Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 4 , T. Mielonen 2 , H. Petetin 1,3 9108 9107 6 , E. V. Berezin 2 , and M. O. Andreae 5 , M. Beekmann 1 This discussion paper is/has beenand under Physics review (ACP). for Please the refer journal to Atmospheric the Chemistry corresponding final paper in ACP if available. Institute of Applied Physics, RussianLaboratoire Academy Inter-Universitaire of de Sciences, Systèmes Nizhniy Atmosphériques, Novgorod, CNRS, Russia Université Paris-Est Lobachevsky State University of NizhnyFinnish Novgorod, Nizhny Meteorological Novgorod, Institute, Russia Kuopio, Finland Hydrometeorological Centre of Russia, Moscow, Russia Biogeochemistry Department, Max Planck Institute for Chemistry, Mainz, Germany Received: 16 January 2015 – Accepted: 13Correspondence March to: 2015 I. – B. Published: Konovalov 26 ([email protected]) MarchPublished 2015 by Copernicus Publications on behalf of the European Geosciences Union. I. N. Kuznetsova 1 2 and Université Paris3 7, Créteil, France 4 5 6 The role of semi-volatile organic compounds in the mesoscale evolutionbiomass of burning aerosol: a modelling case study of the 2010Russia mega-fire event in I. B. Konovalov Atmos. Chem. Phys. Discuss., 15, 9107–9172,www.atmos-chem-phys-discuss.net/15/9107/2015/ 2015 doi:10.5194/acpd-15-9107-2015 © Author(s) 2015. CC Attribution 3.0 License. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | e, ff and 10 ected by PM ff ∆ and CO data from 10 ect, (Jacobson 2001), as well ff and CO concentrations ( 10 9110 9109 ects associated with carbonaceous aerosol from CO observed in Kuopio (by almost a factor of two), ff ∆ / 10 PM ∆ ecting both climate processes and air quality (Andreae and ff Meanwhile, there are indications that the available chemistry transport models CO) measured in Finland (in the city of Kuopio), nearly 1000 km downstream of the 1 Introduction Carbonaceous aerosol originating from openin biomass the burning atmosphere (BB) plays byMerlet, a a ; 2001 majorLangmann role et40 al., %2009). of In the particular, atmospherictributes BB budget significantly to of is climate black estimated forcing carbonBB to (IPCC, emissions (BC)2013 ; provide are (BondAndreae also about known and et to, Ramanathan which al., be2013 ). a2013), contributes major which to source of con- both particulatebrown organic direct carbon matter (e.g., and (POM), Chakrabarty indirectsorption et radiative by al., BC forcing (up2010; by toSaleh a providingas et factor contributing absorbing of al., two) to),2014 due the to enhancingimpact the light light lensing of scattering e ab- aerosol (Keil emissions andworldwide from, Haywood (e.g., fires2003). onHeil Episodes the of and regional a Goldammer, air major 2001; qualitySinha have been et reported al., 2003; Bertschi and Ja ficiently accurate. For example, thein concentrations Central of America aerosol were originating systematically fromperformed underestimated wildfires by (byWang about et 70 al.(2006) %)spite with in of the simulations the RAMS-AROMA regional factthe transport that simulations). model the Predictions (in variability ofBlueSky of surface Gateway the (Strand aerosol et aerosol concentrations al., concentrationacceptable2012) in range was air California of quality well the from modeling captured observed system the in values in were northern in found one California), to part be but in negatively of biased the model in domain the (specifically, other part of the domain (in south- (CTMs) simulating sources and atmospheric evolution of BB aerosol are not always suf- 2005; Konovalov et al., ; 2011 physicalStrand and et chemical, al. 2012; propertiestoEngling of be et BB al., adequately aerosol,2014).and represented Therefore, and predicting the in its climate atmospheric changes sourcesGoodrick and numerical and et air models evolution al., pollution2012). have aimed phenomena, (e.g., atKiehl analyzing et al., 2007; Abstract Chemistry transport models (CTMs)dicting are atmospheric an and indispensable climate tool e for studying and pre- open biomass burning (BB);to both this global type radiative ofwildfires. forcing Improving aerosol and model is to performance known episodes requiresresults with to systematic of measurements comparison air contribute of of BB pollution significantly ancies simulation aerosol in and between regions elucidating them, possible a which, reasonsuncertainties for “by discrep- in default”, are emission frequently data.spheric attributed Based evolution in of on the BB published literatureto aerosol laboratory to organic and data aerosol by regarding modelingimportance using atmo- along the of with volatility taking a basiscompounds “conventional” gas-particle set (SVOCs) approach, into (VBS) partitioning we account approach and in examinedplumes simulations the oxidation from of intense of the wildfires mesoscale semi-volatileof that evolution of organic occurred primary smoke in aerosol westernthe components Russia air in were pollution 2010. constrained monitoring BBtions with network emissions performed the in with the the PM CHIMERE Moscowthe CTM ratio region. of were The smoke-related evaluated results enhancements by in considering, of PM in the particular, simula- the VBS approach is capablethe to ground bring measurements the both simulationsthe to in conventional a approach Moscow reasonable is and agreement alsoment in found with to Kuopio. of result Using simulations in the a andin VBS major satellite considerable improvement instead measurements of changes of the of in agree- emission aerosol predicted estimates derived optical aerosol from depth, composition AOD as measurements. and well top-down as BB aerosol ∆ fire emission sources. Itoxidation of is SVOCs found and assuming thatunderestimates organic while values aerosol of the material to conventional be approach non-volatile) strongly (disregarding 5 5 10 25 20 15 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | man et al., ; 2009 May et al. , 2013) ff 9112 9111 ects of wildfires and has already received considerable ff The main goal of this study is to examine the impact of using the VBS approach in- A general novel approach to OA modeling, known as the volatility basis set (VBS) Although most modeling studies tend to attribute systematic discrepancies between our model to the casemer of 2010 the as mega-fire a eventprovided result that abundant of occurred observational an in abnormal material Westernderstanding heat for Russia of wave in the atmospheric (Barriopedro sum- critical e et evaluation al., of2011). our This current event un- stead of the conventional one(though on episodic) the simulated situation evolution when ofBB BB BB aerosol was aerosol processes a in by an major using important source data of of OA. dedicated We laboratory parameterize measurements the and apply Donahue et al.(2006). Severallution studies of applied OA thisemissions from approach and anthropogenic found for (fossil that modelingmeasurements it fuel the (see, provides burning) evo- e.g., reasonable and agreementLane2010; between et (in simulationsHodzic, al. some and et2008; cases)Murphy al., et biogenic and al., 2010; , Pandis ; 2012 Tsimpidi; 2009 Zhang ettoFarina et et, al. modeling al., al., BB2010; 2013). aerosolShrivastavaandBergström along et biogenic et with sources, al., OA al. (2012) but; 2011 did originatingadvantage appliedAhmadov not of from the the arrive predominantly VBS at VBS anthropogenic approach approach anyaerosol; over unambiguous note a conclusion that simpler regarding their (“conventional”) an VBSfrom one biomass scheme in burning did the and not case other distinguish of sources. between BB the properties of OA approach, which is intended toorganic represent compounds the volatilities and of their a ageing broad processes spectrum in of the primary atmosphere, was introduced by tration was also diagnosed2009; inAkagi some et field al., 2012). studies tial Recently, (HobbsVakkari growth et et and al.(2014) al., increasing showed2003; evidencefew oxidation hours forYokelson state of substan- et of atmospheric al., biomass transport. Meanwhile,ployed burning in all aerosols the the during chemistry above transport mentioned theaerosol simulations models first emissions em- of as BB non-volatile, aerosol and evolution onlycompounds treated oxidation (VOCs) the of was primary taken several definite into volatile account organic as a source of SOA in some of the models. simulations and atmosphericemission observations inventories, of it BB seemsancies aerosol also may to quite be probable uncertainties due thatcesses. in to at Indeed, deficiencies the for least the in fire a special theburning, part case it modeling of of has organic representation been the aerosol argued of (e.g., (OA) discrep- son BBShrivastava originating et et aerosol from al., , al. 2006; fossil pro- 2007)Donahue fuel et thatvolatility, al. adequate of2006; primary modelsRobin- OA of (POA) OA compoundsfrom evolution as require oxidation well taking as of into formation of account semi-volatilethermore, secondary the laboratory organic OA measurements (SOA) compounds indicated (SVOC) that,fuel like burning, in the BB the POA aerosol emissions atmosphere.and emissions from Fur- Robinson, feature fossil a2006 ; broadGrieshop spectrum et of al., volatility2009b; (e.g., Hu Lipsky ern California). Large regionalGOCART CTM biases were in found by AODorderPetrenko to simulations et achieve(2012). al. performed aKaiser reasonable with et agreement(AOD) the with of al.(2012) corresponding global found global satellite simulations that of measurements, in in the aerosol BB optical the depth aerosol ECMWF emissions integrated specified 3.4. forecast Using system AOD had and to carbonbination be monoxide with increased (CO) outputs globally satellite of by the measurements(qualitatively a mesoscale analyzed similar CHIMERE factor in to CTM, of com- theKonovalov resultscarbon et by monoxide(2014) al. Kaiser found emissions et from, al. about forest2012) and a that grassland the factor fires ratios oftors in of 2.2 from aerosol Siberia literature. and and are In 2.8 likely contrast, CHIMERE to largerKonovalov simulations et be than to al.(2011) ground those revealedsia, based that calculated the observations in BB with during order aerosol typical to wildfires emissionsCO fit in had emission emissions. the to western fac- be Rus- scaled with a factor of about 0.5 relative to the and may be subjectamounts of to SOA rapid (Grieshop oxidation et2012; al., processesOrtega; 2009a et leadingHennigan al., et to2013). al., An formation increase2011, of of2012; BBDonahue substantial aerosol et mass, al. or particle number concen- 5 5 25 15 20 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | -line ff ects of fire emissions (Hodzic ff 9114 9113 ecting the atmospheric evolution of aerosols of various types and ff cient mechanism in the case of ozone simulations was very similar to that of ffi While most earlier CHIMERE applications addressed contributions to atmospheric This paper is organized as follows: Sect. 2 describes our modeling framework; in a much more complex mechanism,were such as calculated SAPRC07 with (Carter, the2010). TUVas Photolysis model rates a (Madronich et function al., of1998) embedded AOD in derived CHIMERE from Moderate-resolution Imaging Spectroradiometer 2.2 Basic model configuration Gas-phase processes wereCHIOR2 simulated with (Derognat the etof reduced al., 40 chemical2003; species. mechanismMenutallyMenut MEL- et e et al., (2013) al. 2013) found including that about the 120 performance reactions of this computation- et al., 2007; Konovalov etlations al., performed2011, with2012, CHIMERE2014; wereagreementPéré found et with by al., airKonovalov).2014 quality et In monitoringevent) al.(2011 particular, to data caused simu- be in by in Moscow wildfires good duringin in the this 2010. extreme study. air The The pollution valov same et CHIMERE event al.(2011, configuration and2014), except is similarmethod for aimed similar data some at changes are to deriving and considered thatpower updates fire (FRP) in mainly emissions (Ichoku applied the from and to, Kaufman satellite studies our 2005). measurements by ofKono- fire radiative composition from anthropogenicplied and in biogenic several sources, studies it focusing on was the also atmospheric successfully e ap- ticular, emissions of gasesinterfaces and enable calculation aerosolsdata (the of of anthropogenic the corresponding and corresponding emissionpounds biogenic inventories), emissions chemical emission due on transformation a of tosome tens model semi-volatile gas-phase of grid species com- and by/from from of aerosol heterogeneous gases particles, and reactions, advection aerosols,CHIMERE and absorption/desorption and turbulent and their of mixing examples dry(2013) of and wet its and deposition. numerous in Thecodes applications the at detailed are. http://www.lmd.polytechnique.fr/chimere CHIMERE description provided of documentation in availableMenut online et along al. with the model This study is based on using the CHIMERE CTM, which is a typical Eulerian o our simulations for predictingwildfires by aerosol using composition the and “top-down”some approach. estimation concluding A remarks of summary are of emissions provided the from in results Sect. of 4. this study and 2 Model and measurement data description 2.1 CHIMERE CTM: general characteristics particular, it outlines the methods andsions parameterizations representing and BB evolution aerosol emis- andof defines the the numerical simulations scenarios are of presentedlite our in measurements comparison numerical with in experiments. data Sect. Results of 3, in-situ and which satel- also discusses the implications of the results of attention in the scientificet literature al., (Elansky2011; etWitte2013; , al. etPopovicheva et2011; al., , al. 2014).Konovalov2011; However, to etGolitsynno the study, al. best et yet of focusing2011; al., our on knowledge,Mei 2012; modeling therethese theHuijnen has evolution fires. been (“ageing”) et By of, al. aerosol considering in2012; benefits this BB of plumesKrol special using from et the case, VBS we al., approachcially intend for at modeling to temporal aerosol examine scales evolution in the considerablymeasurements. BB exceeding feasibility plumes, In those and espe- addressed general, in thisunderstanding typical of laboratory study BB is aerosol processes intendedmodels. and their to representation contribute in chemistry to transport advancing current model designed fortinental simulating scales. and It predictingical includes air processes parameterizations pollution a of at most the important regional physical and and con- chem- origin (such as primaryand anthropogenic, organic dust, aerosols) biogenic, and sea gaseous salt, air secondary pollutants. inorganic These processes include, in par- 5 5 25 15 20 10 20 15 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). Some 1 50 km grid − and twelve g ◦ × 2 [drybiomass]) 1 is the assumed − el 0.8 m h , ± l ) is the factor converting FRP 1 − cient. We took into account that W ffi 1 ), were calculated as follows: (g[modelspecies]g . This relationship follows a popular − 2 E). The simulations were performed s τ l − ◦ β m , 1 l − cient, using the experimental data by Reid ciency coe is an additional ad hoc correction factor spec- 9116 9115 ffi ) (gs ffi t N; 20–56 C ( ◦ s E , (g[drybiomass]s t α ), (1) is the fraction of the land cover type τ ( l at time erent from that of BB aerosol. We evaluated this bias as the C ρ ) ff s t ( el h l ρ ) is the daily mean FRP density derived from daily maximums of FRP s l erence between the simulated and measured AOD in the grid cells on 2 ff − αβ ciency is di l ffi X (Wm d d Φ = Φ ) t ( The evolution of BB plumes was simulated with a resolution of 0.5 by 0.5 The WRF-ARW (v.3.6) model (Skamarock et al. , 2005) was used as a meteorological Apart from using the standard model output data for concentrations of gaseous and s ified as a function of AOD at 550 nm wavelength, in a given cell of the model grid, are the emission factors, diurnal variation of fire emissions, and where layers in the vertical; thestudy upper region layer corresponded (corresponding to toand the the a 200 hPa model part pressure domain) of level. The Eastern covers Europe most (48–66 of European Russia Below, we outlinechanges our with calculations respect to ofemissions the for fire previous a studies, emissions species where by a similar paying method special was attention used. Fire to for the period fromthe 12 model’s July “spin-up”; to therefore, the 20 period August of 2010. our The analysis2.3 first began three on 15 days Fire July. were emissions reserved for E with 30 levels extendingYamada–Janjic in (Eta) the scheme vertical, (Janjic up1994)layer processes to was together the with used the 50 for hPa Etatheory) the similarity pressure for scheme simulation surface level. (based physics of The on, (Janjic boundary the Mellow– 1990). Monin–Obukhov to the biomass burningconversion rate factor) (BBR) for a (below, given we land refer cover to type this factor as the FRP-to-BBR driver for CHIMERE. The meteorological data were calculated on a 50km et al., ,2014 forvalues. further detail); the bias was then subtracted from the simulated AOD mean relative di tions by applying the mass extinction e bias in AOD valuesconsidered) calculated contributions in of this anthropogenic, way biogenic,extinction may and e be dust aerosols, associated whose with mass small (in thethe case days where and when the contribution of BB aerosol was negligible (see Konovalov aerosol species, we considered AODin Konovalov at et 550 al.(2014) nm; following it a(2005). robust was Specifically, method, AOD evaluated proposed was in by derived theIchoku from and same simulated Kaufman aerosol way mass as column concentra- (MODIS) measurements (see Konovalovsecondary et inorganic aerosol al., was2011, simulated withnamic for the model further tabulated ISORROPIA versionNenes ( detail). of et the al., aerosol Evolution thermody- 1998). were of Anthropogenic specified emissions by of using gasesgramme) and inventory the data EMEP (EMEP/CEIP , (European)2014 Monitoringaerosol for the and emissions year were Evaluation 2010. distributed Pro- Anthropogenic among10 primary nine µm size by bins assuming with a diametersand bimodal from 1.6 20 log-normal nm for distribution to the withcordingly fine a to mode mean the and and CHIMERE of SDaerosol standard 4 of µm settings). precursors) 0.11 and µm Biogenic were 1.1 emissions calculateddata for (including by the by those the course using of MEGAN mode, the(Guenther (Model respectively of standard (ac- et Emissions CHIMERE al., of interfaceof Gases2006) and and soil for Aerosols NO emissions from emissionsaccount Nature) from by by model vegetation,Stohl using and et amonthly al.(1996). the simple climatological parameterization Dust European data developed aerosol from inventory by were emissions the usedVautard LMDz-INCA were as et global taken initial). al.(2005 model and into The boundary (Folberth conditions et for al., our) 2006 simulations. a predominant part of atmosphericto aerosol loading biomass in burning the situation andet considered was chose al.(2005), due to this be coe the same as in Konovalov et al.(2014) (4.7 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , C (3) is taken to 0 α are inferred from α F ). The diurnal cycles , and the “a posteriori” ml 0 h α ) such that its average over the : τ ) is applicable to both forest and ml ( α h C F erent estimates of ff (below, we refer to this factor as the FRP- ect of cloud and smoke contamination on ff α 9118 9117 is the assumed diurnal distribution of the FRP erences between them. First, in this study, we . To estimate the latter, the formulations given ); introducing this factor was found to drastically ff , and di el τ ml 1 h − h W 1 − erent modeling scenarios in this study. Note, however, that is the moment of time when the daily maximum of FRP is ff } ) to minimize the e was implicitly assumed to be equal to max ◦ t ) el ...K h : 1, max α t ( F = ml k g[drybiomass]s , h 4 l k − ρ Φ , and that of emissions, ) to be proportional to exp( { 10 P τ is the satellite orbit index, . (2) ml ( × α h k C F max 0 , and non-methane hydrocarbons (NMHC) (see Table 1) were specified using an α = x While Eq. (1) in combination with Eq. (2), is very similar to Eq. (5) in Konovalov et al. Second, for convenience, we normalize the factor Third, instead of assuming very strong diurnal variation of fire emissions (see Kono- For convenience, we express the factor Similar to Konovalov et(2011, al. 2014), the daily mean FRP density is evaluated by d = (2011), there are a few noteworthy di updated dataset (M.O. Andreae, unpublishedemissions data, of individual 2014; VOCsAndreae were calculated andamong by Merlet, the distributing2001); the compounds total represented NMHC emissions in this database (proportionally to the measured valov et al., 2011 ,directly and from FRP Fig. observations 1 usingvalov therein), the et we method) al.(2014 and derived (see formulations thethis proposed Eqs. diurnal by 5 methodKono- cycle and further 6 ofmums, therein). by the In emissions distinguishing this between study,in we the attemptedKonovalov diurnal to et advance cycle al.(2014)was of derived were only FRP applied from daily FRP toet daily maxi- al.(2014), all maximums where (exactly available in FRPspecified the same data, in way this while as study in theare forKonovalov former shown agricultural in and grass Fig.NO fires, 1. and Finally, (separately) the for emission forest factors fires for organic carbon (OC), BC, CO, do not consider the peatfrom fires explicitly. peat Although fires the attempt (not torather visible estimate successful, from the this emissions space), estimation was asonly associated described hinder with evaluation in a of largeKonovalov di uncertainty, et which al.),(2011 would was grass fires (visible from space). improve the agreement of our simulations with air pollution measurements in Moscow. we still take peatfactor. fires For into similar account reasons, implicitlyvalue by we (and adjusting assume the the that same FRP-to-BBR the value conversion of same the FRP-to-BBR correction conversion factor, factor whole study region isdefine equal to unity. Note that, following Konovalov et al.(2011), we approach to calculation of fire)(2005 emissions, and which has was been proposed used by etIchoku in, al. and a Kaufman number2012; ofKonovalov studies et (see,, al. e.g., 2014,Sofiev et and al., references2009; therein)Kaiser since then. The factor the selected FRP daily maximum values;the these model emission grid. data were The then temporal projected resolution onto of the emission data was 1 h. where daily maximums, and observed. The initial calculationsresolution of (0.25 by fire 0.1 emissions were made on a grid of a higher which was initially introducedfor a in possibleKonovalov attenuation et ofintense al.),(2011 FRP fires measured is in from the intended region satellitespart to and by of period compensate very emissions considered; heavy from it smokeor peat is from grass fires also fires. assumed invisible from to account space for but the coinciding with visibleto-BBR conversion forest factor) as the product of its “a priori” value, atmospheric measurements as explained in Sect. 2.6. selecting daily maxima of thewith FRP the density assumed in diurnal each cycle model of grid the cell FRP and maxima, by scaling them Φ correction factor, α Taking into account the experimental data by Wooster et al.(2005), be 3.68 5 5 25 15 20 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | POM ratios / producing sev- 3 and NO erences between results ff 3 ected the model performance only ff 9120 9119 erent methods for modelling BB OA evolution. The first ff The formation of SOA is represented by absorption of SVOCs produced as a result Similar to Konovalov et(2014), al. the injection of fire emissions into the atmosphere lumped model VOC species (SVOCeral precursors) surrogate with SVOC OH, species. O Thesethree three classes lumped of species VOCs, aresubstituted such assumed aromatics as to including a benzene, represent and class asame of class single-step of alkanes polysubstituted oxidation from aromatics. mechanism C4 The introduced by to followingPun C13, the et a formulations(2006), al. class by used with of toKroll some represent mono- et modifications the al.(2006) formationisoprene and of andZhang SVOC terpenes). as et a Further al. (2007), result detailsin is of the regarding oxidation standard the of version representation biogenic of of VOCsHodzic (for CHIMERE OA et can processes al., be2009; foundMenut elsewhere et (Bessagnet al., et). 2013 al. , 2009; of oxidation of primaryof VOCs SVOCs (Bessagnet et from al., oxidationscribed2009; of byHodzic a VOCs et single-step from al., oxidation both2009). mechanism (Pun The fossil et yield fuel al. , and2006) biomass as reactions burning of is three de- 2.4.1 “Standard” method for organic aerosol Aerosol particles emittedvolatile from POM and fires BC. Therefore, are theyof cannot conventionally evaporate deposition and assumed and can be transport to lostdistributed only outside according consist as to of a a of result the lognormala size non- model distribution SD domain. with of a Primary 1.6 mean OA by(see, diameter of taking emissions e.g., 2 into µm are Fiebig account and et fresha al., smoke typical observations2003). mean reported A diameter inmass coarse of of the fraction fresh literature about aerosol of 5 emissions µmas primary (and, and indicated, probably, aerosol even e.g., usually a particles by contributing smalleranalysisAlves having 10–30 part of et % of simulations al., to organic performed2011) the carbon with was the total disregarded standard to and VBS facilitate method. the comparative emission factors of these compounds)model and species then (similarly aggregating as thempogenic it into emissions, is eleven see lumped doneMenut inthe et OC the al., emissions CHIMERE with).2013 emission a POM factor interface emissions of for 1.8, are anthro- taking obtained into by account the scaling range of OC sphere. We consider this methoduniform distribution as of advantageous fire over emissions aployed up in simpler to methodKonovalov the et (assuming height), al.(2011 ofobtained with one although these kilometer), no two which methods significant was wereet di em- revealed in al., the2014). case of We Siberianemissions would fires with (Konovalov like respect to emphasize to that the the previous changes studies in a our calculations of fire was simulated by using the parameterizationrameterization proposed enables by evaluationSofiev of et maximum al.(2012).measured plume This in pa- height a as a given function fire of pixel the and FRP of the Brunt–Väisälä frequency in the free tropo- observed in fire plumes2011; van and der assumed Werf in et, al. fire2010). emission inventories (e.g., Alves et al., slightly and could not influence the major conclusions of2.4 this study. Representation of BB OA processesIn in this CHIMERE study, we employ two di method is used inon the the VBS standard approach version (Donahueand of et was al., CHIMERE. initially2006; The implementedRobinsonoriginating second et in from al., method dedicated fossil; 2007 fuel is versionsLane burning ofet based et and al., CHIMERE al., biogenic).2013 2008) for emissions A (Hodzic theto description et case modelling al., of of of2010; these OA BBZhang methods aerosol. given below focuses on their application 5 5 20 25 15 10 15 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , oc β erent is the ff R . For compar- , are assumed 3 − oc β and the smaller the OA erent volatility distribu- ff C 10 mgm , (4) = 1 − , and the fraction in the total OA i     1 C H − is to unity. In contrast, for small ∆    η cient. We tried to take into account oc T ffi concentrations during their emission 298 β 
2.5 298 K and 1 ) was disabled. This restriction did not af- over 298 3 = ect on our simulations because the total BB − 9122 9121 − ff T poa 1 T β OA i C H R ∆ 0.1 µgm − (assumed to be equal 1.8 here) is applied to convert =  η ∗ is the index of a volatility class). The emission factors for C , the larger is the fraction of POA emissions in the particle erent types of biomass) and depend, in particular, on the exp i ∗ i , and for organic carbon in particles (OC), ff ∗ i ) even after strong dilution. C 3 C − . In addition, we assumed that all POA were released into the and poa 3 + β 3 − 1 − (where are the ambient OA mass concentration and temperature,    i at 298 K, enthalpy of vaporization, , a large part of POA emissions occurs in the gas phase and is not i f ∗ i f T ∗ i 10 µgm C and ambient temperature. Note that disregarding the gas-particle con- i C > X OA     and 0.01 µgm η C = oc OA ∗ β C C = and large Volatility distributions of POA were specified by using the results of a dedicated lab- To improve the consistency of our model with the kinetic model used by May et al. OA poa tions described in Sect. 2.7.according POA emissions to were the distributed among same(see nine size Sect. size sections 2.4.1). distribution as described above for the(2013) standard for method volatilitytion estimations, scheme we in slightly CHIMERE.process Specifically, modified we based the replaced on the kineticBowmanSutugin formulation et part interpolation of(1997) al. formula of the withSeinfeld absorption an and theical(2006). Pandis approximation stability absorp- In based of addition, on to our the insure calculations,(with numer- Fuchs– evaporation of POA infect the our two results, lowest since volatility typical classes much OA higher concentrations ( in the smoke plumes considered were needed. Specifically, we assumed that this uncertainty by considering two simulation scenarios with di ison, Vicente et al.(2013) reported that PM assumed value of the mass accommodation coe POA emissions, distributions and enthalpies of vaporizationemissions. from Unfortunately, thermodenuder the measurements derived oflarge volatility BB distributions uncertainties are (which characterizedsmoke likely by from reflect very burning a of part di of the natural variability of volatility of total POA emissions, β oratory study by May et al. (2013), in which a kinetic model was used to derive volatility to be related2006): as predicted by partitioning theory, (Pankow ; 1994 Shrivastava et al., factor measurements in thefrom 0.69 vicinity to of 25 mgm wildfiresatmosphere from in fires Portugal as particles werehigh (as in a ambient result the concentration of broad of theassumptions condensation range combustion do process not products under very have afteraerosol a their emissions significant were initial e constrained cooling). by measurements, These as explained in Sect. 2.6. 2.4.2 Volatility Basis Set (VBS) method Here, POA emissions (including all organicto material that form is OA assumed to particlesand have distributed a under potential into atmospheric several volatilityconcentration conditions) classes are characterized by considered the as reference saturation semi-volatile where OC into POM. In Eq. (4), the larger the ambient concentration gas constant, and the factor saturation concentration phase, and thus the closer the ratio C accounted for in measurements of particulate phase emissions. While the factors measurements of the emission factors. Therefore, some additional assumptions were characterizing emissions of OC from biomassboth burning, in laboratory have been and field frequently studies measured and (see, e.g., areAkagi widely et used al. , in2011,values and emission references reported inventories therein) (see, in e.g., thevanregarding der literature Werf are et usually, al. version2010), not processes their accompanied may account by for corresponding a data part of the large discrepancies between di 5 5 25 20 15 10 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) and duration of the 3 − and less, and several hours, 3 − 9124 9123 except for a test scenario (see Sect. 2.7) in which the 1 − s 1 − n-alcane). Accordingly, we assumed the same OPOA mass yields 15 molecules 3 − cm erent ways. First, based on the estimates derived by Grieshop et al.(2009a) ff cult to predict a priori which of the parameterizations would enable the best 11 ffi − 10 Along with SOA formation resulting from the absorption of OPOA, we took into ac- Note that the substantial increase of BB aerosol mass due to oxidation processes The POA were assumed to be subject to gas-phase oxidation, which was repre- × count a minor (underof conditions “traditional” of SOA this precursors.adapted study) A from SOAZhang modelling source et scheme associatedanthropogenic(2013): al. with accounting it VOCs oxidation for simulates by this the usingfour formation source six volatility of classes. was lumped SOA The species from BB representinggated emissions oxidation from of of SOA these emissions precursors lumped of and SOA individualwith precursors VOCs recent were using updates aggre- (in the the data same of way asAndreae the and emissions Merlet(2001) of other model organic species). aerosol evolution was much longerto in those the in simulations the (more laboratory than experiments one (about day), 100 µgm compared ing a two-dimensional VBSsuch a scheme potentially (Donahue important et2011), process al., as was fragmentation2012) not (Chacon-Madrid feasible and andthe in taking Donahue, absence this of into study suitable account parameterizations. due to the lack of robust experimental data and was also found inwere laboratory not experiments fitted by toOrtega VBSGrieshop et models al.(2013); et (unlike however, the al., their measurements data Note2009a) in also andHennigan that et thus al. , using were 2011 aand less more complex suitable representation for of configuring BB our OA simulations. evolution, e.g., involv- surpassed that addressedcentrations in were the typically laboratory much higher experiments. (up In to almost particular, 3000 aerosol µgm con- respectively). Besides, ageing ofcal aerosol for emissions European Russia fromyet (e.g. many been Scotch investigated kinds in pine, of(Jathar laboratories. Norway “fuels” et On spruce, typi- al., the elm, other2014 ; yields birch, hand,Grieshop in etc.) even separate et has the experiments, al., not laboratorythe which parameterizations2009a) studies was outlined indicated not above. a reproduced large by box variability models of employing the SOA performance of ouron simulations the in one the hand, special the range case of analyzed conditions in reproduced this in study. our Indeed, simulations significantly rate was doubled. EvolutionPOA of with oxygenated OH POA was simulated (OPOA)erned in produced by the partitioning in same theory way the and as experiencedmass reaction successive that increment oxidation of of as at POA the POA). (that same Second, rate the is,using and SOA OPOA a formation were from gov- “surrogate SVOCs was species”unspecified parameterized representing in a available mixture(2014). emission of The numerous inventories, parameterization, organic as compounds whichtions proposed had to recently the been data by obtained ofet theJathar by al.(2011), biomass fitting et represents burning the laboratory box al. POA experiments modelwith oxidation described as a simula- in aHennigan minor single-generation net process lossthe (associated of VBS the SOA total yields masspentadecane from of (C the POA POA and oxidation OPOAas are species) those similar and given assumes to in that thosewithJathar from the et oxidation analysis al.(2014) of in (seenot n- Jathar Table S3 only et therein). POA(2014), al. In species, webiomass addition, assumed but burning. consistently that Note also that n-pentadecane amore the represents fraction experimental representative data (10 of %) by a ofHenniganAmerica) range than et the of those al.(2011) total obtained real are and NMHC biomass likely was analyzed by burning emissions di Grieshop conditions from et (at al.(2009a). Nonetheless, least, it in North from laboratory measurements of thereaction oxidation was of assumed BB to smokeclass) reduce from by the a a volatility wood of factor stove,increase each organic of the gases 100 organic (from (leading compound mass a to by2 given a 40 %; volatility two-bin the shift reaction rate in constant the was volatility set to distribution) be and to sented by the reaction ofin POA two with di OH. The oxidation mechanism was parameterized 5 5 25 20 15 10 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ◦ is 10 10 10 1 10 i × θ ◦ were obtained ◦ , involved in the erent estimates 1 ff α F × ◦ measurements at the au- 10 measurements by minimizing the 10 9126 9125 ected by fires (here, those were the days when ff is the bias which was estimated as the mean dif- ∆ is the total number of days in the period considered, data for at least 50 % of days during the period addressed d : , (5) N 10 2 J  ∆ − are the modelled and observed daily concentrations of CO or PM i o and CO measurements, respectively. The measurements were nomi- V o measurements from the city of Kuopio, Finland (Portin et al., 2012). and CO measurements, respectively. By comparing relative perturba- V 10 − and CO in the Moscow region (that is, near the fires) and in Kuopio (situ- 10 10 i m 10 V and  i m θ V 1 d were derived independently from CO and PM = N i X α F The observational data were averaged on a daily basis (the days were defined in We also evaluated our simulations against aerosol optical depth (AOD) retrieved Along with the air pollution data from the Moscow region, we used simultaneous = is the index of a day, of where following cost function, J the relative contribution of fire10 %) emissions to and the zero simulated otherwise, CO concentration and exceeded as the L3 MYD08_D3/MOD08_D3Visualization data and product Analysis from systemAOD the daily). (http://daac.gsfc.nasa.gov/giovanni/ NASA data The Giovanni-Interactive were MODIS matched to the simulated AOD values re-gridded to the 1 relationship between FRP and the emissions (see1 Eqs. and2). Di the operator equal to unity for days a We calibrated the fire emissions by estimating the correction factor, grid and averaged over theperiod period of daytime from satellite 10 overpasses). toafter The additional 14 same h spatial measurement of and data temporal local weremodel, interpolation introduced solar which (Konovalov (as time et noted al., (that above)2011) is, was into used over the to the TUV calculate the2.6 photolysis rates in CHIMERE. Optimization of fire emissions i UTC) and matched to thethe daily locations mean of simulated the concentrations stations. from Thein grid observational the (or cells simulated) Moscow covering data region for for the a selected given sites day were combinedfrom by MODIS averaging. measurements onboard(Remer the et AQUA al., and2005; Levy TERRA et satellites; al., 2010) the with AOD the data spatial resolution of 1 nally taken three times per hour. CO and PM tomatic air pollution monitoring stationsomonitoring” of for the calibration State Environmental ofvided Institution fire both “Mosec- CO emissions. and Wein PM selected this only study those (fromsites, sites including 15 those that July located inside pro- to ofMoscow’s 20 the suburbs city August (“Pavlovskii of posad” 2010). Moscow and (“Kozhuhovo”,equipped These “MGU”) “Zelenograd”). and criteria with The in were selected Thermo satisfied stationson TEOM1400a were for the and four Tapered OPTEK Elementployed Oscillating K-100 for PM Microbalance commercial and devices electrochemical based methods em- A Thermo TEOM 1400aused and for Monitor PM Labstions 9830 of PM B IRated absorption about CO 1000 km analyzer frommass Moscow), were we due attempt to to transformation elucidate andmeasurements the loss in changes processes in Kuopio in BB were the aerosol ciated atmosphere. earlier The with found CO to transport and reflect PM of2012; largeMielonen smoke et air plumes, al. pollution2011). from eventsable The asso- fires against contribution of “background” in BB conditions emissions Russialevel in was there to clearly Kuopio, is distinguish- Finland particularly typically (Portin because verymeasurements, et the low. only Although air one al., the site pollution city (Maaherrankatu) of provided Kuopio data has from several both sites CO for and PM PM 2.5 Measurement data Similar to Konovalov et al.(2011), we used the CO and PM measurements; therefore, the data fromation only of this our site model were performance. used for quantitative evalu- 5 5 20 25 15 10 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , and 1. To i = α F alone. 0 and α = α involved aerosol data F α F ects involving possible interfer- ff were used to obtain the “top-down” α F reported below reflects the uncertainty from satellite (MODIS) AOD measure- α α F F 9128 9127 ects caused by changes in the aerosol scheme ff by using ground based measurements, a similar α F erences between the simulated and measured data was set to be zero). ff is the total number of grid cells in the model domain, i , (6) c θ 2 N  was derived, under the assumption of linear dependence ij (see Eq.1), rather than the uncertainty in α ∆ s F − β ij o V was formulated in the same way as in Konovalov et al.(2014): and − are the simulated and observed AOD values for each grid cell, J α ij ciently isolate direct e m o V ffi V  ij , from results of “twin” simulations performed with θ α and F 1 c erences between the optimized simulations and the measurements similar to is the selection operator taken to be unity when relative contribution of fire = N ff m i X , in our AOD simulations was the same as in Konovalov et), al.(2014 except that V on ij 1 d was obtained either from CO data or using the “standard” aerosol scheme), the , of our model domain, 0 used for estimating of the bias in the situation considered in this study was too ∆ = θ j N j X m α = was estimated from results of the Monte-Carlo experiment involving bootstrapping derived from the initial twin experiment. Otherwise (for the cases when the estimate V F The initial estimate of In addition to the estimation of = ij α α on the evolution ofence BB of aerosol BB from and anycarried other less out types direct “background” of e simulations aerosol, (labelledbut our below with simulations as all included “BGR”) the two without othergenic stages. fire assumed emissions). First, emissions aerosol Taking we sources into (suchand account as evaluated that anthropogenic, in the dust simulations VBS andbackground of bio- scheme conditions aerosol had by evolution not in usingof ever Russia, the been we BB standard used opted aerosol aerosol to scheme.emissions was simulate Second, from the simulated the the other evolution by sourcestrations running and of CHIMERE with aerosol zero species with boundarymodel were fire conditions. runs. calculated Finally, emissions Such as concen- a the butSOA configuration sum without and of of SVOCs our the originating simulations outputs fromThis implies from fires may that these are not not two the be interactingwhich POA, exactly could with as be true, other well used types but as to ofwe presently describe aerosol. disregarded and there formation evaluate are such of interactions. nointo secondary For account available the inorganic that parameterizations same aerosol according reason, to from e.g., bothWitte fire et our emissions. al., simulations Taking 2011) andfires air an pollution were independent levels mostly analysis over determined the (see, interaction study by region of BB in BB emissions, the and we period expect other of intense that emissions the on impact the of results possible of this study is insignificant. Re- small). 2.7 Configuration and scenarios of simulations To be able to e within a “moving window” coveringover 15 the consecutive whole days, period the of averagingθ was the performed study (because otherwise the number of data points with here, instead of averaging the di emissions to the simulatedbias, AOD exceeds 10 % and zero otherwise. Estimation of the and day, ference between measurements andpollution conditions simulations (i.e., on when days featuring “background” air where of J estimates of total BB aerosol emissionsthe in cost the function study region (see Sect. 3.4). In this case, ments. The AOD-measurement-based values of discussed in Konovalov etF al., 2011 and ofKonovalov the et di al. , 2014). The uncertainty in cordingly, the uncertainty in theof estimates the of product of procedure was used to derive estimates of additional iteration was nottween necessary fire emissions because and the aerosol nonlinearity concentrations was of negligible a (similarly relationship to be- the cases Konovalov et al.(2014), except that possibleexplicitly uncertainties taken into in account emission in factors the were Monte-Carlo not experiment carried out in this study. Ac- achieve higher accuracy in the case when the estimation of of from VBS simulations, the estimation procedureF was re-iterated using a model run with 5 5 10 15 20 25 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) γ 0.76 = r cient ( ffi erent types of fuel; strong ) considered by May et al. 3 ff − 0.88 in Moscow and , their results indicate that this = µgm r OH k 4 10 = 1.0). An additional simulation was made ∗ C = (derived from CO measurements in Moscow γ ( 9130 9129 α γ F erent experiments with di ff is also indicated by a significant divergence of OA mass enhance- OH k cient, we present here only the results obtained with the most probable ffi 0.1, but since its results were found to be very similar to those obtained with = γ 1.0, they are not reported here. Figure 3 shows the evolution of CO in the Moscow region and in Kuopio according to We considered several model scenarios with fire emissions, including the scenario = and applied to emissions oflow) all of gaseous 1.88; species the in uncertaintySD all of of to this the be estimate simulations 1.14. was discussed Both evaluatedenhancements be- in the of terms model of CO and the observations concentrationKuopio geometrical demonstrate (in in episodes the both of end very Moscow ofserved strong (mainly July time and in series early is early August). considerable August) The at and correlation both of in locations the ( simulated and ob- both measurements and simulations. The simulations takingwere into made account with fire the emissions optimal estimate of 3 Results 3.1 Near-surface concentrations We focus our analysis on the airon pollution 29 events July observed and in 8 the August cityand (Portin of et shows Kuopio al., (Finland) 2012). “snapshots” Figure of 2on demonstrates the these our simulated model days domains but distributions also ofdemonstrate on CO that, the emitted preceding in days by eachtransported (28 fires in episode, July not the and the north-east only 7 direction smoke August).fires from that Our a had simulations region appeared occurred around (Konovalov over Moscow, et whereplumes, Kuopio the al., Fig. had largest 2011). 2 been As also anJuly shows illustration and of the 7 sources spatial of August.Kuopio distributions the was We of smoke mostly estimate CO in the emissions thatback-trajectory range from analyses the from (Portin fires 1 age et to on of al., 3in 28 days.).2012 smoke all This Note estimate in of that is CO the the intherefore, behaved simulation for plumes line almost scenarios definiteness, with identically passing the in results evolution over of which ofSTN the CO scenario BB from (that fires is, emissions is with were presented the taken here scenario into only using account; for the the standard version of CHIMERE). in Kuopio). Note that the(see optimization1 Eqs. ofand2) just one couldnot parameter adjust of insure the our the amplitude fire rather emission of strong model CO correlation variations between in the Moscow simulations but and could measurements, variability of ments in the agingOne experiments of the by scenariosHennigan wasvolatility et specified distributions al.(2011) by estimated taking and by intoOrtegaganicMay account material et et the in(2013). al. al.(2013). large the uncertainty For highest volatility of example, class the the ( fraction of or- scenario VBS-5 was aimed at(under assessing the the relative assumption importance that ofparticular, the there dilution we is process no took formation intoa of account formal SOA uncertainty that from range although oxidation forGrieshop of the SVOC). et OH In reaction al.(2009a) rate did not report in which BB aerosolSect. evolution 2.4.1) was as simulated well with(used as the below five both standard scenarios in aerosol involving theare scheme the listed text (see VBS in and Table scheme. 2. in Specifically, The the alongfour with scenario figures) “realistic” the and labels “standard” scenarios corresponding scenario (from (STN) parameterour VBS-1 we settings model designed to results VBS-4) to in possible order uncertainties to in examine the the VBS sensitivity scheme, of while the “unrealistic” sults of an additional control run,at in once which (and, we consequently, took all into aerosolthis account was expectation. all assumed the to emission be sources internally mixed) supported rate could significantly vary in di (2013) was estimated to range from 0.3 to 0.7 if the accommodation coe equals unity (see Tabletions S4 used in in ourMayexperiment simulations et results are al., by specifiedMay2013). in etdation The al.(2013) Table coe 3. two did Note not types that(according yield of to a althoughMay volatility unique the et distribu- value, al. dilution with of2013) value the of accommo- γ 5 5 20 10 15 25 15 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ; 10 cient cient ffi ffi cient in the ffi ciently fast, so ffi erent simulation sce- ff concentrations than the 10 concentrations observed in Kuopio in the VBS simulation (similar to an 10 10 applied in the simulations for these and the erences between the di 9131 9132 α ff F ) of the root mean square error (RMSE) and with 0.91). The decrease in the correlation coe 3 − = erences between the simulated and observed CO concentrations from these and other scenarios con- r ff erences between the representations of aerosol pro- ff 10 erences between the performance statistics calculated for ff 0.75) reflects co-variation of the observations with a contri- concentrations from simulations performed with the standard = r 10 erent aerosol schemes, simulations for the STN and all of the VBS measurements (as explained in Sect. 2.6) and are reported in Table 4. ff 10 concentration on both 29 July and 8 August, and enables achieving much better 0.88) than the STN scenario ( 10 = Similar to the VBS-2 scenario, the other scenarios with the VBS scheme involv- In spite of the considerable di Time series of PM r scenario VBS-1, as could beOA expected. concentration It on should the be OH noted reaction that rate the or dependence on of the the accommodation coe improving agreement between the mean values of the observed and simulated PM better agreement of simulationsscenario. with The measurements time in series Kuopio, ofsidered compared PM (except to for the the STN scenarioremarkable that VBS-2 the presented VBS-1 in and Fig.that VBS-4 is, 4b) scenarios the yield are sensitivity almost shown of indistinguishable in oursmall. results; simulations This Fig. to result 5. indicates changes It that in atmospheric the is that aerosol oxidation those processing reaction was rate primary su is POA very the species that Moscow had region been toin evaporated during any Finland their of were transport the almostthe scenarios from fully considered. largest Nonetheless, oxidized SVOC the and oxidation scenario absorbed rate) VBS-4 by yields (which features particles slightly larger PM ing the POA oxidation parameterization by Grieshop et(2009a) al. yield considerably in Kuopio (see Fig. 4bVBS-2 and scenario) Table 5). predicts Specifically, the anPM VBS about version of two the times modelagreement larger (for of the contribution the simulations ofthe with fire standard the version. emissions The measurements to di onthe these whole remarkable time series days of than theone VBS hand, and STN the simulations use arewith not of a quite the decrease unequivocal: on (from VBS the 7.3 scheme to 6.7 instead µgm of the standard scheme is associated overestimation of CO in the STN simulation) on 9 August. narios become evident when the simulated data are compared to the measurements is partly due to a strong overestimation of PM model is in general nonlinear, andparameters the is sensitivity small of only our in simulations the to limited changes range of of these the model parameter values. For exam- other scenarios to the emissionsrived from of PM all non-volatile and semi-volatile species were de- cesses in the di scenarios demonstrate very similarservations performance (see when Fig. compared 4aadjust to and the the emissions. Table Moscow However, 4), major ob- mainly di because these data have been used to ( on 29 July and 8surement August) data. are The shown optimal in values Fig. of 4 in comparison with corresponding mea- but, on the other hand, the VBS-2 scenario yields a slightly lower correlation coe if our fire emission datacapable were of completely reproducing wrong. the Mostabout major importantly, features a our of simulations thousand the are kilometersand observed away the CO from measurements evolution at demonstrate theon a a source 29 location good regions; July agreement in and of particular, “peak” 8 the CO August. model concentrations The di version of CHIMERE(which (that was is, found to with best the reproduce STN the high scenario) PM and with the VBS-2 scenario concentrations in Kuopio can partlyedge be of due the to smoke the plumesand fact (see where that Fig. the this simulations 2), city were whererors. was especially the Note situated sensitive concentration also at to gradients that the any were theof transport large rather the and high BGR emission correlation er- scenario obtainedbution ( for of the anthropogenic Kuopio site pollutionin in transported Kuopio; the from however, case the Russia transportwith to the of Finland transport anthropogenic to of CO thevariations. CO (coinciding CO On from in level the fires) space can whole, andand explain the time only transport results a shown processes minor in duringour part Fig. modelling the of 3 system, study the indicate although observed period not that CO perfectly. are both simulated fire emissions rather adequately by 5 5 25 20 15 25 10 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ad- α F ) were fit value for ) than in 1 α − F CO] ∆ / 10 PM ∆ CO values on all days erent from those for the ∆ ff values that are very similar and α F 10 PM ∆ 0) are quite di = OH k 9134 9133 denotes the “excess” concentration contributed values calculated using measurement data re- erences between the representations of aerosol ∆ ff fit concentrations obtained for the VBS-3 scenario are erence between the emission factors for POA and OC CO] ectively counterbalanced by SOA production. ff 10 ∆ ff / is almost two times larger in Kuopio (0.13 gg 10 erent. In particular, while NEMR calculated with the standard CO, where fit ff PM ∆ ∆ to CO] ). We regard this fact (which was not noted in earlier publications) ∆ 1 10 / − 10 PM ∆ levels) before they reached the monitoring sites in the Moscow regions. PM ∆ OA C set by stronger production of SOA from oxidation of VOCs in the VBS scheme ff To characterize the NEMR values over the whole study period, we evaluated the Figure 6 illustrates the spatial distributions of NEMR in the smoke plumes transported Comparison of the [ Note that the simulations presented in Fig. 5 were made using estimates of To quantify the changes of aerosol concentrations relative to the concentration of CO slope of a linear fit to a relationship between the processes in the standardresponding and to VBS Kuopio schemes, arethe the more standard NEMR simulation. than values In two inplumes, general, times the where the the grid larger NEMR aerosol cell in values isof are cor- the NEMR likely largest in VBS to at the be simulation central the moreslow (most than edges “aged” evaporation dense) of in of and part POA the more of species diluted. theattenuation and plumes The of also can photolysis increase by be rates slowing-down hampered byin of by BB SVOC Fig. relatively smoke oxidation 6d (note due along a to coincides a “valley” with direct of the NEMR (imaginary) location local line of minimums connecting the Moscow thickest smoke and (see Kuopio; Fig. this 2d)). “valley” Moscow (0.069 gg manifestations of the fundamental di veals that [ from Russia to FinlandSTN on and 29 VBS-2 July scenarios. andscenarios Evidently, are 8 the strikingly di August NEMR according distributionsversion to of obtained our CHIMERE for simulations tends these to foras two decrease the the (apparently smoke due is toMoscow transported mainly (see away aerosol Fig. from deposition) 2e theing and major the f), fires same the smoke that transport VBSof occurred events. version south-east oxidation Therefore, enables from our processes, simulations net whichsmoke indicate production dilution a dominate of and major over aerosol over role evaporation dur- dry of deposition almost primary everywhere. SVOCs As one due of to the spectacular where the contribution ofulations) fires 10 %. to Such COcalculated “fitted” concentration independently NEMR exceeded for the values (according Moscow (denoted to andand below Kuopio simulation our sites, data as both sim- (see with [ Fig. the 7 measurement and Tables 4 and 5). ple, results for the VBS-5 scenario (with ratio (NEMR) (similar, e.g. to asVakkari the et ratio of al., 2014). In our case, NEMR can be defined (which can be regarded asin a this chemically study) passive in tracer BB on plumes, the it time is scales convenient considered to consider the normalized excess mixing significantly smaller compared to thoseThis calculated is for an the expected VBS-1 result, and taking(2014) VBS-4 into assumes scenarios. account much that smaller the mass parameterization yieldset by of). al.(2009a Jathar the et OPOA al. species than that by Grieshop justed independently for eachnario scenario. “VBS-5” This (under adjustment which partly abe explains irreversibly major lost why fraction due the of to initial sce- yields evaporation particle almost in the the emissions same absence is results of expected as SOA to the production scenario from SVOCs) “STN”. Indeed, the optimal to that for the STN scenario.within These the estimates source indicate region that was evaporation of e POA species by fires. VBS-1 scenario in Kuopio. PM in accordance with Eq.4) thatand about 46 7th % volatility of primary classes)ambient POA already species evaporated (mostly due from the toFurther 6th dilution evaporation (i.e., (mostly due from topartly the decrease o 5th in volatility class) was relatively small and was than in the standard aerosolVBS-5 scheme scenario, (as the demonstrated other below VBS in scenarios Sect. yield 3.3). optimal Unlike the the VBS-5 scenario iscates (taking 54 % into account larger the than di that for the STN scenario, and this fact indi- 5 5 15 20 10 25 25 10 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | fit CO] erence ∆ ff / 10 erent fires) ff erences are ff PM ∆ erent monitoring stations ff resolution) used in this study ◦ 1 value obtained from the measure- × ◦ fit values obtained from simulations and 10km resolution in the Moscow region erences, which probably reflect regional – were found with the data from the “Ze- CO] × fit ff 1 20 %) in the estimate of the mass extinc- ∆ − / ∼ erences between the measurements and the CO] values for Moscow and Kuopio is not an arte- 9136 9135 10 ff ∆ fit / PM 10 ∆ measurements from 4 di CO] concentrations predicted with the both methods are ∆ PM 10 / 10 ∆ 10 for each of the monitoring sites separately. The following PM CO ratios due to varying emissions factors for di fit ∆ ∆ CO] and ∆ / 10 10 PM PM ∆ ∆ erences between measurement and simulations. Visually, the di ciency employed in this study to convert the simulated aerosol mass column ff ffi It should be kept in mind that not only our simulations are imperfect, but that the AOD Time series of daily AOD values averaged over the study region are shown in Fig. 9. In line with the results shown in Fig. 6a and c, the CHIMERE standard version (which values – 0.080, 0.056, 0.022, and 0.086 gg lenograd”, “MGU”, “Pavlovskii Posad”,tively. and All these “Kozhuhovo” values monitoring (invariability stations, spite of respec- of their big di are considerably smaller than the [ ments in the city of Kuopio. yields little SOA in BBdicting plumes) a fails much to smaller explain the relative increase increase of in NEMR the in aerosol Kuopio concentration: by [ pre- measurements (see Table 5). 3.2 Aerosol optical depth Figure 8 presents theulations spatial performed distribution with of the AODsponding on STN MODIS 8 measurement and data. August VBS-2 A 2010 scenarios veryvisible according large in both BB to comparison plume in sim- reaching with the Kuopioable the is model di clearly corre- and measurements data, although there are also consider- is calculated to bemostly only reflects 10 % the larger variabilitylation in of reproduces Kuopio the the than daily observed in NEMRthe changes Moscow. VBS values. Probably, in scheme In this with the contrast, change the NEMRin the other values VBS-2 a scenarios almost simu- (except better perfectly. for Using agreement the VBS-5 of scenario) the also results [ largest between the measurement data and the simulations made with the STN sce- between 26 July andthis 20 error August 2010 was was dueuncertainties on to in average incorrect about the identification 20 level %, of 3 and some data that aerosol a product part as (at of cloud. the Although 1 the the “operational” AOD retrievals at the 10km measurement data thatuncertainties. we In use particular, herevan Donkelaar for et comparison al.(2011) can found that also the contain relative considerable error of VBS-2 simulations are smaller,compared and to much the better results for agreementnificantly the between larger STN AOD them scenario. than Interestingly, is the the standardcorresponding evident VBS method near-surface method even PM gives in sig- the source region, although the the average) contribution ofSTN BB scenario. aerosol Accordingly, the to use of AOD,ables compared the much VBS to better method the instead overall of simulation agreementa the of with negative standard simulations the bias one with en- in themay, the in measurements, simulated although principle, data be istion due not e completely to eliminated. uncertainty Aconcentration ( part into AOD of (see this also bias Sect. 2.3). Averaging the AOD databution over of the random whole errorsseries. domain in Evidently, the the is standard simulation simulations expected strongly andwith underestimates to AOD. the measurements The minimize simulation VBS-2 to the scenario the contri- typically respective predicts time a much larger (more than a factor of 2, on nario: clearly, the standard modelcluding strongly both underestimates Moscow and AOD Kuopio. in The many di locations, in- very similar. In fact, weof SOA found to that OA the concentrationssurface VBS-2 at concentrations; simulation this higher predicts can altitudes a be (inhigher larger due the altitudes contribution to Moscow and both lower region) a temperatures than larger leading to typical to near– “age” more of condensation AO of situated SVOCs. at in Moscow and/or a resultevaluated of [ a technical failure of one of the monitors, we additionally between the “observed” [ fact of averaging of CO and PM as strong observational evidence of SOAfrom formation the in BB Moscow plumes region during their to transport Kuopio. In order to make sure that the major di 5 5 25 15 20 10 10 20 25 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | or 10 BC ratio / at the Maa- 10 on the “smoky” 10 9138 9137 erent chemical compounds contributing to OA matter, they still can ff erences between these data qualitatively reflect changes in the BB aerosol ff erent. In particular, while the STN simulation predicts that more than 90 % of BB Compared to the standard scheme, the VBS scheme yields also a larger fraction of Obviously, the results obtained with the standard and VBS schemes are profoundly ff OC. However, if we assume that the contribution of BB aerosol to PM of 14.2 observed there(assuming by thatPopovicheva the et ratio al.(2014)the of mass on fraction POM smoky of days to BCKuopio in OC was is August on was almost average 2010 about 2 aboutThis 2, times 3.5 is %). a less this Interestingly, result in the observation of BC theidized increasing indicates material. fraction the VBS that Data total in simulation of mass BC of than measurementstower aerosol in in particles atmospheric Kuopio the due were measurement to standard available absorption only station modelwhich of from unfortunately run. ox- (Leskinen the Puijo did et not al., provide2009; simultaneous accuratePortin measurements etdays of (29, al. PM July2012), and 8herrankatu August) site, at we the can Puijo estimate site (usingthe was the mass the data fraction same of from as BC Table 1 that aerosolestimate in was toPortin about does PM et 2 not %. al., Obviously, enable)2012 usingof such that us an our to “approximate” VBS-2 make or anydicates STN firm that simulations conclusion the with about BC regard the fractionby to relative in the the accuracy BB standard BC aerosol model. fraction, in but Kuopio could nonetheless be it lower in- 3.4 than that predicted Top-down estimates of BB aerosolObtaining emissions top-down estimates (that is, estimatesments) constrained by of atmospheric emissions measure- ofmodelling approach aerosols (see, (as e.g., Enting, wellHuneeus2002 ; et asZhang al., gaseous et2012; al., species)Xu2005; byDubovik et using et al., al., the2013)2008 ; inverse is aimed at validation and improving “bottom- SOA (V-SOA) formed from oxidationbution of of volatile V-SOA (traditional) still precursors,that remains the but minor black the carbon even contri- (BC) in fractionnot the is, exceed expectedly, aged 5 also %. plumes. small Our at Both results both scenarios for locations Moscow and predict are does compatible with the average OC form the composition offrom BB the fire aerosol spots on to its Moscow. way (typically having taken several hours) is likely to behardly smaller diminish than probable those systematic uncertainties inalgorithm. associated the Based with operational the on retrievals, cloud spatialassume the screening that averaging analysis those could systematic by uncertaintiestheyvan on may average occasionally Donkelaar do be et not muchof exceed larger), al.(2011 5 10 (since in %; it the however, grid standard cells seems MODISvalue). where algorithm safe AOD removes any to is retrieved approaching AOD greater a than value this 3.3 Aerosol composition Although our simulations baseding on between a di simple VBS scheme do not allow distinguish- much larger in Kuopiofrom (71 %) 49 % than in in Moscow Moscowtion to (38 in %), merely Moscow 12 with confirms % the in that POA Kuopio. oxidation fraction Note processes shrinking that were a rapid considerable enough S-SOA to frac- already trans- composition as a resultsource of and aerosol “recipient” ageing regions during considered. transport of BB plumesdi between the aerosol composition is determined byVBS-2 POA scenario species both indicates in a Moscoworiginating large and from in contribution Kuopio, oxidation of the of secondary organic SVOCs. species As (S-SOA) expected, the fraction of S-SOA species is provide some useful insightsorption/desorption and into oxidation the processes involving changesthat SVOC (that are of is, largely aerosol by disregarded the in composition processes elling). the caused framework Figure of by 10 the compares ab- conventional approach themade to speciation with OA of the mod- BB STN aerosol andfrom two according VBS-2 model to scenarios. grid cells our Specifically, we covering simulations and the consider Kuopio city near-surface data centers data of correspond Moscow tothat and 18:00 Kuopio. the UTC The di on Moscow 7 and 8 August, respectively: we expect 5 5 15 10 20 25 20 25 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) during the emission OA for the same modelling C α is expected to compensate F α F estimates are applied to the extended α F (and the corresponding top-down emission α 9139 9140 F ) values estimated for the period covered by our (see Sect. 2.4.2); under this assumption, the to- α 3 F − derived from satellite and ground-based measurements α erences between our emission estimates and the corre- F ff erent estimates of the top-down emissions and the correction ff and ambient temperature, which are unfortunately not reported in the literature OA derived from satellite measurements are presented along with the corresponding C We obtained top-down estimates of total emissions of aerosol from fires in the study It is remarkable that (1) the BB aerosol emission estimate obtained using the stan- α are reported assuming the ambient level of OA concentration ( factors are not statistically independent. The emission estimates for the VBS scenarios period, taking into accounthalf that of fire August emissions werewith in relatively very the available small, monthly first in half datawith order of of the to July bottom-up compare corresponding and inventories. our estimates thescenarios Our emission as second of emission estimates those the estimates, discussed correction above along nario (except factor “VBS-5”), for are the estimates presented forparison in the Fig. with “unrealistic” 11. the sce- The dataand emissions from GFASv1.0, estimates the for are bottom-up emissions shown fire ofF in emission total com- inventories, particulate such matter as (TPM), GFED3.1 while the estimatesmates of for the scenarioto VBS-5 be are much omitted largerare from (as clearly these could unrealistic figures, (in be becauseto particular, expected) they the the than total turn VBS-5 those aerosol out scenario, for emissionsestimate were all compared for 1.8 the the to Tg VBS-5 other according 1.3and Tg scenario scenarios the for (relative and other the to VBS STN theand scenarios) dilution estimates scenario). of is for The POA indicative the in muchuncertainties of both the larger of study the STN region the major scenario during di roles the period of of both intense SOA fires. formation Note that the factor measurements to be 10 mgm estimates obtained from ground-based measurements (see also Table 4). The esti- estimates) derived from the(within AOD the measurements range for uncertainty) the with VBS-2surface the monitoring scenario corresponding data, is estimate while derived consistent this fromby is local using obviously near- not the the standard casedependent approach. with data The the estimate inconsistency means obtained of thatmajor the they deficiencies estimates fail of based to simulations on based passthe on the fact the the that in- the cross-validation, standard estimates approach. and of On is the indicative other of hand, sponding data of theFokeeva GFED3.1 et al., inventory, it2011; Konovalov can ettory be al. , strongly2011; notedKrol underestimated that et the there al., CO2013) is emissions that evidence from the (e.g., the GFED3.1 inven- 2010 Russian fires; it seems at least with theof “best” the VBS reliability scenario of areRegarding our consistent top-down provides the emission strong remaining estimates evidence di in obtained favor with the VBS approach. simulations (from 15 July to 20 August). The region during the period fromsurements 1 and July the to correction 31 factor August ( 2010 by using the MODIS AOD mea- up” emission inventories andcesses. advancing As noted our in general the introduction,have knowledge conventionally the of simulated models BB employed the aerosol in emission under inversevolatile modelling the material. pro- studies assumption Here that we it examined, in consistsBB particular, of emissions whether non- or could not change top-down significantly estimateswith if of the this VBS assumption approach was to relaxed OA in modelling. accordance tal POA emissions are about 20 % larger. Optimization of for the scenarios “VBS-1” andfor “VBS-4” the are also STN considerably scenario, smallerthe (3) than estimate the all estimate for estimates the basedGFASv1.0 unrealistic on and VBS-5 using GFED3.1 scenario) the data show than VBStant better the approach result agreement estimate is (except with that for for the the both optimal STN the estimate scenario. of Another impor- together with the emission factor estimates. dard model (1.3 TgTPM) ison about using 60 the % VBS larger approach than the with corresponding the estimate VBS-2 based scenario (0.8 TgTPM), (2) the estimates possible uncertainties in thedata POA for the emission OA factors. emission factors Noteon can again depend (as that argued, the e.g., by experimental Robinson et al., 2007 ) 5 5 15 25 10 20 20 15 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and CO concentrations) by using 10 9142 9141 cient laboratory or ambient measurement data ffi air pollution monitoring data in the Moscow region. erent organic species (Aumont et al., 2005). A general problem arising with more ff 10 It should be emphasized that our numerical experiments with the VBS scheme were The results of simulations made with the VBS scheme are compared with corre- dimensional volatility-oxidation space (2-D-VBS) enablesganic constraining properties the average more or- used tightly in than this the study.tion An scheme more even could conventional much involve explicit more one-dimensionalof characterization di complex of scheme chemical (and and potentially physicalcomplex realistic) properties schemes OA is evolu- the lack of su neither intended nor allowedprocesses us considered. to Indeed, estimate ourresentation the VBS of real scheme values the provides ofof complex only the organic processes a parameters material. very involving of For simplistic absorption/desorption the example, rep- andDonahue oxidation et al.(2012) argue that assuming a two- mass during transport fromproach Russia proved to to Finland), be the in simulationsobtained a when good based evaluating agreement on our with the simulations the VBS againstticular, measurements. satellite the ap- Similar AOD use results measurements. of were In the par- a VBS factor approach of enabled two reducing relative RMSE the of simulations simulations based by on almost the “conventional” approach. needed to constrain allbility the that parameters. a On simplistic the schemefor other may example, demonstrate hand, when good optimization there performance of is for itserrors. always a parameters In compensates the wrong some our reason; possi- systematic case, model systematicregarding model the errors fragmentation may process bevolatility) associated, (splitting and in of a particular, simplified C-C with representation bonds, dis- ofto the which decrease functionalization tends volatility); processes to (which a tend in increase potentially detail, important e.g., role by ofOAMurphy particles these (i.e., et processes the was(2012). al. nucleationinitial discussed Our process), hours which model of may also be theNonetheless, disregards important atmospheric our formation at results processing least of provide of during strongstudy new the BB to evidence a that smoke special the (e.g., case VBS oftoVakkari modeling method the et aerosol “conventional” applied originating method. al., in from2014). wildfires this is indeed superior measurements at an airated pollution about monitoring 1000 km sitefound north-west in to strongly from the underestimate Moscow). city theabout While observed of NEMR two the Kuopio, in times standard Finland Kuopio (which larger simulations (situ- turned there were out than to be in Moscow, thus indicating the gain of BB aerosol from ground-based and satellite measurements. Intions particular, with we respect evaluated to our the simula- normalizedas excess the mixing ratio ratio of (NEMR) enhancements of caused BB by aerosol fires (defined in PM sponding results obtained with the standard OA scheme in CHIMERE and with data thus reasonable to expectinventory. that the TPM emissions were also underestimated by this 4 Summary and concluding remarks In this study, we usedmodelling the to volatility simulate basis thefor set mesoscale the (VBS) evolution case of approach of aerosol to theified from organic mega-fire the open aerosol event VBS biomass that (OA) scheme occurred burning laboratory in in the experiments Russia CHIMERE in aimed chemistry summer atcesses 2010. transport studying in We model gas-particle the mod- by mixtures partitioning using oflike data and gases the from oxidation and VBS aerosols approach, pro- the emittedversion “conventional” from OA of biomass modeling CHIMERE burning approach disregards (BB). used the Un- of in volatility the secondary of standard primary organic OA aerosollations species by and scenarios oxidation the were formation of consideredpossible semi-volatile uncertainties to precursors. in test Several the the simu- tive parameters sensitivity role of of the of VBS dilution theEmissions scheme and model and of oxidation output to gases processes data evaluate and in the(FRP) to particles the rela- data evolution from from of fires satellite aerosol werePM (MODIS) in modelled BB measurements, using plumes. and fire were radiative constrained power by CO and 5 5 15 10 20 25 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff erences in chemistry transport and ff 9144 9143 orts are also needed for achieving better under- ff erences between the ageing of BB aerosol from fires in di ff This study was supported by the Russian Foundation for Basic Research Future studies of BB aerosol evolution, combining modeling with laboratory and field Finally, we found that the replacement of the standard aerosol model in CHIMERE Important implications of using the VBS instead of the “conventional” approach for Meagher, J., Hsie, E.-Y., Edgerton,for E., summertime Shaw, S., secondary and organic Trainer, aerosols M.:phys. over A Res., the 117, volatility eastern basis D06301, United set doi:10.1029/2011JD016831, States model 2012. in9112 2006, J. 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L.:Vicente, On A., M., the Alves, contribution C., Krzyzanowski, of Calvo, A. natural M., I., aeolian Fernandes, A. P., Nunes, T., Monteiro, C., Almeida, S.Wang, M., J., Christopher, S. A., Nair, U. S., Reid, J. S., Prins, E. M., Szykman, J., and Hand, J. L.: Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X. Y., O’Neill,Witte, S., J. and C., Douglass, A. R., da Silva, A., Torres, O., Levy, R., and Duncan, B. N.: NASAWooster, M. A-Train J., Roberts, G., Perry, G. L. W., and Kaufman, Y. J.: Retrieval of biomass combus- 5 5 10 20 15 25 15 10 20 25 30 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) specified in the emission model (see 1 − , gkg β 9158 9157 POA oxidation scheme Volatility distribution type IAIBIIIA A erent modelling scenarios with emissions from fires. The ff 1 − s 3 11 11 11 11 2.44 2.49 3.10 agricultural burning grassland forest − − − − , cm 10 10 10 10 × × × × OH x k OCBCCO 4.2NMHC 0.42NO 9.9 95 3.1 0.55 5.5 65 7.7 0.58 8.7 115 erent types of vegetative land cover. The data are based on Andreae and Merlet ff Biomass burning emission factors ( Simulation settings for the di Modeling scenario STNVBS-1VBS-2VBS-3VBS-4VBS-5 N/A 2 2 2 4 N/A N/A N/A N/A A (2001) and subsequent updates. POA oxidation schemes Iet al.(2009a) and and IIJathar et areity al.(2014), distributions respectively based (see are on Sect. specified 2.4.2). in thescenarios The Table 3. corresponding parameterizations listed Note volatil- described that in along in ground” the with conditionsGrieshop the table, in simulations an the based absence on additional the of “fire” model fires (see run Sect. (“BGR”) 2.7). was made to simulate “back- Table 2. Eq.1) for di Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | = 2 2 1 − vap 10 10 10 H × × × 1.0, 1.00 8.40 6.73 = concentration 2 2 1 − γ concentrations 10 10 10 10 10 × × × 1.05 7.87 6.71 2 2 1 − 10 10 10 × × × data; the geometric SD char- are the optimal estimates for α 10 is the mean PM F 1.00 8.33 6.78 10 2 2 1 − PM 10 10 10 cient and the enthalpies ( × × × ffi erent simulation scenarios (see Table 1). type ff 1.06 7.89 6.67 2 2 1 − 10 10 10 9160 9159 × × × Volatility distribution AB0.20.0 0.1 0.2 0.0 0.10.3 0.2 0.15 0.45 1.04 8.01 6.71 is the normalized excess mixing ratio evaluated as the fit 2 2 1 − 2 1 ) used in the di 10 10 10 i − − 2 3 4 CO] f ∗ i × × × ∆ C 10 10 11010 10 0.110 0.1 0.05 0.05 / are given in parentheses. 10 1.02 STN VBS-1 VBS-2 VBS-3 VBS-4 VBS-5 6.68 α F PM 2 ∆ 2 − 10 10 × × N/A 1.03(1.09) 1.03(1.06) 1.05(1.09) 1.12(1.13) 0.95(1.08) 1.54(1.11) N/A 0.86 0.86 0.86 0.85 0.86 0.85 6.94 , ] N/A 8.15 fit ). 3 ] 1.23 ∗ i − 3 ected by fire emissions (see also Fig. 7). − ff C Volatility distributions ( CO] Characteristics of the simulation data (after bias correction) compared to air pollution ∆ / [µgm 10 ] 1 4log − 10 PM − α ∆ F PM Characteristic Observations Simulation scenario RMSE [µgm r [ [gg on days a slope of a linear fit to the relationship between perturbations of CO and PM 85 acterizing uncertainties in the fire emission correction factor (see Eq.1) derived from PM measurements at monitoring stations in the Moscow region. over the study period. [ Table 4. Table 3. The distributions arethe based recommended on values of the the accommodation data coe by May et al.(2013) and were used together with Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 1 − 10 10 × × 1.55 6.64 )) used in the 1 t 1 − ( m 10 10 h × × 1.74 1.16 1 1 − 10 10 × × 1.59 8.43 1 1 − 10 10 × × 1.81 1.34 1 )) and daily FRP maximums ( 1 − t ( e 10 10 9162 9161 h × × 1.74 1.13 2 1 − 10 10 × × STN1.58 VBS-1 VBS-2 VBS-3 VBS-4 VBS-5 7.32 1 1 − 10 10 × × N/A 0.91 0.89 0.88 0.88 0.89 0.91 1.30 , ] N/A 7.25 6.26 6.74 7.44 6.35 7.65 fit 3 ] 1.75 − 3 − Diurnal profiles of fire emissions ( Characteristics of simulation data (after bias correction) compared to air pollution CO] ∆ / [µgm 10 ] 1 − 10 PM ∆ Characteristic ObservationsPM Simulation scenario r [ RMSE [µgm [gg emission model (see1 Eqs. and2). Figure 1. measurements at the Maaherrankatu site in Kuopio. Table 5. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | in (b)

. The CO 47

(b) 46

7 August 2010. (f) and in Kuopio (a) erence between the simulations 28 July and ff (e) ) of fire-emitted CO at 18:00 UTC on 3 − in Kuopio on 29 July and 8 August and d f b b (a)

9163 9164 7 and 8 August 2010, respectively, along with spatial ected by fires); note that a negative bias (specifically, in ff ) emitted from fires on 2 − (c, d) Time series of daily CO concentrations in Moscow Simulated near-surface concentration (mgm

a a

c e 28 and 29 July and on Figure 3. concentration for the simulationby scenario taking “STN” into (see account both thethat anthropogenic red for and lines the fire with emissions “BGR”(along crosses) (as run with explained are (see in obtained other Sect. the sourceslines 2.7), solid while contributing depict brown to the lines) the modeland reflects boundary bias measurements only conditions (representing on anthropogenic for thethe CO days CO). plot systematic emissions not “a”) The di a is dashedstations shown blue and with the the Maaherrankatu opposite sitelines sign. indicate in The the Kuopio) measurement CO are data concentrations shown observed (from by Mosecomonitoring green lines. The vertical dashed Moscow on 28 July and 7 August. distributions of CO amounts (gm Figure 2. (a, b) Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 48

erent simulation scenarios ff b concentrations, except that in addition to results 9166 9165 10 concentrations according to di 10 The same as in Fig. 3 but for PM Time series of daily PM

a Figure 5. in comparison with measurements inwhich Kuopio. is Note shown that in the Fig. time 4b, series is for omitted the in VBS-2 this scenario, figure. for the STN and BGR runs, this figure shows (by a purple line) results for the VBS-2 run. Figure 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | CO) ∆

and 50 51

10 and 8 August

PM ∆ (a, b) for 29 July 3 − scenarios. and CO concentrations ( (b, d) 10 b d b 9168 9167 ected by smoke from fires. Note that the relative scales and VBS-2 ff ) originating from the fires. The NEMR values are shown 1 − (a, c) and CO (gg Kuopio on days a 10 CO values are the same on both plots. The slope of a linear fit to the data (b) ∆ and 10 Normalized excess mixing ratio (NEMR) calculated as the ratio of near-surface mass Scatter plots of the enhancements of PM

a

c

a PM ∆ 2010 according to the STN Moscow and (a) provides an estimate offits NEMR at (see the Sect. 68 % 3.1). confidence The level. shaded areas depict uncertainties of the of the Figure 7. in concentrations of PM only in the grid cells with CO concentration exceeding 100 µgm Figure 6. (c, d) Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . (c) 52

erent scenar-

18/08 ff 13/08 =0.94 r

=0.95

=0.85 r r

08/08 03/08

b 29/07 9170 9169 in comparison with the MODIS measurement data date of the year 2010

(b)

24/07 and “STN” 19/07 model:VBS-2 mean=0.45 RMSE=0.11 model:STN mean=0.35 RMSE=0.20 model:BGR mean=0.25 RMSE=0.34 bias mean=0.08 measurements mean=0.49

(a) 14/07 0 2

0.4 1.6 1.2 0.8 Aerosol optical depth optical Aerosol Time series of AOD at 550 nm obtained from simulations made with di Spatial distributions of AOD at 550 nm on 8 August 2010 according to simulations for a

c c ios and derived fromstudy the region MODIS (see measurements. Fig. The 8). daily data are averaged over the whole Figure 9. the scenarios “VBS-2” Figure 8. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | The (b)

55 54

erent simulation scenarios ff in Kuopio (at 18:00 UTC on 8 Au- (b)

b b

9171 9172 derived from MODIS (boxes with solid filling) measure- α F Top-down estimates (in Tg) of total BB aerosol emissions from the study re- Composition of BB aerosol according to the simulations scenarios “STN” and “VBS-

a

a

in Moscow (at 18:00 UTC on 7 August 2010) and (a) Figure 11. (a) gion in the period from 1 July to 31 August 2010 according to di and in comparison withGFED3.1 total inventories. The particulate estimates are matter derivedcorresponding (TPM) from optimal emission the estimates MODIS data of AOD from measurements. the GFASv1.0 and gust 2010). Figure 10. 2” ments in comparison withground-based corresponding measurements in estimates the (boxestic” Moscow with scenario region. dashed “VBS-5”, Note filling) which that would obtained the exceed from estimates the for axis the limits (see “unrealis- Sect. 3.4), are not shown.