Evaluation of Fine Particle Number Concentrations in CMAQ Sun-Kyoung Park A; Amit Marmur A; Seoung Bum Kim B; Di Tian A; Yongtao Hu A; Peter H

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Aerosol Science and Technology Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713656376 Evaluation of Fine Particle Number Concentrations in CMAQ Sun-Kyoung Park a; Amit Marmur a; Seoung Bum Kim b; Di Tian a; Yongtao Hu a; Peter H. McMurry c; Armistead G. Russell a a School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA b Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Texas, USA c Department of Mechanical Engineering, University of Minnesota, Twin Cities, Minnesota, USA

First Published on: 01 November 2006 To cite this Article: Park, Sun-Kyoung, Marmur, Amit, Kim, Seoung Bum, Tian, Di, Hu, Yongtao, McMurry, Peter H. and Russell, Armistead G. (2006) 'Evaluation of Fine Particle Number Concentrations in CMAQ', Aerosol Science and Technology, 40:11, 985 — 996 To link to this article: DOI: 10.1080/02786820600907353 URL: http://dx.doi.org/10.1080/02786820600907353

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E-mail: USA. 76005-5888, TX ce.gatech.edu Arlington, 5888, Te and RD83107601, RD82897602, Agreements RD83096001. under Agency tection Te 3 2 1 McMurry, H. Peter Park, Sun-Kyoung CMAQ Concentrations in Number Particle Fine of Evaluation Copyright 2006 40:985Ð996, Technology, and Science Aerosol nhmnhat;freape hycntigrratosrang- reactions trigger can they example, for health; human on INTRODUCTION 1. ue aaeesfrtelgomlmdsi MQaegenerally are reasonable. CMAQ in modes lognormal the for as- parameters that sumed estimate suggests particles to measured of algorithm parameters distribution (EM) size Expectation-Maximization the of the Though, use simulations. concentration number the of 0.01 accuracy the than smaller particles for less accounts that size mode with particles log- simulate 0.01 three than accurately by distributions cannot size modes particle normal Assuming has density impact. particle small while a underestimated, concentra- significantly particle number be causes Assumed CMAQ to other in tions level. emissions also particle primary the are predicted pre- of the size better there affect for concentrations, that modified number factors be particle to of needs CMAQ diction in nucleation used binary homogeneous mechanism While us- 8/31/2000. done Georgia, to Atlanta, 1/1/1999 is prob- in from Evaluation concentrations identify number performance. particle to model measured ing improve measurements to to and compared lems derived are concentrations CMAQ and number health using particle human fine to related Simulated directly visibility. surface be and may number concentrations particle number area because particle important of parti- is simulation fine concentrations Accurate simulates distribution. CMAQ well number how cle addressed relatively have but studies concentrations, mass few investiga- particle scientific fine and well simulates how CMAQ investigating management conducted been quality have studies Numerous air tion. in used widely eateto ehnclEgneig nvriyo inst,Ti iis inst,USA Minnesota, Cities, Twin Minnesota, of University Engineering, Mechanical of Arlington, Department at Texas of University Engineering, USA Systems Georgia, Manufacturing Atlanta, Technology, and of Industrial Institute of Georgia Department Engineering, Environmental and Civil of School a oni fGvrmns 1 i lg rv,PO Box P.O. Drive, Flags Six 616 Governments, of Council xas a,USA xas, lvtdlvl fabetprilshv ietinfluence direct a have particles ambient of levels Elevated h omnt utsaeArQaiy(MQ oe is model (CMAQ) Quality Air Multiscale Community The drs orsodnet u-yugPr,NrhCentral North Park, Sun-Kyoung to Pro- correspondence Environmental Address U.S. the by supported was research This eevd2 eebr20;acpe 3Mrh2006. March 13 accepted 2005; December 21 Received c ,priual uignceto vns nadditional An events. nucleation during particularly µm, mrcnAscainfrArslResearch Aerosol for Association American 1 3 mtMarmur, Amit n rita .Russell G. Armistead and 1 eugBmKim, Bum Seoung a improve can µm 1 isdsge ome h AQ o PM for NAAQS strate- the health, meet human to for designed surface responsible gies or are number particle concentrations in- if Hence, area significantly 2001a). not al. does et (Woo mass crease are particle particles nucleation, small by very formed when particle example, addition, for concentrations, In number 2001b). particle with correlated al. always not et are Woo concentrations mass 1998; 2001; al. al. et et (Donaldson Penttinen concentrations mass dis- pulmonary particle to than Vedal related ease directly 1995; more al. are et Gold concentrations surface Saldiva area and 2000; number 1999; particle al. fine 1995; that et suggest al. studies Ebelt some et 1997), Dockery 1993; Pope 2001; al. 1999; al. et al. et et Dockery (Brauer 1994; health Pope human and the concen- health mass with adverse particle associating and trations studies levels to scien- addition particle of In fine number effects. growing between a linkages concentrations to tific Qual- response mass in Air particle promulgated fine Ambient were for National (NAAQS) the Standards 1997, ity In Earth’s the balance. perturbing Faxvog and radiation 1977; deposition, respon- acid Orloff 1978), also and Roessler are (Adrian and reduction Particles prema- visibility 2001). and for al. attacks sible et heart (Donaldson to death wheezing ture and coughing from ing atce ihsz ewe . n 10 and 2.5 between size with particles 2.5 than less size with particles atce nCA r iie notogop,fie(PM fine groups, two into concentrations. divided particle are as CMAQ well in Particles as species gas-phase CMAQ predicts 2004). (US-EPA (SMOKE) Emissions Matrix Kernel Sparse Operator and meteorological 2003), (PSU/NCAR Mesoscale (MM5) the 5 version 1999), Model Ching (CMAQ) and Quality Air (Byun Multiscale model Community Models-3, the is of frameworks composed modeling quality most the air of One comprehensive models. quality air using studied been have sions epidemiological in number index al. analyses. particle important et an the (Woo was that itself desired suggested concentration also as studies health These human 2001a). protecting for effective h hsc,ceity n h epneo atce oemis- to particles of response the and chemistry, physics, The 2 iTian, Di 1 o ga Hu, Yongtao )adcas atce (PM particles coarse and µm) 1 µm). 2.5 ih o eas be not might 985 2.5 10 : : Downloaded At: 19:58 3 July 2008 etlcro,adohruseie aeilo anthropogenic PM of origin. material ele- unspecified carbon, other organic and carbon, biogenic ammo- mental and nitrate, anthropogenic sulfate, water, are CMAQ nium, in treated species chemical 986 al vrg ocnrtoso PM of concentrations average Daily CMAQ Recently, species. Te the of most for was 50% than less mean normalized error the with that revealed mass 2003) particle al. underestimated et CMAQ (Mebust 1995 June in States United eastern the Ini- over Stein 2003). concentrations Tonnesen species and aerosol 2006; the Jun al. of evaluation 2005; et tial Park al. 2003; et al. Fan et Mebust 2003; 2004; al. et (Eder data mea- abundant surement relatively using evaluated extensively been have anthropogenic of material unspecified other origin. and dust, blown h enfatoa isaderro PM of error and 2006). bias al. fractional et the mean (O’Neill in The 1996 transport 1Ð16, July and during formation Northwest was aerosol Pacific CMAQ investigate to underestimated. used also Sec- also was height. aerosol (PBL) organic cal- layer the ondary boundary in uncertainties planetary the morning to culated 25%. due than likely less are bias differences normalized Those the with well reasonably ulated rtoswr infiatylre hntoeo atcemass particle of those than concen- larger Northwest number Pacific significantly particle the in were in Errors 10 trations 2005). to 2004, 5 al. of et factor 2 (Elleman a about by than less diameter) (i.e., the mode in accumulation the of 0.1 (i.e., those than and mode less Aitken size the with of pre- particles under concentrations CMAQ number that found aerosol have dicts studies few A addressed detail. re- been such to not in has ability concentrations CMAQ’s number concentration, aerosol mass produce particle fine the al. of et (Park errors model affect significantly 2006). not did trations concen- between simulated scales volume-averaged spatial and different point-observations the the to due uncertainties the found that evaluation 2006). CMAQ on al. variability et spatial (Boylan of effects bound Further, acceptable the within were trations h iknmd a rwit h cuuainmd.PM mode. accumulation in the particles into grow and may coagulation, mode Aitken through the other modes two each The nm. with 2.0 interact is v4.3 1998). CMAQ al. in diameter et particle (Kulmala New effect hydration the based (Wilemski incorporating vapor, theory 1984) nucleation acid homogeneous sulfuric binary of classical nucleation the the from calculated is 0 n 0,rsetvl,adtoeo PM of those and respectively, 50%, and 30% ls h rdcinrt fnwpril ubr [m J number, particle new of rate production parti- The aged cles. represents mode emissions, one) direct (larger from accumulation represents the or while nucleation mode from one) either (smaller particles Aitken fresh The CMAQ. in modes) in yntrlpoesssc sboigds rsasalt sea or dust blowing PM CMAQ. as in waves such from processes particles natural by sions a,fo uut2 oAgs 1 00(a ta.2005). al. et (Fan 2000 31, August to 24 August from xas, PM iepril ascnetain iuae sn CMAQ using simulated concentrations mass particle Fine nsieo hs xesv tde ouigo h evaluation the on focusing studies extensive these of spite In sdt iuaefiepril ocnrtosi Houston, in concentrations particle fine simulate to used 2.5 stetdb w neatn u-itiuin (or sub-distributions interacting two by treated is 10 stetdb h oremd rmdrc emis- direct from mode coarse the by treated is )b atro 0t 100, to 10 of factor a by µm) 2.5 10 ao pce eesim- were species major nldssasl,wind- salt, sea includes 2.5 2.5 aswr within were mass pce concen- species −3 sec .K AKE AL. ET PARK S.-K. −1 µm 2.5 ], lainrtswe sn h H nu- the higher using at when improved rates accuracy significantly cleation The was 1999). rates al. nucleation et the Korhonen of 2005; Hegg al. and et (Coffman Gaydos production 1995; particle realistic most the duce diint h enr ytm,teinmdae nucleation H low ion-mediated very a in the particles new In systems, produce can 2005). ternary mechanism al. the et to (Gaydos system addition rates ternary nucleation this of simulated formulation increased appropriate An 2002). al. et to hoy(H nucle- theory homogeneous ation binary the used CMAQ because estimated size aerosol distribution. the of characterization the affecting to in- processes or due accurate modes might lognormal size three CMAQ as mean aerosols in of measured treatment the inadequate size than particle larger mean significantly be modeled the that con- cluded authors the Thus, constituents. gas-phase or concentrations t atceszr(MS 2 m20n) n ae parti- laser PM Hourly a 2001a). al. and nmÐ2 et nm), (100 (LPC) nmÐ250 counter mobil- (20 cle scanning (SMPS) standard sizer a particle nm), ity nmÐ50 (3 (NSMPS) 3 sizer from cle bins particle size 3 Hourly 39 to 2001a). for nm al. measured et were (SEARCH) Woo concentrations 2003; number Characterization al. et South- and the (Hansen of study Research part a Georgia Aerosol as Atlanta, 8/31/2000 eastern urban to 1/1/1999 in from monitor 1a) (Figure (JST) Street Jefferson the METHODS 2. In the simulations. improve Georgia. the to of Atlanta, feasibility accuracy the in examines located also paper area this urban addition, an at 8/31/2000) 3 to to nm bins size 3 39 from of concentrations the uses number particle on evaluation measured assumptions The hourly those re- concentrations. of this number influence particle of the predicted goal analyze The to size. is particle search of distribution assumed emissions, and primary of con- distribution size particle assumed assumed density, assumptions: particle predicted several on affect relies CMAQ also and centrations, factors other CMAQ, Turco pact and Yu 2002; al. et Laakso nu- 1994; 2000). homogeneous al. et binary (Katz for cleation unfavorable are that centrations eea enr ytm,teH the systems, ternary several Among mechanism. nucleation ternary the reconcile is could production that particle process One 1999). al. 1996; et al. Stolzenburg et 2006; Weber al. 2005; et Kulmala 2005; Hegg al. and Coffman et Gaydos 1999; 1995; al. et (Clarke rates nucleation the mate il ocnrtosuigteoclainfeunyo h mass the of frequency transducer. oscillation the using concentrations (TEOM) particle ambient measures Microbalance which 1991), Oscillating Rupprecht and Element (Patashnick Tapered a using tde losgetdta h atcenmeswr under- were numbers particle the that suggested also Studies iepril ubradms eeswr oioe at monitored were levels mass and number particle Fine im- to continue will theory nucleation in uncertainties While imtruigann-cnigmblt parti- mobility nano-scanning a µmin using diameter 2 SO o n eradegtmnh (1/1/1999 months eight and year one for µm 4 n ae) hc infiatyunderesti- significantly which water), and 2.5 ascnetain eemeasured were concentrations mass 2 )(a o ta.20;Woo 2000; al. et Loy (Van µm) 2 O-H O-H 2 2 SO SO 4 4 -NH -NH 3 3 ytmcnpro- can system ytm(Napari system 2 SO 4 con- Downloaded At: 19:58 3 July 2008 I.1 a oaino h efro tet(S)mntrn tto nAtlanta, in domain. station (CMAQ) monitoring (JST) model Street Jefferson quality the of Air Location (a) (b) Georgia. 1. FIG. aa(CR20) hc eentue sa nu fMM5. 1.5 wind of and were humidity, input specific speed an temperature, as in used (MEs) errors not Mean hourly were surface which TDL 2003), (UCAR the Mete- data using 2006). evaluated al. were et (Park fields elsewhere orological in available is system “Results” modeling the (see in used concentrations parameters the number of information grid) Detailed section). km 36 a simulated on and (based observed between negligible discrepancies are the differences these to grids, compared km 12 and im- km 36 possibly on results based and model comparing However, data, performance. would detailed model km) proved spatially 12 more (e.g., grid provided epidemiological modeling have finer time-series con- a number a on simulated based for Evaluating centrations 2006). 1b) al. et (Figure (Marmur 78 km study with 36 States size United to of eastern 1/1/1999 from the run over was 8/31/2000 CMAQ (v4.3). CMAQ MM5 and (v1.5), (v3.5.3), SMOKE framework: Models-3 EPA’s using lated omldsrbto.T opr h iuae concentrations simulated the compare To log- a distribution. follow normal coarse(c), and accumulation(j), Aitken(i), three The modes, three cumbersome. comparison using direct simulated a make are which modes, concentrations CMAQ but bins, size metrological the 2001). for al. et benchmarks (Emery the evaluation model within are values These orypril ubradms ocnrtoswr simu- were concentrations mass and number particle Hourly atcenme ocnrtosaemaue nec f39 of each in measured are concentrations number Particle ◦ ,18gkg g 1.8 C, −1 n . sec m 0.8 and , IEPRIL UBRCNETAIN NCMAQ IN CONCENTRATIONS NUMBER PARTICLE FINE −1 respectively. , × 6grids 66 oal elmthtemaue aus nadto,CMAQ addition, In values. measured the match well reasonably concentrations mass and volume simulated whereas Simulated measured values, than logarithmic. lower much is also y-axis were to concentrations the area up surface that of Note factor 2). a (Figure by 1000 concentrations measured than lower cantly RESULTS 3. not analysis. do the Differences influence diameter. significantly mobility electric diameters, or of Stokes kinds as other such using when con- slightly resolved differ Size diameter. centrations aerodynamic the from on concentrations based modal CMAQ number from particle calculated analy- resolved are size the concentrations Also, of paper. this results in the performed affect sis significantly not do differences 2.5 for than negligible less concentrations are number differences particle total the However, used. are is variables PMx when higher 0.5 between slightly 2.5 size to with a particles are for concentrations nm and 500 used, to 3 con- with between number particles sizes for particle concentrations resolved Number size 1). (Table in centrations changes to distri- lead lognormal and the butions in (Bhave parameters variables Different 2005). output Binkowski diagnostic CMAQ’s from tained de- is mass bin and concentrations. area, size surface number, each particle simulated interval. in the on each concentrations pendent particle for the concentrations of number Accuracy density particle probability the of of integral function the by concen- bin and size number each area, in particle surface trations calculates number, PMx particle distri- concentrations. using lognormal mass mode the each of in parameters Yin bution and the Jiang estimates 2004; al. PMx et 2001). di- Jiang 2005; aerodynamic (Jiang the PMx using in ameter expressed par- concentrations resolved number size ticle into converted con- were CMAQ number from particle centrations modal concentrations, measured the with PMx igotcoutput Diagnostic emti en(D mean Geometric igotcvralsfo /319 o33/99a h JST the at 3/30/1999 to 3/23/1999 from variables diagnostic D σ σ D onra itiuin bandfo M n CMAQ’s and PMx from obtained distributions lognormal iuae n atcenme ocnrtoswr signifi- were concentrations number particle fine Simulated ob- be can distributions lognormal the of Parameters g g g g [µm] [µm] r oehthge hnCA’ igotcoutput diagnostic CMAQ’s when higher somewhat are µm tto nAlna Georgia Atlanta, in station g n emti tnaddvain(σ deviation standard geometric and ) iknacmlto Coarse accumulation Aitken .500 0.80 0.81 0.07 0.11 0.05 0.08 .226 2.20 2.20 2.66 2.07 2.52 1.83 AL 1 TABLE Mode ,adthose and µm, g 987 µm )of Downloaded At: 19:58 3 July 2008 988 htprilsaei peia hp.Teshrclassumption assumes spherical The also shape. CMAQ spherical a spheres. in are are particles that particles by all concentrations that number assuming volume using and calculated area surface are measured concentrations that Note aver- sizes. overestimated particle CMAQ age that suggested results These 3). 1 ure than larger size 1 for than concentrations overestimated less and size for concentrations number underestimated 2.5 than less particles for concentrations mass and volume, area, surface number, particle Daily Georgia. Atlanta, 2. FIG. (Fig- µm .K AKE AL. ET PARK S.-K. µm, il ufc rai eddt cuaeyetmt h particle the estimate accurately to needed of par- is scope the of area the estimation within surface correct not ticle the is that issue note However, this study. not as this were paper errors These this 2004a). in al. shape quantified et particle Park 2002; the al. area with et (McMurry surface associated calculated concentrations not in volume are errors and small agglomerates exist chain there diesel so example, spherical, for correct; not is rm1119 o83/00a h S tto in station JST the at 8/31/2000 to 1/1/1999 from µm Downloaded At: 19:58 3 July 2008 att h supin sd ee he supin:particle assumptions: three Here, used. assumptions the to part ANALYSIS 4. variation. and temporal underestimated less had significantly were anal- simulated concentrations summary, that number showed In concentrations 2004). number al. simulated et of simulate ysis (Hogrefe to features model 2005) fine meteorological the al. very et of the inability Park the 2005; as al. well et as (Marmur spatial the emissions and of effect, averaging allocation spatial the pro- CMAQ, emission in smooth used the by files part con- in caused mass is simulated which of centrations, that number with simulated consistent was of concentrations variation temporal smooth relatively The concentrations. simulated than variations seasonal/daily/diurnal apparent more had concentrations number measured simu- Thus, those lated. in 1 not from but concentrations particles measured in simu- for observed were hours the morn- rush in the afternoon with strong and coinciding ing not peaks is Diurnal which concentrations. lated variation, ap- had weekly an concentrations marked Measured have a 4). not (Figure did trend winter concentrations seasonal the parent in simulated higher but relatively were spring, diameter and the in nm 45 than km. 36 scales. the spatial from different concentrations the number those from simulated than in arises error higher the of 17% fraction were a from Thus, grid station km JST 12 the at the concentrations and Number (Park study separate 2003). a in Russell 2001 1Ð31, July from grids km well 36 as as grids km 12 using simulated particle were scales, concentrations spatial number different the monitoring of effect the the analyze To tions. at concentra- volume-averaged simulates values CMAQ whereas point station, are 2006). al. concentrations et (Park Measured model the and observation the between spatial scales different the to due partly be could concentrations number im- is study. area health particles, surface human the the of addition, for lifetime In portant area. the surface with the on related depends directly is which ve- locity, deposition particle the example, For concentrations. number I.3 einnme ocnrtoso atce o 9sz is(rm3n o2.5 to nm 3 (from bins size 39 for particles of concentrations number Median 3. FIG. neetmto fpril ubradsraeae sdein due is area surface and number particle of Underestimation less size with concentrations particle measured Temporally, particle simulated and measured between discrepancy The IEPRIL UBRCNETAIN NCMAQ IN CONCENTRATIONS NUMBER PARTICLE FINE µmto2µm c 1.77 1.8 carbon Organic carbon Elemental ammonium and nitrate, Sulfate, cm [g atcedniis(seily CadE)ue nCA (see CMAQ in used 2). EC) Table higher and mod- the OC to (especially, to due increased densities is mass ratio particle which The observed concentrations, volume 1.15. of to than mass less ratio from slightly true be would wa- the mass of particles, eled possibility the al. the et in Considering Meyer 2004). ter 1995; used al. Demerjian is et drier and Schwab Nafian Gong 2000; a 2003; as al. negligible et the be (Eatough that would suggest water studies of although amount water, of amounts small tains ms) .3(oue,39 sraeae) n 93 (num- 29.34 PM and that area), Note 3). (surface (Table 3.95 ber) (volume), 1.15 1.43 were concentrations ra- (mass), number simulated Average to dif- measured simulations. of the and tios comparing measurements by between examined ference was concentrations number well. as particle correct on are based distributions mass size the and with values, matched measured well sim- the reasonably if were underestimated concentrations somewhat mass ulated be would CMAQ concen- appear in number CMAQ trations simulated in Hence, values. carbon measured elemental than and larger carbon densi- particle organic Assumed of 2003). ties al. et (Binkowski 2) (Table information density particle assumed using concentrations number Density Particle 4.1. examined. are modes, lognormal three as aerosols treatment of the and emissions, primary of distribution size density, upnadLm2001; Lim and Turpin a nuneo h ncuaedniyo siaigparticle estimating on density inaccurate the of Influence particle into concentrations mass particle converts CMAQ hn,Cngrtae l 2005; al. et Canagaratna Zhang, )fo //99t /120 tteJTsaini tat,Georgia. Atlanta, in station JST the at 8/31/2000 to 1/1/1999 from µm) −3 sue n esrdpril este gcm (g densities particle measured and Assumed ] d cur ta.2002. al. et McMurry AL 2 TABLE 2.5 b asmaue yTO con- TEOM by measured mass ake l 04;Pr ta.2004b; al. et Park 2004a; al. et Park MQLiterature CMAQ . 1.77 2.2 1.2 2 a ∼1.78 1.72∼1.83 , b −3 2.0 , c ) 1.2 , d 989 d d Downloaded At: 19:58 3 July 2008 a otl,()dyo ek n c oryaeaedistributions. average hourly (c) and week, of day (b) monthly, (a) 990 I.4 eprlted ftepril ubrdsrbto,d/(p [µm dN/d(Dp) distribution, number particle the of trends Temporal 4. FIG. 127 .779.9 827 8533.7 1.1E+04 1.8E +06 Maximum 222.228.7 781 3.3E+03 8.8E+04 percen. 75th 151.018.1 529 2.3E+03 5.2E+04 Median .994 369 1.6E+03 3.2E+04 percen. 25th .40 48 7.6E+01 2.2E+03 Minimum 101.713 549.1 1.3E+03 9.5E+04 STDEV Mean umr ttsiso oryaeaepril ubr ufc ra oue n ascnetain o atce esthan less particles for concentrations mass and volume, area, surface number, particle average hourly of statistics Summary 168 .122 663.7 2.5E+03 7.5E+04 B OE B OE B OE B MODEL OBS MODEL OBS MODEL OBS MODEL OBS ubr[cm Number 2.5 rm1119 o83/00a h S tto nAlna Georgia Atlanta, in station JST the at 8/31/2000 to 1/1/1999 from µm −3 ] ra[µm Area .K AKE AL. ET PARK S.-K. AL 3 TABLE 2 −1 cm cm −3 −3 .712 .91 ]V ,fo //99t /120 tteJTsaini tat,Georgia. Atlanta, in station JST the at 8/31/2000 to 1/1/1999 from ], lm [µm olume .620 .914 .88 .50 .49 .415 3 cm .597 .627 .018 .911 .10 .613 .721 −3 ] wtrwsexcluded) was (water .581 .824 .317 .910 .90 .010 .318 as[µgm Mass −3 .4 .4 .0 .9 .0 .6 .5 ] Downloaded At: 19:58 3 July 2008 nteacmlto oAte oefrPM for emissions mode PM Aitken primary to the accumulation of the ratio in the to respond centrations current the of topic 2002). hot Woo a and (McMurry is issue emissions size particle The 2002). of (Tsyro mode distribution 100% accumulation and the mode, in accumulation is the that PMFINE in of are assume PEC version, and POA model of 85% aerosol unified Evaluation and (EMEP) Monitoring Program European accu- the the quality example, in air for other be models, Currently, would the modification. emissions needs to PEC mode PEC and mulation assumption POA and the of so, POA 99.9% If of that 1994). al. 15% et (Venkataraman remaining accumula- mode the the Aitken to and assigned elemental mode, be and tion should (POA) emissions carbon (PEC) organic that carbon primary show of studies However, 85% mode. around as- accumulation nitrate are the emissions primary to (PMFINE) (PSO4), signed PM fine sulfate al. unspecified et primary and (Binkowski of (PNO3), mode 100% Aitken the and to 2003), frac- assigned remaining the are and 0.1%, mode, as- tion, accumulation are the aerosol in emissions organic be (PEC) to primary carbon sumed fine elemental the primary the and of in (POA) 99.9% fraction small i.e., a mode; with mode Aitken accumulation the in are sions h eal supini httemjrfato fPM of fraction major the that is CMAQ, assumption In default distributions. the size source about information contain particle on analyzed. the emissions was be, initial distributions of size might distribution this size why the investigate of rea- To influence 2). predicted (Figure were well concentrations sonably al- number volume measured simulated the simulated because than though lower expected significantly was were result 1.0 concentrations This than 4). (less particles and small for centrations than (larger particles large 1.0 overestimated for CMAQ concentrations compared. number be particle can also sizes particle ent Emissions PM Primary of Distribution Size 4.2. number simulated and measured concentrations. par- explain between fully higher difference cannot large the alone parti- density the to particle underestimated due the but CMAQ 10% density, ticle about Thus, concentrations cases. number less both cle changed in al. concentrations 1% in- et mass concentrations than Park However, number 2004a; the 10.9%. al. 2001), et creased Lim (Park assumed and respectively Turpin are 1.77, EC 2004b; and and OC 1.78 of as densities When concentrations 10.7%. number the increased 2002), al. et (McMurry respectively n Ci MQaeasmda . cm g 1.2 as OC assumed of are CMAQ densities in When EC 2). and (Table CMAQ with concentrations calculated number were and mass particle and densities, particle estvt nlsso o iuae atcenme con- number particle simulated how of analysis Sensitivity h P msinivnoyfrpriuaemte osnot does matter particulate for inventory emission EPA The differ- for concentrations particle simulated and Measured atcedniisi MQaecagdt h measured the to changed are CMAQ in densities Particle ) u infiatyudrsiae atcenme con- number particle underestimated significantly but µm), IEPRIL UBRCNETAIN NCMAQ IN CONCENTRATIONS NUMBER PARTICLE FINE −3 2.5 n . cm g 2.0 and )(iue 3 (Figures µm) msin was emissions 2.5 emis- −3 , I.5 al vrg esrdpril ubrcnetain nJTfrom Georgia. JST Atlanta, in in concentrations station number JST particle the at measured average 3/30/1999 Daily to 3/23/1999 5. FIG. OBS CMAQ E experiencing days on low be to continued but days, typical for concentrations measured approached simulated concentrations the number 0.85/0.15, Thus, to change). 0.999/0.001 from 1% changes than ratio the (less as same concentra- the mass virtually particle remained simulated but tions 4), (Table and 6 of PEC increased factor to a concentrations by 0.999/0.001 for number from 0.999/0.001 particle changed simulated of ratio 0.85/0.15, ratio the When default emissions. the POA to addition the in with 0.99/0.01, run and was nucle- 0.9/0.1, (accumulation/Aitken), model to 0.85/0.15 The of due 2001a). ratio al. nm et 45 (Woo than 5) (Figure less ation con- size high with exceptionally particles of by centrations characterized being latter (3/29∼3/30) the nucleation days, and (3/23∼3/28) which regular 3/30/1999), both to includes (3/23/1999 period limited a for performed cuuain(E accumulation iuae n atcenme ocnrtosb MQ as CMAQ, by concentrations number particle fine Simulated i :E eua as4.5E+04 2.5E+04 1.8E+05 1.8E+04 5.9E+03 4.2E+03 days event Nucleation days Regular 0.15:0.85 0.10:0.90 0.01:0.99 (default) 0.001:0.999 nta ai fpril msin nteAte (E Aitken the in emissions particle of ratio initial j tteJTsaini tat,Georgia Atlanta, in station JST the at j oe hnefo /319 o3/30/1999 to 3/23/1999 from change modes ) AL 4 TABLE ubr[cm Number i and ) 991 −3 ] Downloaded At: 19:58 3 July 2008 to rcs snee.I umr,teteteto aerosols of treatment the summary, In nucle- needed. the of is treatment process new proper ation the the of and shape selected, appropriate be particles should The mode mm. predict 0.01 better than to less order sizes nucleation with in a i.e., considered mode, be Aitken should the mode, of that of than diameter smaller mean is geometric which the mode, additional an 0.01 that than shows less well size fit with not particles did for concentrations number 1978). (Whitby measured aerosols urban the typical However, of those (Ta- to CMAQ also in and assumed 5), those ble to similar were data measured the 0.01 than parti- larger for cles concentrations number measured the well fit algorithm 3 measurements above size made because not evaluated were mode be Coarse not Appendix). could Expectation- parameters (see an algorithm using (EM) 6) (Figure Maximization were distributions data lognormal measured to used, fit parameters distribution the Toevaluate 5. Table in summarized 1978). are CMAQ Whitby in 2003; parameters al. Distribution et (Binkowski modes coarse and lation, accumu- Aitken, distributions: lognormal three follows Modes tribution Lognormal Three as Aerosol 2005; of Treatment al. et 4.3. (Gaydos uncertain still 2004). al. is et Kulmala phenomena leading (Woo such chemistry condensation and physics to by exact ox- the followed However, or 2001a). species 2001), al. et al. phase et gas (Tobias of gases by exhaust idation occur hot can of Nucleation cooling 2001a). rapid al. et (Woo events nucleation 992 oue(as v iknacmlto ore0.02 0.02 coarse accumulation Aitken 0.01 (v) coarse (mass) accumulation Volume Aitken coarse accumulation Aitken (s) area Surface (n) Number Variable aaeeso h onra itiuinfrtepril ubr ufc ra n ouecnetain sdi MQ and CMAQ, in used concentrations volume and area, surface number, particle the for distribution lognormal the of Parameters ∗∗ ∗ h itiuinuigtelgomlprmtr rmteEM the from parameters lognormal the using distribution The dis- size particle the that assuming particles simulates CMAQ aaeesfrtesraeae n as(oue ocnrtosaecluae sfollows. as calculated are concentrations (volume) mass and area surface the for Parameters ee oFgr 6. Figure to Refer • • oue(as:D (mass): Volume ufc ra D area: Surface Fgr ) aaeesetmtdusing estimated Parameters 6). (Figure µm gs = gv Mode µm. nD ln = nD ln gn hs ae ntemaueet tteJTsaini tat,Georgia Atlanta, in station JST the at measurements the on based those + gn (lnσ 2 + (ln 3 Fgr ) hsresult This 6). (Figure µm gn ) σ 2 gn , σ ) 2 gs , σ = gs σ = gn σ gn en(µm) mean Geometric .K AKE AL. ET PARK S.-K. 0.30 0.18 0.07 6.46 3.47 1.00 AL 5 TABLE ulainprmtrzto Zag i ta.2005). al. et Liu (Zhang, binary parameterization the with nucleation compared nu- distributions size ternary and number the predicting particle in that performance showed better gave results parameterization cleation The CMAQ. using and implemented tested ho- was ternary new parameterization a nucleation Recently, is mogeneous also 2005). particles al. existing result et on This (McMurry formation sizes. of reasonable rate larger the to using that growth preexist- suggests their to and clusters particles nucleated competition of ing a loss involves coagulation size the detectable between a of growth to the particles that nucleated shows forma- study Experimental particle than rates. uncertain new is absolute less the is CMAQ of particles existing in rates on condensation relative used and tion using formation so particle uncertain, the also of rate that suggest absolute Studies CMAQ. the homogeneous the in binary used the mechanism modifying nucleation without achieved truly be mono-distribution the model. of that than four-distribution better the significantly was pre- of model of concentrations number Performance size particle 2000). particle dicting Kulmala represent and to (Pirjola version approach distribution another monodisperse is uses (MULTIMONO), MM32 which model 2002). monodisperse (Pir- al. multicomponent et module the Tsyro dynamic of 2003; aerosol al. as et MM32 jola aerosol uses EMEP Aitken, version 2002). the (Tsyro model to modes addition coarse in and mode, accumulation, nucleation a includes version 0.01 than smaller ticles than larger particles simulate 0.01 should modes lognormal three as CMAQ oeta h mrvmn ntepril rdcincannot prediction particle the in improvement the that Note esnbywl,btde o cuaeysmlt par- simulate accurately not does but well, reasonably µm tnaddeviation standard Geometric ∗ 2.0 2.0 2.0 2.2 1.7 2.2 1.7 2.2 1.7 .Crety h MParslmodel aerosol EMEP the Currently, µm. en(µm) mean Geometric 0.045 0.043 0.017 0.28 0.18 0.07 NA NA NA OBS tnaddeviation standard ∗∗ Geometric 2.03 2.03 2.34 2.03 1.81 1.88 NA NA NA Downloaded At: 19:58 3 July 2008 h bevdvle,tog hshdasalipc sddus- did as impact small a had this though values, observed the was modes. treatment lognormal the three and as emissions, aerosols initial of the of distribution size density, particle of impacts including con- investigated, number were particle centrations predicted the as- on CMAQ of in Influences used sumptions levels. particle predicted the affects to model specification leading other likely concentrations, number uncertainty an particle one of remains underestimation is which research, mechanism, of nucleation area active the While factor 1000. a to of up underestimated number significantly Simulated were CMAQ evaluated. concentrations were by 8/31/2000 to simulated 1/1/1999 concentrations from number Particle demon- strated. thoroughly as not repro- is to concentrations number ability aerosol CMAQ’s duce but evaluated, extensively been have CONCLUSIONS 5. geometric Georgia, the Atlanta, and in concentrations, station (V) JST volume the and at (S), 8/31/1999 area to surface 1/1/1999 (N), from number concentrations the volume with (c) along and algorithm area, EM surface (D the (b) from mean number, parameters particle using median distributions Measured fitted (a) the and 6. FIG. n as fudrsiaigpril ubrconcentrations number particle underestimating of cause One CMAQ using simulated concentrations mass particle Fine htteasmdpril est nCA shge than higher is CMAQ in density particle assumed the that g ,adgoercsadr eito (σ deviation standard geometric and ), )frteAte i,adacmlto j modes. (j) accumulation and (i), Aitken the for g) IEPRIL UBRCNETAIN NCMAQ IN CONCENTRATIONS NUMBER PARTICLE FINE atce a mrv h cuayo h ubrconcentration number the simulations. of accuracy the improve than can less particles size with particles capture 0.01 accurately not the could distribu- lognormal of tions three distribution that size showed concentrations The us- measured aerosols distributions. of lognormal treatment three the ing was underestimation the of con- cause Another number values. measured particle approached and increased, increased mode centrations Aitken particle the initial in of Aitken fraction emissions the the when that in Sensi- showed mode. results being test accumulation tivity the as as 85% treated remaining the be and mode, should POA of emissions 15% PEC around and that show Measurements accu- the mode. to emissions mulation PEC and mode, POA of Aitken 99.9% the remaining to the and emissions particle fine of 0.1% emissions CMAQ only Currently, particle assigns observed. than initial particles larger of was CMAQ distribution in size was underestimation assumed the the of cause that Another sizes. grid larger ing .A diinlmd htacut o h nucleated the for accounts that mode additional An µm. 993 Downloaded At: 19:58 3 July 2008 lre . autn . iee . ee,R,adMMry .(1999). P. McMurry, and R., Weber, F., Eisele, V., Kapustin, A., EPA Clarke, the of Algorithms Science (1999). K. J. Ching, and W., D. Byun, rur . bl,S . ihr .V,Bum . eku .J,adVedal, and J., A. Petkau, J., Brumm, V., T. Fisher, T., S. Ebelt, M., Brauer, As- Integrated (2006). A. Russell, and J., Wilkinson, M., Odman, J., Boylan, Multiscape Community Models-3 (2003). J. S. Rosselle, and communication. S., Private F. Binkowski, (2005). S. F. Binkowski, and P., Bhave, Signals—Visibility Anemometer Laser (1977). L. K. Orloff, and J., R. Adrian, REFERENCES 994 bl,S . eku .J,Vdl . ihr .V,adBae,M 20) Expo- (2000). M. Brauer, and V., T. Fisher, S., Vedal, J., A. Petkau, T., S. Ebelt, Semi- (2003). L. N. Eatough, and K., W. Modey, W., R. Long, J., D. Eatough, Particle Ambient (2001). W. MacNee, and A., Seaton, V., Stone, K., Donaldson, Particle (nanometre) Ultrafine (1998). W. MacNee, and Y., X. Li, K., Donaldson, E., M. Fay, H., J. Ware, D., J. Spengler, P., X. Xu, A., C. Pope, W., D. Dockery, Particulate of Effects Respiratory Acute (1994). A. C. Pope, and W., D. Dockery, Likelihood Maximum (1977). B. D. Rubin, and M., N. Laird, P., A. Demster, of Effect the of Preliminary-Study A (1995). A. D. Hegg, and J., D. Coffman, mr,C,Ti . n awo,G 20) nacdMtoooia Mod- Meteorological Enhanced (2001). G. Yarwood, and E., Tai, C., Emery, J. Chen, and F., C. Mass, S., D. and Covert, A., F., R. Kotchenruther, C. A., R. Mass, Elleman, S., D. Covert, A., R. Kotchenruther, A., R. Elleman, CMAQ Models-3 the of Evaluation An (2003). R. Dennis, and S., Yu, B., Eder, atcePouto erMrn lus ufrcAi n Predictions and Nucleation, Acid Binary Sulfuric Clouds: Classical Marine from Near Production Particle Park, Triangle Research Agency, 27711. Sys- NC Protection Modeling Environmental U.S. (CMAQ) oratory, Qualtiy Air tem. Multiscale Community Models-3 Epidemiol. Environ. .(01.Epsr fCrncOsrciePloayDsaePtet to Effects, Patients Health Disease Cardiovascular Pulmonary and Obstructive Respiratory Chronic Particles: of Exposure (2001). S. Cisibility, and Matter Particulate Assoc. Manag. Fine Appalachian 2. Southern the Part in Mountains: Pollutants Atmospheric of Modeling sessment Description, Model 1. Component Res.—Atmos. Aerosol Geophys. Model (CMAQ) Quality Air Sizing, Particle 684. to Application and Characteristics eainhp ewe esnladAbetArConcentrations, Air Ambient and Personal Between Matter: Particulate Relationships to Patients Disease Pulmonary Obstructive Chronic of sure Challenge, surement V Mechanisms, Potential Perspe. System: Health Cardiovascular the and Inhalation Injury, Lung Mediated 1759. Cities, United-States 6 Air-Pollution in Between Mortality Association and An (1993). E. F. Speizer, and G., B. Ferris, Air-Pollution, Discussion), (with Algorithm EM (B) Soc. the Statis. Via Data Incomplete from Boundary-Layer, Marine Atmos. the Res. in Nucleation Particle on Ammonia doi:10.1029/1999GL900438. nentoa op oao CA. Novato, Corp, International Episodes, Texas Two for Te Evaluation Performance and eling Meeting, Annual AMS 85th CA. the Diego, Chemistry, San Atmospheric Northwest. on Pacific Conference for Evaluation 7th Mass and Number Aerosol CMAQ (2005). Pa- for Evaluation Mass and NC. Number Northwest, Aerosol CMAQ cific (2004). Health, J. Human Chen, and PM on Colloquium Forth PA. the Pittsburgh, and Exposure ences, Module. Aerosol Assoc. Manag. Waste ltl eodr rai eoo nUbnAmshrs etn Mea- a Meeting Atmospheres: Urban in Aerosol Organic Secondary olatile a aua eore osrainCmiso,peae yENVIRON, by prepared Commission, Conservation Resources Natural xas topei oeigdvso,Ntoa xoueRsac Lab- Research Exposure National division, modeling Atmospheric 0:1776,doi:10.1029/2000JD000037. 100:7147Ð7160, n.Rv ulcHealth Public Rev. Ann. 56:12Ð22. 109:523Ð527. 39:1Ð38. M20 etn—atclt atr topei Sci- Atmospheric Matter: Meeting—Particulate 2003 PM eerhTinl Park, Triangle Research Workshop, User’s Models-3 11:490Ð500. to.Environ. Atmos. 50:1081Ð1094. J. 0(6:13 doi:10.1029/2001JD001409. 108(D6):4183, eoo Sci. Aerosol 37:1277Ð1292. 29:553Ð560. epy.Rs Lett. Res. Geophys. 15:107Ð132. e nln .Medicine J. England New ple Optics Applied J. J. xoueAnal. Exposure 2:2425Ð2428, eott the to Report i Waste & Air 329:1753Ð J. .K AKE AL. ET PARK S.-K. Environ. J. J. 16:677Ð Geophy i & Air Royal J. amr . ak .K,Mloln,J,adRsel .G 20) PM2.5 (2005). G. A. Russell, and J., Mulholland, S.-K., Park, A., Stud- Marmur, Model (2002). M. Kulmala, and L., Pirjola, M., J. Makela, L., Laakso, uml,M,Vhaai . ead,T,DlMs,M,Lui . Kerminen, A., Lauri, M., Maso, Dal T., Petajda, H., Vehkamaki, M., Kulmala, uml,M,Lhie,K,adLasnn .(06.CutrAtvto Theory Activation Cluster (2006). A. Laaksonen, and K., Lehtinen, M., Kulmala, Sul- for Parameterizations (1998). L. Pirjola, and A., Laaksonen, M., Kulmala, ohnn . uml,M,Lasnn . isnn . crw R., McGraw, Y., Viisanen, A., Laaksonen, M., Super- Kulmala, a of P., Condensation (1994). Korhonen, M. V. Chakarov, and A., J. Fisk, L., J. Katz, CMAQ and Observed of Comparison Statistical (2004). L. M. Stein, and M., Jun, Soft- PMx the of Application and Development (2001). D. Yin, and W., Jiang, PM CMAQ of Evaluation (2004). D. Yin, and H., Roth, E., Giroux, W., Jiang, in,W,Gru,E,Syh . oh . i,D 20) ifrne Between Differences (2004). D. Yin, H., Roth, S., J., Smyth, E., Giroux, Rosenthal, W., Jiang, Y., J. Ku, K., Civerolo, B., Lynn, J., Biswas, C., Hogrefe, F., W. McDonnell, W., D. Dockery, A., C. Pope, I., A. Damokosh, R., D. Gold, Ultrafine Situ in of Modeling (2005). S. Pands, and C., Stanier, M., T. Gaydos, Particle- vs Visibility Aerosol Carbon (1978). M. D. Roessler, and R., F. (2005). Faxvog, Q. Z. Li, and J., Nielsen-Gammon, H., G. Li, Y., R. Zhang, W., J. Fan, og .V,Cag .T,adMKa,J .(2005). W. J. McKean, and T., A. Craig, V., R. Hogg, N., Kandasamy, J., J. Jansen, E., B. Hartsell, S., E. Edgerton, A., D. Regenerated Hansen, a on Losses Hydrocarbon (1995). L. K. Demerjian, and Q., Gong, tat,Georgia. Atlanta, and Studies, Program Related Supersites Matter Conference—Particulate Studies. Specialty Health Con- national for Models: Implications and Source-oriented Differences and ceptual Receptor Using Apportionment Source Atmosphere, the doi:10.1029/2002JD002140. 107:4427, in Nucleation Ion-Induced on ies 35:143Ð176. fUtan topei atce:ARve fObservations, of Review A Particles: Atmospheric Ultrafine of V. 6:787Ð793. sa xlnto fteLna eedneBtenFrainRt of Rate Formation Between Dependence Linear the 3nmP of Explanation an as doi:10.1029/97JD03718. Rates, Nucleation Acid/Water furic doi:10.1029/1999JD900784. n ened .H 19) enr ulaino H of Nucleation Ternary (1999). H. J. Seinfeld, and Ions, on Nucleation Vapor.9. saturated Levels, Sulfate Daily Modeled PET-1497-01S, number: Report 2001. Canada. Resources in Results Natural Matter Development, Particulate Modal Quantities, CMAQ Size-Resolved Converting for Package ware its Canada. and Alberta, Modelling Banff, Pollution 2004, 25Ð29, Air October on Application, (ITM) NATO/CCMS 27th Meeting Assessment, Technical Performance International Model on Impact its and Diameter Issue Particle The Data: Measurement Field Size-Resolved Using Results NC. Models-3 Hill, 2004 Chapel Valley, 2004, Fraser 18Ð20, Lower October Conference, the in Evaluation Performance Model H MQFn oePril n PM and Particle Mode Fine CMAQ Evaluation Model States: United Results, Eastern the Over Climatology Ozone Regional- Simulating Scale (2004). L. P. Kinney, and R., Goldberg, C., Rosenzweig, Statistics. ical Mexico Southwest in Children of Function City, Respiratory the on Pol- Effects Ozone and lutant Particulate (1999). M. Castillejos, and A., Retama, P., Serrano, Atmos. States, United Eastern the in Formation Particle Atmospheric Distribution, Size Atmos. Res. Texas, Houston, in (PM2.5) Matter Particulate Fine of Simulations 53:1460Ð1471. 1-Overview, Part Research Study: Aerosol Characterization Southeastern The and (2003). L. C. Blanchard, and M., G. Hidy, Dryer, Nafion(R) 2 . iml,W,adMMry .H 20) omto n rwhRates Growth and Formation (2004). H. P. McMurry, and W., Birmili, M., Oin Atmosphere, the Epidemio. rilsadSlhrcAi Concentration, Acid Sulphuric and articles 1:0S2 doi:10.1029/2004JD004683. 110:D07S12, to.Environ. Atmos. 1:123 doi:10.1029/2005JD005805. 110:D16203, rnieHl,NwYork. New Hall, Prentice 10:470Ð470. J. pl Optics Appl. i at aa.Assoc. Manag. Waste & Air 38:2627Ð2638. rprdfrPormo nryRsac and Research Energy of Program for Prepared J. to.Environ. Atmos. 17:2612Ð2616. epy.Rs Atmos. Res. Geophys. J. 2.5 epy.Rs Atmos. Res. Geophys. J. ocnrtosadterIpc on Impact their and Concentrations hm Phys. Chem. J. 38:4427Ð4436. 45:490Ð493. i at aa.Assoc. Manag. Waste & Air to hm hs Discuss. Phys. Chem. Atmo. nrdcint Mathemat- to Introduction J. epy.Rs Atmos. Res. Geophys. 101:2309Ð2318. 104:26349Ð26353, rc ARInter- AAAR Proc. 2 SO 103:8301Ð8308, J. epy.Res. Geophys. J. 4 ,NH eoo Sci. Aerosol J. Geophys. 3 and , Downloaded At: 19:58 3 July 2008 cur,P . ag . ak . n hr,K 20) h Relationship The (2002). K. Ehara, and K., Park, X., Wang, H., P. McMurry, (2006). A. Russell, and P., Tolbert, J., Mulholland, S.-K., Park, A., Marmur, cur,P .(93.NwPril omto ntePeec fa Aerosol— an of Presence the in Formation Particle New (1983). H. P. McMurry, aai ,Npe,M ekmk,H n uml,M(02.Parametrization (2002). M Kulmala, and H, Vehkamaki, M, Noppel, I, Devel- Napari, (2000). E. Rupprecht, and L., J. Ambs, H., (2003). Patashnick, J. B., M. S. Meyer, Roselle, and S., F. Binkowski, K., B. Eder, R., M. Mebust, R. III, Mauldin R., M. Stolzenburg, H., Sakurai, M., Fink, H., P. McMurry, Urban 3Ð100-nm of Distributions Size (2002). S. K. Woo, the and in H., P. Formation McMurry, Particle New (1979). K. S. Friedlander, and H., P. McMurry, aahik . n upeh,E .(91.Cniuu m1 Measurements Pm-10 Continuous (1991). G. E. Rupprecht, and H., Patashnick, the in emissions to 2.5 PM of Sensitivity (2003). G. A. Russell, and S.-K., Park, apportionment Source (2005). M. Zheng, and A., R. G., Y., B., L., Ke, S.-K., Park, A. Russell, and Y., Hu, J., Mulholland, K., Wade, E., C. (2004b). Cobb, H. S.-K., P. Park, McMurry, and R., M. Zachariah, B., D. Kittelson, K., Park, Properties Structural (2004a). H. P. McMurry, and B., D. Kittelson, K., Park, Figueroa, S., Otterson, D., Finn, C., Claiborn, J., Chen, B., Lamb, S., O’Neill, ijl,L,Tyo . arsn . uml,M 20) Monodisperse A (2003). M. Kulmala, L., Tarrason, S., Tsyro, L., Pirjola, MULTIMONO, Model Dynamical Aerosol (2000) M. Kulmala, and L. Pirjola, and J., Ruuskanen, A., Mirme, P., Tittanen, L., K. Timonen, P., Penttinen, ewe asadMblt o topei atce- e ehiu for Density, Technique New Particle Particles-A Measuring Atmospheric for Mobility and Mass Between Studies, Using Impli- Health and States Time-Series Differences for United cations Conceptual Southeastern Models: the Emissions-Based and in Receptor PM2.5 of Apportionment Source ae,Tm cls n u-.1M- ieDistributions, Size Mu-M Sub-0.01 and Scales, Time Rates, fTrayNceto ae o 2O-H-2 Vapors, H2SO4-NH3-H2O for Rates Nucleation Ternary of PM Continuous TEOM the for System Equilibration Monitor, Sample a of opment Aerosol Model (CMAQ) Evaluation, doi:10.1029/2001JD001410. Quality Model Air Component-2. Multiscale Community Models-3 G., L. Atmosphere, doi:10.1029/2005JD005901. Huey, Atlanta 110:D22S02, D., Sulfur-Rich Tanner, the Particle New S., in for Criterion Sjostedt, Formation A (2005). K., D. Voisin, and E., Moore, Edgerton, B., F., J. Nowak, Eisele, J., Smith, L., Lung the in Effects Observations, Cleara. and Measurement Aerosols: Atlanta Aerosol, an of Presence Sci. sn h aee lmn siltn Microbalance, Oscillating Element Tapered the Using NC. Park, Southeast. Georgia. Atlanta, Studies, Related Cconference—Particulate and Program Specialty model. Supersites receptor International Matter a AAAR and an model quality of air Proceedings three-dimensional a using PM2.5 of press. Concentrations, in Pollutant in Variation Spatial due Matter to Particulate for Evaluation Model Qualiy Air in Uncertainty (2006). Agglomerates, Nanoparticle of Density J. Material Inherent of Measurement Mobility, and Mass 38:881Ð889. Particle Microscopy to Electron Relationships Transmission (TEM): by Measured Particles Exhaust Diesel of Modeling with Northwest Technol. (2006). Sci. Pacific System, M. Modeling the (CMAQ) Anderson, Quality in Air Stocum, Multi-Scale Transport and Community S., and the M., Formation Aalbers, Dubois, Aerosol J., M., and Arnold, Ozone Stoll, R., C., Wilson, Swab, M., J., Boyer, C., Bowman, C., Atmos. eoo yaisMdl,aPoiigCniaefrUei Long-Range in Use for Candidate Promising a Module, Dynamics Aerosol Res. Environ. Boreal Subjects, Asthmatic Urban Perspec. Adult in Health in Particles viron. Function of Lung Spirometric Size on and Effects Concentration Air: Number (2001). J. Pekkanen, Assoc. aoatceRes. Nanoparticle 95:72Ð80. 41:1079Ð1083. 0:31 doi:10.1029/2002JD002132. 107:4381, J. eerhTriangle Research Workshop , User’s Models-3 the of Proceedings i at aa.Assoc. Manag. Waste & Air 40:1286Ð1299. 6:267Ð272. 5:361Ð374. 109:319Ð323. to.Environ. Atmos. 15:169Ð178. eoo c.Technol. Sci. Aerosol J. 50:1345Ð1349. to.Environ. Atmos. epy.Rs Atmos. Res. Geophys. 13:1635Ð1651. IEPRIL UBRCNETAIN NCMAQ IN CONCENTRATIONS NUMBER PARTICLE FINE topei Environment, Atmospheric 36:227Ð238. J. J. J. eoo e.Depos. Med. Aerosol eoo c.Technol. Sci. Aerosol epy e.Atmos. Res. Geophy. i at Manag. Waste & Air 40:2533Ð2551. J. J. ol Interface Coll. epy.Res. Geophys. 108:4184, Environ. En- ea,S 19) min atce n elh ie htDivide, That Lines Health: and Particles Ambient (1997). S. Vedal, upn .J,adLm .J 20) pce otiuin oP25Mass PM2.5 to Contributions Species (2001). J. H. Lim, and J., B. Turpin, Ini- (2002). Tarras«on, L. and D., Simpson, M., Kulmala, L., Pirjola, S., Tsyro, ee,R,MMry . ali,R,Tne,D . L lre . and A., Clarke, FL, E., D. Tanner, R., Mauldin, P., McMurry, A. R., Jefferson, Weber, and D., Tanner, F., Eisele, P., McMurry, J., Marti, R., Weber, Distributions Sire (1994). K. S. Friedlander, and M., J. Lyons, C., Venkataraman, Observations. Hourly Va Surface Canada http://cf.unc.edu/cep/empd/products/ manual. User’s and SMOKE (2004). US-EPA U.S. TDL (2003). UCAR sr,S 20) is siae fteEfcso eoo yaisin Dynamics Aerosol of Effects the of Estimates First (2002). S. Tsyro, Results Modeling Recent Modeling: Haze Regional (2003). S. G. Tonnesen, P. McMurry, M., Zuk, H., Sakurai, J., P. Ziemann, E., D. Beving, J., H. Tobias, F., Eisele, R., Mauldin, J., Smith, H., Sakurai, P., McMurry, M., Stolzenburg, Labora- (2004). L. J. Ambs, and L., K. Demerjian, O., Hogrefe, J., J. Schwab, J., A. Lichtenfels, W., D. Dockery, J., Schwartz, A., C. Pope, N., H. Likeli- P. Maximum Saldiva, Densities, Mixture (1984). F. H. Walker, and R., A. Redner, Class Tutorial System Modeling Mesoscale PSU/NCAR (2003). PSU/NCAR Kanner, E., M. Raizenne, C., A. Larson, G., E. Lovett, L., R. Verrier, A., C. Pope, S., J. Evans, W., D. Dockery, M., M. Namboodiri, J., M. Thun, A., C. Pope, Associ. ocnrtos eiiigCmo supin o siaigOrganic Estimating for Mass, Assumptions Common Revisiting Concentrations: APMS’01 . Simulation, and 2001. Modelling 9Ð12, pollution April Champs-sur-Marne, Air on conference In: Second Europe. (ed.), in Matter Sportisse Particulate of B. Transport Long-Range from Results tial autn .(99.NwPril omto nteRmt Troposphere: Remote doi:10.1029/1998GL900308. 310, Sites, the Various at in Observations Formation of Comparison Particle A New (1999). V. Kapustin, Implications Mechanisms, Rates: Nucleation Formation for Particle New Atmospheric Measured (1996). 28:555Ð562. Characterization, Sampling, Source and Carbon.1. Methods, Elemental Measurement and Aromatic-Hydrocarbons Polycyclic of Associ. Manag. Waste Compo- Intercomparisons, Sampler Aerosol and and Concentrations nent Mass PM2.5 (ARIES): Study Epidemiology Inhalation smoke. http://dss.ucar.edu/datasets/ds472.0/. h aclto fP1 n PM2.5. and PM10 of publ/reports/2002/mscw Calculation the Carolina. North WRAP, and VISTAS for Spectrometer, Desorption Mass Nano-DMA/Thermal Beam a Particle Using Nanoparticles Analy- Engine Chemical Diesel (2001). of B. D. sis Kittelson, and R., Waytulonis, D., Zarling, H., Atmospheric Nucleated Freshly 2005JD005935. Atlanta, of Rates in Growth Particles (2005). C. Clement, and Microbalance Oscillating Samplers, Element Tapered Modified of Characterizaton tory Brazil, Health Sao-Paulo, Mortality in and Study Air-Pollution Time-Series (1995). People—A M. Elderly G. in Bohm, and I., Barone, M., J. Salge, Algorithm, EM the and hood Research. 3, Atmospheric Version for Center System National Division, Modeling Meteorology Microscale MM5 Guide: User’s and Notes Pollution, Air Particulate 138:890Ð899. with Associated Heart (1999). Variability W. D. Dockery, Rate and R., D. Gold, M., Villegas, J., Schwartz, E., R. Adults, US Med. of Prospective-Study a in Predictor a Mortality as of Air-Pollution Particulate (1995). W. C. Heath, and E., F. Speizer, doi:10.1029/2002JD002867. Tests, Model Box Models: Transport nL oy, 151:669Ð674. eoo c.Technol. Sci. Aerosol . aaoi . n yg,R 20) h eoo eerhand Research Aerosol The (2000). R. Wyzga, and T., Bahadori, M., 50:159Ð163. 50:1446Ð1458. J. i at aa.Associ. Manag. Waste & Air i olto oeigadSmltos Proceedings. Simulations. and modeling Pollution Air J. 47:551Ð581. eerhTinl Park, Triangle Research workshop, User’s Models-3 epy.Res.-Atmos. Geophys. note 35:602Ð610. hm nier Comm. Engineer. Chem. IMReview SIAM 4 2002.pdf. nio.Si Technol. Sci. Environ. J. epy.Res.-Atmos. Geophys. 54:1254Ð1263. http://emep.int/ EMEP/MSC-W, 26:195Ð239. epy.Rs Lett. Res. Geophys. 1:2S5 doi:10.1029/ 110:D22S05, mr .Rsi.Ci.Care Crit. Respir. J. Amer. J. i n atrManag. Waster and Air 151:53Ð64. nio.Si Technol. Sci. Environ. 35:2233Ð2243. mr er J. Heart Amer. eocl and Mesoscale 108(D9):4258, rh Environ. Arch. J. 26:307Ð i & Air 995 Downloaded At: 19:58 3 July 2008 aeeso itr itiuin,w oi htteobserved the that posit we data distributions, pa- the mixture estimating When of of 1984). Walker rameters concept and (Demster Redner the functions 1977; al. likelihood on et optimizing lognormal based for of is data mixture algorithm incomplete a EM of The parameters distributions. the estimate to ployed APPENDIX 996 h olwn itr onra distributions. lognormal mixture following the assuming without intractable cases analytically many is in process optimizing the since function data, hn,Y,Lu . ag . n aosn .(05 rdcigArslNum- Aerosol Predicting (2005) M. Jacobson, and K., Wang, P., Liu, Y., Zhang, Jimenez, and R., D. Worsnop, T., J. Jayne, R., M. Canagaratna, Q., Zhang, o,K . hn .R,Pi .Y . n isn .E 20b.Ueof Use (2001b). E. W. Wilson, Yu and H., Y. D. Pui, R., D. Chen, S., K. Woo, Measure- (2001a). H. P. McMurry, and H., Y. D. Pui, R., D. Chen, S., K. Multicomponent Woo, in Nucleus Critical the of Composition (1984). Aerosol, G. Wilemski, Sulfur of Characteristics Physical The (1978). T. K. Whitby, NC. Analysis, Process Algorithms Nucleation and Homogeneous CMAQ: with Distribution Size and ber Processes, and Sources Aerosol Res.-Atmos. for Geophys. Implications Submicron Pittsburgh: of Composition in Chemical Particles Size-Resolved and Time- (2005). L. J. Nucleation, ufc Area, Surface Particle Estimate to Parameters Aerosol Integral of Measurements Continuous Particle Ultrafine of Events, Observations Distributions: Size Aerosol Atlanta of ment Nucleation, Vapor Environ. ,F nEpcainMxmzto E)agrtmwsem- was algorithm (EM) Expectation-Maximization An ,adTro .(00.Utan eoo omto i Ion-Mediated Via Formation Aerosol Ultrafine (2000). R. Turco, and ., X Y r nopeeadcnie h xsec funobserved of existence the consider and incomplete are hssgicnl aiiae piiigtelikelihood the optimizing facilitates significantly This . eoo c.Technol. Sci. Aerosol 12:135Ð159. epy.Rs Lett. Res. Geophys. eoo c.Technol. Sci. Aerosol J. f hm.Phys. Chemi. ( 1:0S9 doi:10.1029/2004JD004649. 110:D07S09, eerhTinl Park, Triangle Research workshop, User’s Models-3 X |) 34:75Ð87. = 78386 doi:10.1029/1999GL011151. 27:883Ð886, i=1 80:1370Ð1372. K 34:57Ð65. Y α oepeiey e’ assume let’s precisely, More . i p i ( X |θ i ), .K AKE AL. ET PARK S.-K. Atmos. J. h ifrnebetween difference when the determined are parameters Final M-step. the and E-step the We where nta us fprmtr n h bevddt aldthe called data observed the the and function. expec- on parameters the conditional of compute function guess we initial log-likelihood E-step, complete the the of so-called In tation the M-step. two between the and repeating into E-step by divided performed 2.5 typically is than are gorithm less that size for particles modes of number the and oedtiso h Magrtmi vial lehr (Hogg elsewhere iterations. available 2005). is al. of algorithm et number EM the specified of the details More with satisfied or threshold h ucincmue nE-step. in maximize computed to function parameters of the estimate the update we M-step, the In n aine denoted variances means, and coefficients, their mixtures, the in components of number lyotie ypirkolde ee eset we Here, knowledge. prior by obtained ally i=1 K aaeeso itr onra itiuin nld the include distributions lognormal mixture of Parameters hnoti e fudtdprmtr yieaigbetween iterating by parameters updated of set a obtain then α i ={ = Q , (, 1. α i ,θ p i i },θ ( ( i X −1) ( i ) |θ i = ) i ={ ) K = r max arg = , ( E i α µ ) √ [log i i and µ , 2πσ Wib 98.TeE al- EM The 1978). (Whitby µm i σ , 1 i 2 and p } Q i ( ( X X for i , (, +1) , σ e Y i − 2 sls hntespecified the than less is i respectively. , |) [ln( = ( X i 2σ )−µ −1) i 2 1,..., X i ] ) , 2 , ( i −1) K K ] K uhthat such = susu- is 2as Q