A PRELIMINARY EVALUATION OF MODELS-3 CMAQ Using particulate matter data from the IMPROVE Network Brian K. Eder, Michelle R. Mebust and Sharon K. LeDuc* I. INTRODUCTION The Clean Air Act and its Amendments require the United States Environmental Protection Agency (EPA) to establish National Ambient Air Quality Standards for Particulate Matter (PM) and to assess current and future air quality regulations designed to protect human health and welfare. Air quality models, such as EPA's Models-3 Community Multiscale Air Quality model (CMAQ) [Dyun unci Ching, 1999], provide one of the most reliable tools for performing such assessments. CMAQ simulates air concentrations and deposition of various pollutants including PM. These simulations, which can be conducted on a myriad of spatial and temporal scales, support both regulatory assessment as well as scientific studies by research institutions. Within CMAQ is an aerosol component, or module, designed to simulate the complex processes involving PM, which is commonly separated into PM25 (particles with aerodynamic diameters < 2.5 |jm) and PMK) (aerodynamic diameters < 10 |.im). In order to determine its value to the air quality regulatory communities, CMAQ needs to be evaluated using observational data. One such evaluation, which compared visibility parameters derived from CMAQ to visibility parameters obtained from National Weather Service observations, revealed that CMAQ was able to replicate general spatial and temporal patterns [Eder et al., 2000], The current evaluation compares PM simulated by CMAQ with PM data collected by the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network. * I5rian K. tiler**. Michelle R Mehust. Sharon k LeDuc** NIZR1.. U.S. Lnvironmental Protection Agency. RT/' NC. 27711,USA. ** On assignment from Air Resources Laboratory. National Oceanic and Atmospheric Administration, RTP. NC. 27711.USA I ------- 2 IS.K. EDEIi ETAL 2. CMAQ 2.1. General description CMAQ, which is an Eulerian model, simulates the atmospheric and land processes affecting the transport, transformation and deposition of air pollutants and their precursors [Byun and Ching, 1999], CMAQ follows first principles and employs a "one atmosphere" philosophy that tackles the complex interactions among multiple atmospheric pollutants and between regional and urban scales. Pollutants considered within CMAQ include tropospheric ozone, PM, airborne toxics, and acidic and nutrient species. The model also calculates visibility parameters. 2.2. CMAQ aerosol component description The aerosol component within CMAQ, described in Binkowski [1999], was derived from the Regional Particulate Model (RPM) [Binkowski and Shankar, 1995], itself an extension of the Regional Acid Deposition Model (RADM) [Chang at al.y 1990], Particle size distributions are represented as the superposition of three lognormnl modes. PM;> panicles (also called fine particles) are represented by two modes, the Aitken and accumulation modes, each having variable standard deviations. Aitken mode particles are those with diameters smaller than about 0.1 nm. Accumulation mode particle diameters range between 0.1 and 2.5(.im. Each mode receives primary emitted material, is subject to wet and dry deposition, and may form through condensation of gaseous precursors. The two modes interact through coagulation, and the Aitken mode may grow into the accumulation mode and partially merge with it. The fine panicle species considered within the CMAQ aerosol component include sulfate, nitrate, ammonium, water, primary organic aerosols, secondary organic aerosols from anthropogenic and biogenic origin, elemental carbon, and primary aerosol material not otherwise specified. The coarse particle mode within CMAQ, representing particles having aerodynamic diameters between 2,5 and 10 jim, consists of wind-blown dust and other large particles of unspecified origin. Coarse mode particles in the model also undergo wet and dry deposition. 2.3. CMAQ model simulation characteristics The 2000 release of Models-3 CMAQ was used in this evaluation The modeling domain covers the eastern U.S. (Figure 1.), with each grid cell covering 36 km by 36 km. The entire month of June 1995 was simulated. The domain's vertical profile contains 21 layers of varying thickness. The simulation used Version 2 of the Regional Acid Deposition Model chemical mechanism (RADM2), which includes 57 species and 158 reactions, 21 of which are photolytic. The meteorological fields were derived from MM5, the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research (NCAR) Mesoscale Model [Grafl tsi a/., 1994], Emissions were processed with the Models-3 Emission Processing and Projection System (MEPPS) [Benjay el a!., 1999]. ------- A PRELIMINARY EVALUATION OF MODELS-3 CMAQ 3. IMPROVE speciated data IMPROVE is a collaborative monitoring effort governed by a steering committee composed of representatives from Federal, regional and State organizations [Pitchford and Scruggs, 2000], The network was designed to (1) establish current visibility and aerosol conditions, (2) identify the chemical species and emission sources responsible for visibility degradation, and (3) document long-term visibility trends at over 100 locations nationwide. The IMPROVE monitors collected two, 24-hour integrated samples (midnight to midnight, local time) per week in 1995, Additional information concerning IMPROVE can be obtained from a web site (vista.cira.colostate.edu/improve) maintained by Colorado State University. Unfortunately, the majority of IMPROVE monitors are located in western states; as a result, only 18 sites fell within the model evaluation domain (Figure t). All the sites are rural, except the urban WASH (Washington D.C.) site. Information concerning these sites can be found in Table 1. pf-\ SWi A m ~W J* ! 77—\ ¦'MOOS*"' • \ bowab ,>«** Hi _ \ ( 1 'ftCAP V *"\ MACAA. —77 • "i CRSM H--» SI IRQ DPBll/ '• — - r^T |f JU^WASlrtvf BRIG doso'V'^IvU y--—y' /•' ¦suiyv,' K maca>~J ¦jF.rr^ *Sr> STROMA lOKEF. { 0v=w^3^r'\ f 1A V j' - « Figure I. IMPROVE stations within the CMAQ domain used in (his evaluation. ------- I!.K. EDKU ETAl. Table I. IMPROVE sire information Code Location Long (°W) Lat (°N) Elev. (111 ACAD Acadia NP, ME 68.308 44.415 129 OWA Boundary Waters, MN 91.950 47.950 524 BRIG Brigantine NWR, NJ 74.472 39.475 9 CHAS Chassahowitzka NWR, FL 82.567 28.750 2 DOSO Dolly Sods, WV 79.205 39.143 1158 GRGU Great Gulf Wilderness, NH 71.217 44.300 439 GRSM Great Smoky Mountains NP, TN 83.987 35.710 815 JEFF Jefferson, VA 79.433 37.667 299 LYBR Lye Brook Wilderness, VT 73.123 43.243 1010 MACA Mammoth Cave NP, KY 86.075 37.277 248 MOOS Moosehorn NWR, ME 67.283 45.1 17 76 OKEF Okefenokee NWR, GA 82.117 30.765 49 ROMA Cape Romain NWR, SC 79.583 33.033 3 SHEN Shenandoah NP, VA 78.450 38.543 1098 S1-1RO Shining Rock Wilderness, NC 83.283 35.650 1621 SIPS Sipsey Wilderness, AL 87.382 34.358 279 IJPBU Upper Buffalo Wilderness, AR 93.245 35.880 723 WASH Washington, D.C. 77.063 38.932 16 4. Evaluation The scope of this evaluation was somewhat hindered by the IMPROVE network's twice-a-week sampling schedule, which when incorporated into the one-month simulation period, limited the number of comparisons. (144 possible observations (18 stations, 8 days: June 3, 7, 10, 14, 17, 21, 24 and 28)). Summary statistics for each of the 5 species included in the evaluation are found below. Scatterplots are found in Figure 2. PM,„ Organic Carbon Table 2. Summary statistics for speciated aerosol data Species N Sulfate 129 Nitrate 129 PM2.5 129 Source CMAQ IMPROVE CMAQ IMPROVE CMAQ IMPROVE Mean O'g/'n3) 4.98 4.83 0.21 0.31 9.06 12.96 129 CMAQ IMPROVE 13.74 19.40 112 CMAQ IMPROVE 1.53 2.32 CV* Wl6 71.63 62.06 55.39 73.74 50.26 44.23 47.34 M;i\. 255.77 64.28 Mean (fi«/m ) 4.82 26.09 55.64 51.48 0.005 0.25 -0.26 0.32 0.34 4.10 5.59 CoclVicienl ol Variation Bias ilclined as (IMI'ROVIi - CMAO)/C'MAO ------- A PRELIMINARY EVALUATION OF MODELS-3 CMAQ 5 n (D 1 3 £ 05 20 15 V 5 0 Sulfate y O O C" O D a ~ O s£ a 3 XP X U O oS-^ DO! nS30^ oftr'ofb^oo ~ agfe«°oDo D fe 10 Observed 20 Nitrate £ 3 0 rF C 13$ 0.5 10 15 Observed 03 20 ID 20 30 Observed 30 40 Observed ¦D % 3 "3 E ° CO 11 0: Organic Carbon 0 nO D 3D I „ c a>J \s - „D ° o „ #3 D «L 3 " 0 r Observed Figure 2. Sc.itferplots ol'observed (IMPROVE) versus simulated (CMAQ) for each species Regression line shown Units arc in MS ------- e> U.K. ICDEK F.TAL 4.1. Sulfate Examination of Figure 2 and Table 2 reveals a fairly good level of agreement between simulated and observed sulfate concentrations. The simulated mean (4.98|jg/m3), and coefficient of variation (79.16%) closely match those observed (4.83|jg/m3, 71.63%). The overall r2 is 0.63 and the regression equation is CMAQ=0.60+0.91 "IMPROVE. Given that the CMAQ aerosol module descends from models designed to address the acid rain problem, it is not surprising that CMAQ simulated sulfate concentrations agree well with observed sulfate measurements. There is, however, a tendency for CMAQ to over predict concentrations (mean bias of 0.37). Examination of the bias across space and time (not shown) reveals that it is positive at 13 of the sites and on all but two days. An inflated positive bias (2.91) was observed at the DOSO (Dolly Sods/Otter Creek Wilderness in WV) site/grid cell. This inflated bias can be attributed to very small concentrations of sulfate observed at this location (in particular on two days, 24 June and 28 June). 4.2. Nitrate Unlike sulfate, examination of Figure 2 and Table 2 reveals a very poor level of agreement between simulated and observed concentrations of nitrate. While the simulated mean (0.21 ng/nv') was relatively close to the observed mean (0.3 I uu/nv'). the coefficient of variation was not (255.77% versus 64.28%). This poor agreement is also reflected in the scatter plot, overall /•" (0.005) and the regression equation of CM AQ=0.40+0.32* IMPROVE. The model consistently underpredicts nitrate concentrations (mean bias = -0.20). Examination of this negative bias across time and space reveals that it is negative at all sites except two, GRSM (Great Smoky Mountains NP) and MACA (Mammoth Cave NP), and on half of the days. Subsequent investigation has determined that, in the CMAQ simulations used here, the ammonia emissions were too low. These ammonia under predictions no doubt contributed to the nitrate under prediction seen in the model output. Efforts are currently underway to determine more realistic ammonia emission levels, eventually allowing a more accurate model evaluation with respect to nitrate. 4.3. PMjs A reasonable level of agreement can be seen between simulated and observed PM25 concentrations (Figure 2., Table2.). The simulated mean (9.06 ng/nv1) and coefficient of variation (62.06%) reasonably match those observed (12.96 pg/m \ 55.39%). as do the various percentiles. The overall r~ is 0.55 and the regression equation is CMAQ= 1.59+0.58* IMPROVE. Because a large component of PM25 is sulfate, the good agreement seen in the sulfate evaluation lends itself to reasonable PM3 5 results. The model consistently under predicts PM2 5 concentrations (mean bias = -0.21) across time (all 8 days) and space (16 of 18 sites). ------- A PRELIMINARY EVALUATION OF MODELS-3 CMAQ 7 4.4. PMI0 Examination of Figure 2 and Table 2 reveals a poor level of agreement between simulated and observed PM|0 concentrations. Although the simulated mean (13.74 |ig/nr), and coefficient of variation (73.74%) are reasonably close to those observed (19.40 |ig/m3, 50.26%), the overall r7 is only 0.13. The regression equation of CMAQ=6.52+0.37* IMPROVE and mean bias (-0.26) further reveal the model's tendency to underpredict PM concentrations. Examination of the bias reveals that it is negative at 16 of the sites and on all but one day (24 June). The only two sites with positive bias were SHEN (0.16) and WASH (1.29). Processes in the CMAQ aerosol module that involve PM|0 need better representation. Efforts are underway to more accurately model wind-blown dust, as well as to include sea salt in aerosol dynamics. 4.5. Organic carbon CMAQ simulates organic carbon with a modes! level of agreement. The r is 0.25 and the regression equation is CMAQ=0.76+0.34*IMPROVE. As with most of the other species, CMAQ generally underpredicts organic carbon concentrations (mean bias of - 0.26). This bias is negative at 15 sites and across every simulation day; however, it is not as large as the bias seen for the other species, as 8 of the 18 -sites and 5 of the 8 days are within 25%. This modest level of agreement results partially from the crude physical representation (currently undergoing improvement) of organics within the CMAQ aerosol component. Further difficulties arise from incomplete knowledge regarding organic aerosol constituents, making it difficult not only to adequately model organic species, but to compare model results with observations. 5. Summary This evaluation compared speciated aerosol data collected during the month of June 1995 against CMAQ simulations for five species: sulfate, nitrate, organic carbon, PM:5 and PM|0. With the exception of sulfate (36%), the model simulations generally produced negative biases (model predictions too low) of between -21 and -26%. This negative bias was generally consistent across the domain and throughout the simulation period. Agreement between model simulations and observations varied considerably across species, with r's of 0.63 (sulfate), 0.55 (PM2 5), 0.25 (organic carbon), 0.13 (PM|„) and 0.005 (nitrate) Several likely sources of error in the model simulation were identified and include inadequate emissions inventories and an incomplete understanding of aerosol dynamics, especially for PM,0. Inadequacies in evaluation data sets have also been identified. Fortunately, the EPA has recently implemented the National PM2 5 Monitoring Network, consisting of mass monitoring (1100 sites), routine chemical speciation (300 sites) and ------- % b.k. i:ui:k fj al supersite characterization. These network measurements will eventually provide much more adequate data, thus allowing for a more thorough evaluation of CMAQ. 6. References Benjey, W.G., J.M, Godowitch and G.L. Gibson, Emission subsystem, in Science algorithms of the El'A Models-3 Community Multiscale Air Quality (CMAQ) modeling system, edited by D.W. Byim and J K.S. Ching, pp. 4-1 - 4-107, EPA-600/R-99/030, U.S. Environmental Protection Agency. U.S. Government Printing Office, Washington D.C., 1999. Binkowski, F.S., Tlie aerosol portion of Models-3 CMAQ. in Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system, edited by D.W. Byim ;nul J.K.S Ching pp 10-1 -10-23, EPA-600/R-99/030, U.S. Environmental Protection Agency. US. Government Priming Office, Washington DC., 1999 Binkowski, F.S and U. Shankar. The regional paniculate matter model. I. Model description and preliminaiy results. Journal of Geophysical Research, 100, 26.191-26,209, 1995 Byun, D.W. and J.K.S. China, Science algorithms of the El'A Models-3 Community Multiscale Air Quality (CMAQ) modeling system. EPA-600/R-99/030, U S. Environmental Protection Agency. U.S Government Printing Office. Washington DC., 1999. Chang, J.S., F.S. Binkowski, N I.. Seaman. D.W. Byun. J.N Mcllenry. P.J. Samson. W.R, Stockwcll, C.J. Walcek, S. Madronich. P.B. Middleton. J.E. Pieim and ll.L. I.andsl'ord, Tlie regional acid deposition model and engineering model, NAPAP SOS/T Report 4, in National Acid Precipitation Assessment Program Acidic Deposition: State of science und technology. Volume /. Washington DC., 199(1 Eder. B.K... MR. Mebust, F.S Binkowski and S J. Roselle. A preliminary evaluation of Models-3 CMAQ using visibility parameters. International Symposium on the Measurement of Tosie and Related Air Pollutants. September 12-14, 2000. Research Triangle Park, NC. Grell, G.A., i. Dudhia and DR. Stauffer, A description of the fifth-generation I'enn State/NCAR mesosealc model (MM5), NCAK Technical Note. NCAR/TN-39X->-STlC 1994 Pitchf'ord, M L. and M. Scruggs. IMPROVE network - Current and future configurations, in; Spatial ant! seasonal patterns and temporal variability of haze and its constituents in the United Slates. \V ('. Malm, principal author, pp 1-1 - I-IK, Cooperative Institute for Research in the Atmosphere. Colorado State University. ISSN 0737-352-47. 2000 Disclaimer. This document has been reviewed and approved by the U.S. Environmental Protection Agency for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. ------- k NERL—RTP-AMD-01—116 TECHNICAL REPORT DATA 1. REPORT NO. EPA/600/A-01/07 6 2. 3.1 4. TITLE AND SUBTITLE A Preliminary Evaluation of Models-3 CMAQ using particulate natter data from the IMPROVE network 5.REPORT DATE 6.PERFORMING ORGANIZATION CODE 7. AUTHOR(S) Brian K. Eder, Michelle R. Mebust, and Sharon K. LeDuc 8.PERFORMING ORGANIZATION REPORT NO. 9. PERFORMING ORGANIZATION NAME AND ADDRESS Same as Block 12 10.PROGRAM ELEMENT NO. 11, CONTRACT/GRANT NO. 12. SPONSORING AGENCY NAME AND ADDRESS U.S. Environmental Protection Agency Office of Research and Development National Exposure Research Laboratory Research Triangle Park, NC 27711 13.TYPE OF REPORT AND PERIOD COVERED Preprint. FY-01 14. SPONSORING AGENCY CODE EPA/600/9 15. SUPPLEMENTARY NOTES 16. ABSTRACT The Clean Air Act and its Amendments require the United States Environmental Protection Agency (EPA) to establish National Ambient Air Quality Standards (or Particulate Matter (PM) and to assess current and future air quality regulations designed to protect human health and welfare Air quality models, such as EPA's Models-3 Community Multiscale Air Quality model (CMAQ) [Byun and Ching, 1999], provide one of the most reliable tools for performing such assessments. CMAQ simulates air concentrations and deposition of various pollutants including PM. These simulations, which can be conducted on a myriad of spatial and temporal scales, support both regulatory assessment as well as scientific studies by research institutions. Within CMAQ is an aerosol component, or module, designed to simulate the complex processes involving PM, which is commonly separated into PM,, (partides with aerodynamic diameters £ 2.5 nm) and PM,0 (aerodynamic diameters £10 nm). In order to determine Its value to the air quality regulatory communities, CMAQ needs to be evaluated using observational data. One such evaluation, which compared visibility parameters derived from CMAQ to visibility parameters obtained from National Weather Service observations, revealed that CMAQ was able to replicate general spatial and temporal patterns [Ederet al., 2000]. The current evaluation compares PM simulated by CMAQ with PM data collected by the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network. 17. KEY WORDS AND DOCUMENT ANALYSIS a. DESCRIPTORS b.IDENTIFIERS/ OPEN ENDED TERMS c.COSATI 18. DISTRIBUTION STATEMENT RELEASE TO PUBLIC 19. SECURITY CLASS (This Report) UNCLASSIFIED 21.NO. OF PAGES 8 20. SECURITY CLASS (This Page) UNCLASSIFIED 22. PRICE ------- |