Development of a NOx Chemistry Module for EDMS/AEDT to Predict NO2 Concentrations (2017) / Chapter Skim
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Appendix A Interim Report
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ACRP 02-43: Development of a NOx Chemistry Module for EDMS/AEDT to Predict NO2 Concentrations Task 9 Interim Report Prepared for: Airport Cooperative Research Program (ACRP) Transportation Research Board of The National Academies Michael Kenney, Principal Investigator KB Environmental Sciences, Inc.
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A ‐ ii Executive Summary I Purpose of the Report The purpose of this Interim Report is to (i.)
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A ‐ iii Background Concentrations - Time-varying background concentrations of NO2 and NOx have not been fully applied to modeling results. Further assessment of the non-airport sources of these compounds is warranted.
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A ‐ iv ACRP 02‐43 Research Participants Research Team: Michael Kenney, Principal Investigator, KB Environmental Sciences (KBE) Carrol Fowler, Administrative Officer, KBE Cristina Schoonard, Air Quality Specialist, KBE Paola Pringle, Air Quality Specialist, KBE Rick Miake-Lye, Principal Air Quality Scientist, Aerodyne Scott Herndon, Air Quality Scientist, Aerodyne Ezra Wood, Aircraft Engine Emissions Specialist, Univ.
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A ‐ v Table of Contents 1. Purpose of the Report ........................................................................................................................... 1 2.
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A ‐ 1 1. Purpose of the Report The principal objective of this Interim Report prepared in support of the Airport Cooperative Research Program (ACRP)
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Within A applying methods. include: A U U U Unfortuna (i.e., calib characteri result, the the availa pressing n 2.2 Resea Based upo computed Research A FA N D Pr ai The produ Module fo computer utilizing underlyin alternative 2.3 Work Consisten Team Pro for this P tasks.
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A ‐ 3 ACRP 02‐43 Amplified Work Program Task 1, Regulations & Standards – The primary aim of this task was to compile, evaluate and summarize what is presently known or currently under study on the combined topics of air quality and the new 1-hour NO2 NAAQS. Task 2, Current Understanding & Research Gaps – This task described the current understanding and research gaps relative to measuring and predicting NO2 concentrations in airport environments.
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A ‐ 4 3. Tasks 1, 2 & 3 Refresher The outcomes of Tasks 1, 2 and 3 were published previously in the Task 4 Working Paper and discussed during a subsequent webinar.
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A ‐ 5 these new NOx tailpipe standards for light-duty vehicles represent an 80 percent reduction per fleet average. Nonroad Engines – U.S.
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A ‐ 6 Dedicated calibration results show that when properly operated the catalytic conversion of NO2 to NO can be near unity, but other compounds besides NO2 are also converted to NO via this process (e.g., nitric acid [HNO3] , peroxyacetyl nitrate [PAN]
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A ‐ 7 beds and at conditions outlined in a certification standard. Importantly, the certification standard has been set for total NOx emissions, and not for the individual species of NO and NO2. Table 3.1 provides a summary of the NOx emission indices for aircraft engines currently within the EDMS/AEDT models.
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A ‐ 8 3.2.4 NO2/NOx Emission Ratios Extensive NO, NO2 and total NOx emissions testing has been conducted on a wide range of aircraft engines in the last decade.12,13,14,15 From this, a few of the most important points regarding actual aircraft NO and NO2 emissions and how they are handled in EDMS/AEDT/AERMOD are listed as follows: ICAO Values - Total NOx emission indices from aircraft engines agree reasonably well with the ICAO certification values, although the actual thrust values used can greatly affect NOx/NO2 values, as discussed above. Thrust Settings ‐ In contrast to most other NOx emission sources (e.g., gasoline vehicles, coal combustion)
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A ‐ 9 Table 3.2. NO2/NOx Emission Ratios for Various Airport Emission Sources Source NO2/NOx Ratio Aircraft - Approach ~0.16 Aircraft - Idle 0.6 to 0.98 Aircraft - Takeoff ~0.08 Aircraft - Climbout ~0.09 APU 0.25 to 0.55 Gasoline Vehicles 0.01 to 0.05 Diesel Vehicles 0.05 to 0.20 Diesel Vehicles with DPM 0.15 to 0.80 Gasoline GSE 0.05 Diesel GSE 0.05 to 0.90 Stationary Engines (Diesel) 0.06 Stationary Engine (CNG)
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A ‐ 10 NO + O3 → NO2 + O2 NO can also be converted to NO2 by reaction with hydroperoxy radicals (HO2) and organic peroxy radicals (RO2, where "R" represents a carbon-based radical like CH3•)
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A ‐ 11 each source. Along with meteorological data, the proper characterization of each source's position information can be critical.
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A ‐ 12 According to information reported in the Task 4 Working Paper, 1-hour averaged NO2 concentrations at all monitoring sites within "metropolitan statistical areas" between 2003, 2004 and 2005 were 5, 12, and 38 ppb, respectively. Among the U.S.
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A ‐ 13 deposition or wet/dry depletion of the plume. AERMOD is commonly executed to yield 1-hour and season average concentrations (in µg/m3)
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A ‐ 14 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., ten percent)
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A ‐ 15 methods is accessible through the current version of EDMS and it is not clear when the methods will be available in AEDT. ARM/ARM‐2 - The main limitations of the ARM/ARM-2 methods are that they are overly simplistic and utilize conservatively high ambient NO2/NOx ratios.
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A ‐ 16 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)
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A ‐ 17 4. Comparison of Emission Factors to Measured Data (Task 5)
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A ‐ 18 Table 4.1. EDMS/AEDT NOx Emission Indices (grams per kilogram of fuel) Engine Type Operating Mode/ Thrust Settings Maximum Minimum Average Jet Engines Takeoff (100%)
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A ‐ 19 Table 4.2. NOx Emission Indexes and NO2/NOx Ratios Derived from Measured Data With the exception of the CJ610 engine, which is the only turbojet engine (the others are all turbofan engines) , the NO2/NOx emission ratio decreases from values between 70 and 95% at the lowest thrust setting studied (e.g., idle)
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A ‐ 20 appear to be closer to 4% thrust (approximately 20% lower fuel flow rate) , and thrust settings for takeoff are closer to 85% than to 100% thrust as a result of "reduced thrust takeoff".
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A ‐ 21 Table 4.3. Comparison of Emission Factors to Measured Data Parameter Currently Used in EDMS/AEDT Published Measurements Aircraft NOx Emission Indexes ICAO values At the same thrust, measurements agree to within ~10% Aircraft NO2/NOx Emission Ratio For the Full Conversion and ARM methods, none is needed. For OLM and PVMRM, there is no default value.
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A ‐ 22 4.3 Non‐Aircraft Emission Factors Although aircraft are the biggest source of NOx emissions at airports, other sources of NOx emissions are not insignificant. These include ground access vehicles (GAV)
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A ‐ 23 regulations. Nevertheless, it is likely that NO2/NOx emission ratios exhibit the same overall trend as those from on-road vehicles (i.e., NO2/NOx < 0.05 for gasoline-fueled GSE and 0.05 to 0.9 for diesel-fueled GSE depending on the presence of post-combustion control technology)
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5. Comp This secti Amplified 5.1. Task As discus following C ac This obje comprised These air based upo medium-t activity le ambient N modeling located in airports e Research necessary The follo and mode these airp 5.2 Air M For this a concurren collected note that for this support o These dat 5.2.1 A ADL is lo classified Mediterra mountains arison of M on discusses Work Plan. Objective sed in Sectio : ompare mod tivity levels, ctive was ach the followin Los Angel Adelaide ( Montreal ( Internation ports were n two "key o-large comm vels deemed O2/NOx con and (ii.)
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The NO2/ located ad 5.2.2 Lo LAX is lo California (667,000 LAX is c the winds the nearby The NO2 analysis w monitorin Quality a and (ii.)
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5.2.4 Tim The air m airports assessmen periods an is beca independe purposes. YUL wer air monit from LAX special st recently-a collected periods monitored Table 5.1 A Adelaide ( Los Inte ( Montreal ( 5.2.5 Qu This secti the case-s e Periods onitoring da identified ab t were coll d with differ use these ntly from ea For example e collected a oring program which was udies.
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A ‐ 27 Data Censoring ‐ A data censoring process was applied to the monitored NO2, NO, and NOx datasets. Essentially, this involved changing any negative values in the raw datasets to zero values.
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A ‐ 28 simulate point, area, volume, and line sources and has the capability to include simple, intermediate, and complex terrains.39,40 It also predicts both short-term (1 to 24 hours) and long-term (quarterly or annual)
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5.3.2 So Using ED based upo and time p For disper Point sour sources w activity) ; out and a sources.
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A ‐ 30 (SODAR) system.
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A ‐ 31 span of 10 years. The relationship is empirically derived between the upper limits of the observed NO2/NOX ambient ratio vs.
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A ‐ 32 The PVMRM allows a "Beta" option to implement Prevention of Significant Deterioration (PSD) increments for any source(s)
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5.4.4 B Box and depictions block of t notated w (Q3)
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5.4.5 H Histogram range of v each testthrough 5 istograms plots are u alues) within case airport w .10, respectiv seful in dete a given dat ere prepared ely. Figure 5.6. YU rmining a di aset.
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Figure 5.8. H Figure 5.9. H A ‐ 35 istogram of Y istogram of UL NOx. YUL O3.
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5.4.6 Scat Comparis inter-varia A A A In N N An examp ter Plots: Int ons of monito ble influence s NOX increa s O3 increase s O3 increase most cases, In som be rela o clear relatio o clear relatio le scatterplot Figure er‐Variable I red NO2, NO s between th ses NO2 in s NO2 dec s NOX dec the NO2/NOX e cases at LA ted to roundi nship betwe nship betwe matrix for th 5.10. Histog nfluence X, and O3 da ese paramete creases reases reases Ratio decrea X AQSAS, ng/quality as en NO2 and N en O3 and NO e YUL datas A ‐ 36 ram of YUL N ta at the testrs. In general ses as NOx in NO2/NOX Ra surance issue O2/NOX Rati 2/NOX Ratio et is provided O2/NOx Rati case airports , these scatte creases.
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5.4.7 Tim Example shown, so Novembe several ho this time p evident by However, periods, concentra the NO2 vehicles) the form o of O3, w concentra e Series Data time series d me of the hig r 2010.
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A ‐ 38 Much higher concentrations of NO2 (e.g., greater than the 1-hour air quality standard of 100 ppb) can only form with higher primary NO2 emissions (i.e., from an emission source with a high NO2/NOx emission ratio, such as idling aircraft)
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A ‐ 39 5.5 Monitored Versus Modeled NO2 Concentrations Assessment This section discusses the assessment of the monitored and modeled NO2 values for the three "test-case" airports derived using the NO2/NOx conversion methods discussed above. For the purposes of this assessment, the ten highest modeled 1-hour NO2 concentrations were examined.
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A ‐ 40 Table 5.5. Top 10 Monitored NO2 Concentrations (ppb) at LAX AQSAS Sites Rank Monitoring Station AQ CE CN CS AQ CE CN CS Season 1 Season 2 1 62 64 68 69 32 43 46 29 2 59 63 65 62 27 43 41 28 3 56 61 63 60 27 41 41 28 4 54 61 61 60 27 40 40 26 5 54 61 61 59 27 39 40 26 6 54 59 60 59 26 38 39 26 7 53 58 60 58 26 38 39 24 8 53 58 60 56 26 37 37 24 9 52 58 59 55 25 37 37 24 10 52 58 59 55 25 37 37 23 5.5.2 Modeling Data The ten highest modeled NO2 concentrations for the six NOx conversion methods are presented below in Tables 5.6 through 5.9.
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A ‐ 41 Table 5.6. Top 10 Modeled NO2 Concentrations (ppb) for ADL Rank Conversion Method Full Conv. ARM2 OLM OLM w/ Variable PVMRM PVMRM w/ Variable 1 454 91 454 454 454 454 2 310 84 186 182 279 279 3 278 81 163 147 237 237 4 263 80 147 145 225 225 5 250 79 147 143 212 212 6 236 78 146 139 195 195 7 216 77 143 139 184 184 8 204 76 143 131 181 181 9 202 76 132 127 156 155 10 172 76 131 120 155 143 Highest monitored concentration was 56 ppb.
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A ‐ 42 Rank Conversion Method (Continued) Full Conv. ARM2 OLM OLM w/ Variable PVMRM PVMRM w/ Variable 9 1026 205 553 453 802 802 10 946 189 513 433 774 679 CN Station 1 1854 371 967 996 1478 1480 2 1479 296 779 812 1331 1331 3 1414 283 747 714 1273 1273 4 1341 268 710 712 1207 1207 5 1335 267 704 691 1202 1202 6 1278 256 679 659 1122 1122 7 1247 249 664 636 1024 983 8 1092 218 589 615 983 982 9 1091 218 585 613 982 970 10 1045 209 562 571 941 941 Highest monitored concentrations ranged from 52 - 69 ppb.
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A ‐ 43 Table 5.9. Top 10 Maximum NO2 Concentrations (ppb) for YUL Rank Conversion Method Full Conv. ARM2 OLM OLM w/ Variable PVMRM PVMRM w/ Variable 1 1100 220 578 595 885 990 2 987 197 530 560 824 889 3 984 197 518 532 797 885 4 916 183 498 530 793 824 5 842 168 455 476 733 733 6 814 163 447 468 655 723 7 728 146 394 408 644 655 8 725 145 390 400 640 652 9 648 130 355 390 583 583 10 581 116 310 333 523 523 Highest monitored concentrations ranged from 61 - 71 ppb.
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A ‐ 44 Mean Squared Error (MSE) – The MSE calculation is an important statistical test that is used to examine the performance of a model and is a measure of the squares of the departure from monitored values.
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LA Figure X AQSAS – F AQ (Fi predic concen CE (Fig predic CN (Fig over-p concen concen other m CS (Fig monito 5.14. Q‐Q P or this case, gure 5.15) : A t, with the trations.
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Figur Figu e 5.15. Q‐Q re 5.16. Q‐Q Plot of Moni Plot of Mon tored Vs. Mo itored Vs. Mo A ‐ 46 deled NO2 C deled NO2 C oncentration oncentratio s: LAX AQSA ns: LAX AQSA S: Season 1 – S Season 1 – AQ. CE.
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Figu Figu LA pr re 5.17. Q‐Q re 5.18. Q‐Q X SPAS (Fig edict, and at Plot of Mon Plot of Mon ure 5.19) : A high concent itored Vs. Mo itored Vs. Mo t low moni rations, they A ‐ 47 deled NO2 C deled NO2 C tored NO2 c tend to over-p oncentration oncentratio oncentration redict.
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Y hi The gener 51 RTP En Modeli http://w Figure 5. UL (Figure 5 gh concentra Figure al distributio vironmental As ng: Developmen ww.epa.gov/scr 19. Q‐Q Plot .20)
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5.5.3.2 Sc For this an time mon model con under-pre presented A A O to e LA 52 Paine, R Evalua 53 Paine, R Evalua 54 Podrez atterplots: P alysis, mode itored ratios. version meth dictions occu and briefly d DL (Figure 5 DL dataset.
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Fig Fig CN Se almos As a were 1:1 li other ure 5.22. Sca ure 5.23. Sca ason 2 (Figu t 0 to 6.35 (n result, no val found in this ne, indicating conversion m tterplots (Pa tterplots (Pa re 5.23) : Mo ote: modeled ues were exc dataset.
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Again, the cited prev investigat 5.5.3.3 Sc For this a concentra they plot and descri A m th e p F LA 55 RTP En Modeli http://w general resu iously in th ion. atterplots: U nalysis, mod tions were pl monitored da bed below: DL (Figure 5 onitored con e monitored quation repro roduces the h igure 5.24. S X AQSAS – F CE (Fi genera conce higher NO2/N CN (F variab vironmental As ng: Developmen ww.epa.gov/scr lts generated is section, a npaired in T eled and mon otted.
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Figu Figu conce higher NO2/N re 5.25. Scat re 5.26. Scat ntrations, the NOx conce Ox Ratio. terplots (Unp terplots (Unp ARM2 meth ntrations, th aired In Tim aired In Tim A ‐ 52 od seems to e PVMRM w e)
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L v N h R Figu Y m N h N AX SPAS (Fig ariable are th Ox concentr igher NOx c atio. re 5.27. Sca UL (Figure 5.
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Fig The result earlier in findings o ambient ra 5.5.3.4 R The RHC observed For demo represents column re divided b NOx to NO 56 RTP En Modeli http://w ure 5.28. Sca s generated b this section. f previous re tio curve inh HC statistic is a NO2 concentr RHC = X(N Where N = th N = 26 X(N)
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A ‐ 55 As shown, the RHC results indicate that the ARM2 has the lowest over-predicted ratio concentration when compared to monitored ratios concentrations. PVMRM is greatly over-predicting ratios when compared to monitored ratio concentrations.
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For this a reported a 5.5.4.1 W Figure 5.2 plot show with prev Previous r Figure 5 5.5.4.2 W The effec higher ov the termin conversio 57 ACI Eu 58 Carslaw Contrib 59 Langw Manag nalysis, an u nd therefore ind Speed 9 provides a s that as win ious studies eports have a .29. YUL Mo ind Direction t of wind dir erestimated v al, as the te n methods as rope. Effects of , David, Sean B utions to Ambie orthy, Lucinda ement, 2012. pper NO2 li NO2 values a scatterplot o d speed incre which have a lso illustrate deled Conce ection indica alues than ot rminal is sou shown in Fig Air Traffic on A eevers, Karl Ro nt Nitrogen Ox Minton.
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5.5.4.3 Te As shown temperatu Figure 5 mperature in Figure 5. re on the NO .30. Boxplots 31, the asses 2/NOx conver of Modeled sment of thi sion method A ‐ 57 NO2 ‐ Monit s variable ind s.
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5.5.4.4 R The analy influence 5.5.5 Sou Although of the th concentra For exam mode) , "g each of th Noticeabl A 75 W ai Figu elative Humi sis of relati from this var rce Apportio emissions fro ree test-case tions based o ple, from the ates" (compr e three airpor e outcomes o t ADL and Y %)
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A ‐ 59 Table 5.12. Modeled NO2 Concentrations Apportioned by Sources Airport Sources Full Conversion ARM2 ug/m3 % ug/m3 % ADL (July 1 ‐ Dec 31 2011) Aircraft Approach 5E-03 0 3E-03 0 Takeoff 40.7 17 23.1 17 Landing 1.4 1 0.8 1 Taxi 10.4 4 5.9 4 Gates (GSE & APUs)
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A ‐ 60 5.6 Conclusions and Observations As demonstrated in this section, the results of Task 6 reveal that modeling NO2 concentrations near airports using existing models and NOx/NO2 conversion methods is characterized by a number of significant complications and shortcomings. The following highlights some of the most noteworthy of these along with some potential remedies.
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A ‐ 61 Multiple Plumes – By its very nature, this assessment incorporated multiple emissions sources and plumes in the modeled. The merging of multiple plumes in the PVMRM method has been found to lead to discontinuities in the model predictions and this effect should be further considered when examining results presented in this study.
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A ‐ 62 6. Research Plan (Task 7)
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A ‐ 63 Model Properties & Characteristics U.S. EPA Classification – This refers to EPA's Preferred/Recommended and Alternative Models (i.e., those included on the EPA's Support Center for Regulatory Atmospheric Modeling) – a potentially important aspect when evaluating models that have undergone the agency's "vetting" process.
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A ‐ 64 Table 6.1. Overview & Comparison of AERMOD and Alternative 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 U.S. EPA's Preferred / Recommended List Used for most regulatory point source assessments.
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A ‐ 65 6.2.2 Discussion of Model Properties and Characteristics 6.2.2.1 Overview As shown above in Table 6.1, AERMOD and CALPUF are both listed among the EPA's Preferred/Recommended Models63 – a potentially important aspect when evaluating models that have undergone the agency's "vetting" process and have been deemed acceptable for regulatory applications. CMAQ and SCICHEM are also models sanctioned by EPA, but not as highly or appropriately.
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A ‐ 66 surrounding atmosphere. The method is fully integrated with the mixing zone concept used in all of the CALINE-based models where pollutants are assumed to be fully mixed within the mixing zone (the immediate area surrounding the roadway)
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A ‐ 67 models, CMAQ65 and the Comprehensive Air Quality Model with extensions (CAMx) ,66 which have typically been used to predict regional O3 formation.
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G tu T be ac co In se st th ac oc tr O da sa aussian puff rbulence clos A + ζB where, A and Pi = Pr ζ and K = R he chemistry tween reacti count result mputationall order to si parated into ate with NO, e plume whe ids. Stage 3 curs requirin ansition crite H for the Sta y or night c mple set of r Source: EPRI equation, an ure theory.
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A ‐ 69 In addition to the use of the full CB chemistry, SCICHEM also allows the use of an optimized set of simple first-order NOx reaction equations that simplifies the input needs for modeling NO2 concentrations as well as runtime. The use of this option is generally suitable for near-source scenarios with simplifying assumptions such as less mixing with background air.
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A ‐ 70 Evaluation Criteria AERMOD CALINE4 CALPUFF CMAQ SCICHEM Tier 1 Full Conversion Tier 2 ARM/ARM2 Tier 3 Simple 1st Order Reactions Pseudo 1st Order Reactions Full Chemistry (Carbon Bond) Staged Chemistry OLM PVMRM Use of photostationary state reactions No No (but ARM2 does indirectly)
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A ‐ 71 Evaluation Criteria AERMOD CALINE4 CALPUFF CMAQ SCICHEM Tier 1 Full Conversion Tier 2 ARM/ARM2 Tier 3 Simple 1st Order Reactions Pseudo 1st Order Reactions Full Chemistry (Carbon Bond) Staged Chemistry OLM PVMRM Plume segmentation and/or region modeling No No, but ARM2 inherently accounts for plume evolution No No, but plume changes No No N/A for grids, but in PinG Yes User‐supplied NO2/NOx ratios by source and operational mode No No (only NOx concentration for ARM2)
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A ‐ 72 6.2.3.2 Key Findings A number of important findings are evident from the qualitative evaluation and comparison of the existing models/methods that are available for computing airport-related NO2 concentrations. The most relevant of these are discussed below: Photostationary state reactions - With the exceptions of AERMOD Tiers I (Full Conversion)
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A ‐ 73 user-specified O3 concentrations, the regression methodology inherently accounts for background conditions. Plume segmentation ‐ Plume segmentation and plume region modeling is similar to accounting for changes in plume characteristics (e.g., plume growth, O3 concentrations, etc.)
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A ‐ 74 depends on whether the input source characteristics (e.g., emission factors, location, etc.) , weather data, background concentrations, etc.
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reportedly options w 6.2.4.2 M For this an airport co The opera T O A W W From EDM resulting OLM, and modeling the defaul ambient e For the ai types wer weighted 69 Kelly, 6 July similar resu ere exercised odeling Scen alysis, an ai nsisting of tw Figu tional and m ime Periods: perations: 20 ircraft: Mixtu ind speed: 2 ind direction S, the AER in area sourc PVMRM m these method t ambient NO quilibrium N rcraft engine e correlated ratios by mod James T
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A ‐ 76 Idle: 0.768 Approach: 0.147 Climbout: 0.070 Takeoff: 0.061 The mode of each area source was identified based on the placement (runway or taxiway) and height of the each source.
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6.2.4.3 Di The result N co m th te S F th by A th di ch A co th scussion of R s of the quan O2 Concentr ncentrations ethod results e other meth nd to produ CICHEM me or the PM per e AERMOD the OLM ERMOD me e CALPUFF fferences in emistry. long with t ncentrations e total NOx Figure 6.3. R esults titative asses ations - Figu for the AM in the highe ods tested.
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A ‐ 78 ARM indicate most of the NOx is in the form of NO2 while the other methods generally indicate a much smaller portion being due to NO2 for the receptors that are closer to the source. For the afternoon hour, the NO2 from the AERMOD methods account for a relatively large proportion of NOx similar to the morning hour.
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A ‐ 79 Figure 6.5. Modeled‐to‐Modeled Comparisons of NO2 Concentrations (PM Period) . NO2/NOx Ratios - Figures 6.6 and 6.7 present the ambient NO2/NOx ratios for R9 through 18.
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A ‐ 80 Figure 6.7. Modeled Ambient NO2/NOx Ratios (PM Period) . For ease of review, Table 6.3 provides a direct comparison of average modeled-to-modeled ratios of (NO2 to NO2 ratios)
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A ‐ 81 different sources) to compete for the available O3.
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A ‐ 82 6.2.4.4 Key Findings A number of important findings are evident from the quantitative assessment and comparison of the existing models/methods that are available for computing airport-related NO2 concentrations. The most relevant of these are discussed below: Distance Versus Concentrations – In general, the greatest differences between the model results are observed at closer distances to the sources (e.g., runways and taxiways)
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A ‐ 83 ARM2 is Less Conservative than other AERMOD Methods Close to Sources – The differences are shown by the average modeled-to-modeled ratios of NO2/NO2 which indicate that PVMRM, OLM, and ARM are comparable whereas ARM2 appears to be a little less conservative than these methods. The ARM2 concentrations were noticeably lower than the other AERMOD methods for receptors close to the runways.
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A ‐ 84 6.3 Research Plan Evaluation Methods & Criteria This section provides the Research Team's recommended approach to evaluating and developing an alternative method(s) for predicting NO2 concentrations from airport-related emission sources and recommending a Preferred Method.
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A ‐ 85 ARM2 performs similar to the Tier 3 methods and for close receptor locations, may perform similar to the more refined CALPUFF and SCICHEM methods. Furthermore, while the methods in CALPUFF and SCICHEM may be more detailed and scientifically acceptable than ARM2, the CALPUFF and SCICHEM methods are closely tied (dependent)
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The sensi variable ( regression S Id in R de Pr id The plan evaluation measured good corr possible i statistics sense.
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A ‐ 87 In addition to the paired modeled-versus-measured comparisons, various other statistics will be used including those representing non-paired datasets, such as: Summary Statistics - Summary statistics are the typical statistics used to describe the population of concentrations such as the mean, standard deviation, min, max (highest) , 2nd highest, etc.
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In accord managed (VS.NET) module w programm 6.3.4.2 AE The docu computati A m co (o In do of ex Figure 6.11 ance with FA .NET Dyna developmen ith clear spe ing language DT Module D mentation re onal modules lgorithm Des ethodology i ncepts the i nly develope terface Con cument is sp the module ample shown . High‐Level A's AEDT mic Link L t environme cifications fo can be used ocumentati quirements : ign Documen n a concise mplemented rs)
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A ‐ 89 Table 6.4. Example Input Parameter List Parameter Precision Type Units Min Max Description Temp Double Kelvin -- -- - -- -- - Temperature O3 Double ppb -- -- - -- -- - Ozone Concentration NOx Double ppb -- -- - -- -- - State/condition NO2NOx Double -- -- - -- -- - -- -- - Exhaust NO2/NOx ratio Each computational module is essentially a programmable object once it is referenced into the development environment (VS.NET)
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A ‐ 90 7. Independent Technical Review Plan (Task 8) This section provides an overview and summary discussion of the outcomes of Task 8.
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A ‐ 91 John Pehrson, CDM Smith, Irvine, CA Engineer with extensive experience on the development and application of NO2/NOx ratios and the use of EDMS/AEDT. Vincent Tito, Epsilon Associates, Maynard, MA Meteorologist with extensive experience on the development and application of NO2/NOx ratios and the use of EDMS/AEDT.
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A ‐ 92 Table 7.2. ACRP 02‐43 Independent Technical Review Response Form Template ACRP 02‐43 Independent Technical Review Response Form Reviewer Information Name: Affiliation: Contact Information: Date: General Feedback Overall Summary Provide comments highlighting the overall achievements and shortcomings of the research and its contribution to improving methods for predicting NO2 concentrations from airport NOx emissions. Response: Overall Progress Provide feedback on the overall progress of the research (check one)
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A ‐ 93 Task 6, Compare Modeled & Measured NO2 Data Under this task, a comparison was undertaken of modeled and measured NO2 data at a sample of airports representing various fleets, activity levels, geography, and meteorological conditions. Overall Assessment Has the research achieved the Task objective(s)
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A ‐ 94 As discussed above, the ACRP 02-43 Panel's review comments on the ITR Feedback Form Template will be obtained at the Interim Report Meeting and any necessary modifications will be made before it is distributed to the ITR Panel. 8. Issues in Need of Resolution The preceding sections of this Report have identified some issues associated with the ACRP 02-43 research conducted thus far that are in need of resolution.
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A ‐ 95 Issue No. 5: Source Apportionment - Although emissions from aircraft landing / takeoff cycles are the largest source of NOx emissions at each of the three test-case airports they are not necessarily the biggest contributor to predicted NOx and NO2 concentrations based on the modeling. Proposed Resolution – (None presently proposed)
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A ‐ 96 REFERENCES Chowdhury, B., I Sykes, D
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A ‐ 97 MACTEC. Sensitivity Analysis of PVMRM and OLM in AERMOD.
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