pollutant such as CO and NO2 First-Order Discrete Parcel Method Line No Gaussian short range 1 hr averaging time CMAQ On EPA's List of Photochemical Grid Models Used for local, state, and regional air quality modeling Nested Eulerian grid and plume- in-grid Criteria gases including O3, pri. & sec. PM, PM, PM components, and HAP species Full gas phase and aerosol chemistry (Carbon Bond Module) Grid and point Yes 1 km to >100 km grids (local to continental coverage with multiple grids) Few minutes to annual averaging times SCIPUFF/ SCICHEM On U.S. EPA's Alternative Models List Alternative general model Second-order closure integrated Gaussian puffs Criteria gases including O3, pri. & sec. PM, PM, PM components, and HAP species Simplified NOx chemistry and full chemistry in SCICHEM (Carbon Bond Module) Point, area, volume Yes 10 m to >100 km 10 min to 1 year averaging times

14    Table 4. Overview and Comparison of AERMOD and Alternative Model Assets and Limitations  Evaluation Criteria AERMOD CALINE4 CALPUFF CMAQ SCICHEM Tier 1 (Full Cnv.) Tier 2 ARM Tier 3 Simple 1 st Order Reactions Pseudo 1st Order Full Chemistry (CB05) CB05 Chemistry Simplified NOx Chemistry OLM PVMRM Treatment of NO- NO2-O3 Chemistry None None Yes Photostation- ary State Yes None Explicit Photochemistry Yes Explicit Photochemistry Accounts for chemical species beyond NO/NO 2 /O 3 No No No No No Yes (part of aerosl. chem.) Yes (full atmos. chem.) No Yes Model/method acceptability Good, EPA “Default “ (Tier 1) Good (EPA Tier 2) Good (EPA Tier 2) Good (EPA Tier 2) Fair (CA. Req.) Good (EPA Rec. Model) Good (EPA Rec. Reginal Model) Good (EPA Alt. Model) Good (EPA Alt. Model) Input data complexity Low Low Med. Med. Med. Med. High Med. - High Med. Computational complexity & runtime Low Low Med. Med. Med. Med.-High High Med.-High Med. Plume growth & changes in O 3 concentrations No No but ARM2 accounts No Yes Yes Yes N/A for grids, but in PinG Yes No Simulates multiple plumes No No, but ARM2 accts. for multiple plumes No, but apprx. by a source grp. feature Yes No Yes, but lmtd. by no. of sources (200) N/A for grids, but in PinG Yes Yes User-supplied ambient O 3 concentrations No No, but ARM2 accts. for bckgd. conditions Yes Yes Yes Yes Yes Yes No

15  From the comprehensive evaluation of the models/methods presented above, and the ratings of the models/methods illustrated above, the Research Team concluded that the AERMOD-based options offer the best overall solution, given the goals and objectives of the research. Therefore, the research was focused on those methods. 3.4  Performance  Characteristics  of  AERMOD,  ARM,  OLM  and  PVMRM Because AERMOD and its three-tiered methods for estimating NO2 concentrations are already part of the EDMS/AEDT software, particular empahsis was placed on the application and advancment of this software during the Research. Some of the more well-known characteristics associated with these prediction methods include: AERMOD - Again, it is important to note that the ARM, OLM and PVMRM methods for computing NO2 using AERMOD are largely based on their applications to single or multiple stationary sources (i.e., power plants, incinerators, etc.). In contrast, airports are characterized by numerous moving sources of differing sizes, shapes, and altitudes and with varying NO2/NOx emission indices and ratios. Plume models do not have the ability to handle recirculation of plumes from sea breeze effects or other diurnal/nocturnal phenomena. Moreover, plume models assume instantaneous mixing within the plume, whereas reactions (e.g., NO + O3) have finite reaction times. In addition, “pockets” of air within the plume can be either NO or O3 rich, resulting in less NO2 formation, and hence, over-predictions at the receptor location. The dispersion coefficients (sigma values) of plume models can also over- or under-predict the dispersion of emitted pollutants (including NOx). Besides resulting in incorrect predictions of total NOx, this resulting concentration error also affects the NO-NO2 inter-conversion modeling. While AERMOD is generally considered a non-chemistry model, it offers two methods for dynamically modeling NO2 formation: the OLM and the PVMRM. Notably, neither of these methods is accessible through the (formerly used) version of EDMS and it is not known when the methods will be available in AEDT.  Full  Conversion - The main limitations of the full conversion method are that it is overly simplistic and applies conservatively high ambient NO2/NOx ratios for modeling airport-related emissions.  ARM/ARM2 - In their current configurations, these two methods are also considered to be too “one-dimensional” for airport applications. This characteristic is particularly applicable to the ARM but potentially less so for ARM2 (which could be improved greatly with airport-specific NO2/NOx monitoring data).  OLM  ‐ Among the limitations with OLM is that it cannot be used to simulate multiple plumes. However, the OLM allows for the specification of multiple sources to be combined (i.e., to allow the formation of a single plume). Also, this method does not take into account the dynamics associated with plume growth and changes in O3 along the length of the plume. Because of this, the OLM is likely to produce Modeling Limitations  In general, the predictions (i.e.,  outputs) from computer models  and analytical methods involving  complex atmospheric formation  and dispersion are inherently  limited when compared to “real‐ world” conditions.  In some cases, it comes down to  picking the one(s) that are the best‐ of‐the‐worst given their intended  applications and their fundamental  limitations.   AERMOD Emphasis  Because AERMOD is already part of  the EDMS/AEDT software package  and is an EPA‐approved model for  this type of application, this  Research emphasized its use, but  not to the exclusion of other  models or methods.