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

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26  there are limitations with (i.) EDMS input and/or output data (ii.) the dispersion modeling within AERMOD itself, or (iii.) the inputs for AERMOD model were insufficient or inaccurate (e.g., inputs of NOx emissions, meteorological inputs, etc.).  Variable Results  - Generally speaking, at lower NOx values, the PVMRM w/variable and OLM w/variable correlate with the monitored concentrations best. At mid-to-higher NOx concentrations, the ARM2 model mostly fit the monitored concentrations the best. The ARM2 modeled ratios also fit the “ARM2 curve” equation produced by similar studies. At higher NOx concentrations, the PVMRM w/variable provides the highest estimate of the NO2/NOx ratio. Importantly, these results could differ greatly with different NO2/NOx emission ratios (e.g., the OLM and PVMRM would predict lower NO2 concentrations). Using the NO2/NOx emission ratio of 0.5, the OLM and PVMRM methods all produced maximum NO2 concentrations that were much higher than any of the measured NO2 concentrations. By comparison, the maximum NO2 concentrations produced by the ARM2 method were significantly lower for all datasets (note that the ARM2 method is insensitive to the NO2/NOx emission ratio).  Multiple Plumes - By the very nature of airports, this assessment incorporated multiple emissions sources and plumes in the modeling. The merging of multiple plumes in the PVMRM method has been found to lead to discontinuities in the model predictions.  Source Apportionment  - The modeling results suggest that the EDMS/AEDT/AERMOD system may be attributing disproportional impacts for different airport sources. For example, emissions from GSE operating near the terminals had a greater contribution to receptor NO2/NOx values even though the airport runway/taxiway systems were closer.  Volume  Sources  - Area vs. volume sources were analyzed under Task 6 for ADL. The findings did not reveal any remarkable advantage for using volume sources, particularly considering the number of individual airport sources of emissions. It is believed that one of the “root” causes of the overall poor correlation with the case-study airport data is that none of the monitoring data were originally collected with the goal of comparing the monitoring to modeling data. For example, much of the monitoring data was collected at locations and over time periods that may (or may not) coincide with airport-related emission amounts or meteorological conditions that are conducive to NO2 formation. 5.0 Modeled vs. Modeled NO2 Comparisons  For the purposes of this Research Project, “modeled-to-modeled” comparisons of NO2 values were also made. This aspect of the Research was conducted to identify trends and outliers (e.g., to see if one model behaves significantly different from the others). It was specifically not aimed at determining or comparing model accuracy. In this case, the AERMOD methods (i.e., Tiers 1, 2, 3), along with other select alternative methods, served as a basis for determining the viability and accuracy of the Preferred Method (ARM2-Airports). These models/methods included the following:  AERMOD (Full Conversion)  AERMOD (ARM/ARM2)  AERMOD (OLM)  AERMOD (PVMRM) Monitoring Data Limitations  The availability and use of NO2/NOx  monitoring data for this Research  was mostly “opportunistic.” That is,  none of the data was strategically  collected for the expressed purpose  of improving the accuracy of  predictive models and/or methods.  This limitation likely had a bearing  on the outcomes of the statistical  analyses performed on the  modeled vs. monitored data.   

  C  S CALINE4 have mad of the grid chemistry similar re and the si For this a into EDM consisting aircraft g graphic. T also show runway/ta a constant For comp to the sou any poten located in A number the quanti existing m computing most relev  D ge th di ta m ch di di  M Co co ho N co pa co re ra                    8 Kelly, J July 19 ALPUFF (wi CICHEM (fu was not inc e it difficult -based natur modeling in sults.8 For S mple NOx con nalysis, an ai S based on of two run ate apron as he receptor n and wer xiway system wind directi arative purpo rces and in-b tial “near fie crementally f of importan tative assessm odels/metho airport-relat ant of these istance  Ver neral, the g e model res stances to th xiways). Thi odels treat emistry dif fferences stance.  orning  ncentration ncentrations urs are due Ox emissions mpared to rticular, th ncentrations flected in the tios for the                         ames T. and Kir 99.” EPA Offic th CALMET ll chemistry) luded in this to accurately e of the mod CMAQ thro CICHEM, b version opti rport layout w a simplified ways, sever illustrated i locations use e aligned p in a step-w on of 0 degre ses, recepto etween the tw ld” effects, w urther away, t findings ar ent and com ds that are ed NO2 conc are discussed sus  Concen reatest differ ults are obse e sources (e.g s is indicati near field d ferently an decrease w versus  s - The g of NO2 in in part to th during that the mornin e higher in the generally h afternoon. (T                     k R. Baker. “Plu e of Air Quality ) SCICHEM ( assessment a model airpo el which also ugh the CB oth the full c ons were asse as devised a setup of LAX al taxiways, n the accom d for the ana erpendicular ise manner a es. rs were locat o runways to hereas the o with the furth e evident fro parison of t available f entrations. T below: trations - ences betwe rved at clos ., runways an ve of how t ispersion an d how the ith recept Afterno enerally low the afterno e lower over time period ( g hours). ambient afternoon a igher NO2/N he equilibriu me Chemistry M Planning & Sta 27  simple NOx c s its reliance rt sources. CM would have mechanism hemistry ssed. nd input airport and an panying lysis are to the nd under ed close capture thers are est receptor m he or he In en er d he d se or on  er on all as In O3 re Ox m odeling with S ndards. 10th Con M 0 50 100 150 200 250 R1 N O 2 Co nc en tr at io n ( pp b) 0 0.2 0.4 0.6 0.8 1 1.2 M od el ed  Am bi en t N O 2/ N O x Ra tio onversion) on the highw AQ was al made compa is replicated approximatel CICHEM and C ference on Air odeled‐to‐M R2 R3 R4 R5 R Ex ARM2 R9 R10 R11 Exam ay source m so not includ risons diffic in SCICHEM y five miles MAQ: Cumber Quality Modelin odeled Airp 6 R7 R8 R9 R10 R Receptor ample NO2 P   R12 R13 R14 Receptor ple NO2/NO ARM2  ixing zones w ed in part be ult. Moreove with repo away. land Power Pla g. March 14, 20 ort Layout Sc 11 R12 R13 R14 R15 R AERMOD‐PV AERMOD‐O AERMOD‐AR AERMOD‐AR SCICHEM‐Fu SCICHEM‐Si CALPUFF AERMOD‐Fu SCICHEM‐Fu SCICHEM‐Si CALPUFF, To lot  R15 R16 R AERMO AERMO AERMO AERMO SCICHEM SCICHEM CALPUF x Plot   ould cause r, full rtedly nt on 6 12.  heme 16 R17 R18 MRM LM M M2 ll Chem mple ll Conversion ll Chem, Total NOx mple, Total NOx tal NOx 17 R18 D‐OLM D‐PVMRM D‐ARM D‐ARM2 ‐Full Chem ‐Simple F

le R O ap am  CA Co pl A ot pa co m ge as lo  A ar O th T re w le an co m  P un an O O 9 Environ Demon Standa 10 Karam at the 1 11 Karam “Appli Quality 12 Chowd SCICH 2013. 13 Enviro Prepare 14 MACT MACT 15 RTP E Modeli 16 Environ Demon Standa vel is quick eceptor 11 l 3 concentrati pears to resu ong the diff LPUFF  an nservative ots show tha RM are mo her methods st studies nservative n ethods.9 Th nerated by th well.10, 11, 12 west.) RM2 is Less  e shown by LM, and AR ese other me he ARM2 co ceptors close hich are bas vels. Althoug d OLM, tha ncentrations ethod shows lume Mergin derstanding d lower NO LM-grouped 3 is available mental Protecti strating Compli rds. 2010. chandanib, Prak 1th Annual CMA chandani, P., R. cation of SCICH Models: The P hury, B., I. Syke EM.” Presented n. “Evaluation d for EPA Offi EC. Sensitivity EC Federal Pro nvrionmental A ng, Developmen mental Protecti strating Compli rds. 2010. ly reached ocation.) Als on in the a lt, in part, in erent method d  SCICHEM - The NO2 t the PVMR re conserva . This is co that have ature of the e lower e more refin , 13 (SCICHEM Conservative the average m M are compa thods (see ac ncentrations to the runw ed on regres h past studie t conclusion paired in spa similar conce g/Grouping that the grou 2 concentratio option tend t (i.e., as indic on Agency (EP ance with the N ash, et al. “App S Conference, Morris, B. Bra EM for Near-F ath Forward Con s, D. Henn, N. K at the AWMA of Chemical Di ce of Air Quality Analysis of PV grams, Inc., Res ssociates, Inc. t and Evaluatio on Agency (EP ance with the N at about th o, the highe fternoon hou less diversit s.   Are  Les concentratio M, OLM, an tive than th nsistent wit indicated th se AERMOD concentration ed methods produced s  than Other  odeled-to-m rable wherea companying were also m ays. This ap sing data fro s have indica is mainly b ce and time ntration leve - Exercising ping of sourc ns. Similar t o merge quic ated by the a A). Memorandu O2 National Am lication of SCIC Chapel Hill, NC shers, G. Yarwo ield and Far-Fie ference, Raleig umar, E. Knipp Guideline on Ai spersion Models Planning and S MRM and OLM earch Triangle P Ambient Ratio n Report. Prepa A). Memorandu O2 National Am 28  e r r y s  n d e h e s in CALPUFF ignificantly AERMOD Me odeled ratios s ARM2 app figure). easurably lo pears to refl m EPA mo ted that ARM ased on com as well as sen ls at receptor the OLM gro es (i.e., plum o the other m kly with dist fternoon hou m. Clarificatio bient Air Qual HEM-2012 for . October 2012 od, L. Parker, E ld Single Sourc h, NC, March 2 ing, and P. Kar r Quality Model using Atmosph tandards. EPA in AERMOD. ark, NC. 2004. Method Versio red for the Ame m. Clarification bient Air Qual Avera and SCICH lower results thods Close of NO2/NO ears to be som wer than the ect the origi nitoring sites 2 may be m paring perc sitivity stud locations fur uping option e merging) re ethods, the ance from th r). n on the Use o ity Standard. EP 1-Hour NO2 C . . Knipping, N. e Impacts.” Pres 013. amchandani. “C s: The Path For eric Plume Me Contract No. EP Final Report, A n 2 (ARM2) f rican Petroleum on the Use o ity Standard. EP ge NO2/NOx EM also supp while CALP  to Sources - 2 which indic ewhat more other AERM ns of the AR representati ore conserva entile-type v ies.14, 15, 16 Ho ther away fro s showed co sults in less predictions f e source, esp f AERMOD Di A Office of Ai oncentration As Kumar, B. Cho ented at the AW omparative Stu ward Conferenc asurements from -D-07-102. Sep laska DEC Con or use with AE Institute. Septem f AERMOD Di A Office of Air  Ratio Comp ort these fin UFF produce  These differ ate that PVM conservative OD method M2 method ve of backg tive than PVM alues rather wever, the A m the runwa nsistency wi O3 being ava rom the OLM ecially when spersion Model r Quality Planni sessments.” Pre wdhury, and I. MA Guideline dies Using e, Raleigh, NC, Field Experim tember 2012. tract No. 18-80 RMOD for 1-h ber 20, 2013. spersion Model Quality Planni ared to PVM dings d the ences RM, than s for ology round RM than RM2 ys.  th the ilable and more ing for ng and sented Sykes. on Air March ents.” 18-04. r NO2 ing for ng and RM