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Atmospheric Environment 44 (2010) 3085e3094

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Atmospheric Environment

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Study of a winter PM episode in Istanbul using the high resolutionWRF/CMAQ modeling system

Ulas Im a,*, Kostandinos Markakis b, Alper Unal c, Tayfun Kindap c,Anastasia Poupkou b, Selahattin Incecik d, Orhan Yenigun a,Dimitros Melas b, Christina Theodosi e, Nikos Mihalopoulos e

aBogazici University, Institute of Environmental Sciences, 34342 _Istanbul, TurkeybAristotle University of Thessaloniki, Department of Physics, Laboratory of Atmospheric Physics, 54124 Thessaloniki, Greecec Istanbul Technical University, Eurasia Institute of Earth Sciences, _Istanbul, Turkeyd Istanbul Technical University, Department of Meteorology, _Istanbul, TurkeyeUniversity of Crete, Department of Chemistry, Environmental Chemical Processes Laboratory, P.O. Box 2208, 71003 Heraklion, Greece

a r t i c l e i n f o

Article history:Received 18 November 2009Received in revised form17 May 2010Accepted 20 May 2010

Keywords:Air quality modelingAerosolChemical compositionIstanbulWRFCMAQ

* Corresponding author at: Pressent address: UniveChemistry, Environmental Chemical Processes LaborHeraklion, Greece.

E-mail address: ulasim@chemistry.uoc.gr (U. Im).

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2010.05.036

a b s t r a c t

High winter-time PM10, sulfate, nitrate and ammonium levels in Istanbul were investigated using a highresolution WRF/CMAQ mesoscale model system. A model-ready anthropogenic emission inventory on2 km spatial resolution was developed for the area and the present study is the first attempt to test theseemissions. The results suggested that the system was capable of producing the magnitudes. PM10 levelscalculated by the model underestimated the observations with an average of 10 per cent at BogaziciUniversity sampling station, whereas an overestimation of 12 per cent is calculated for all stations. Highuncertainties, particularly in traffic and coal combustion, led to over estimations around emission hotspots. Base case results together with the sensitivity studies pointed significant contribution of localsources, pointing to the need of control strategies focusing on primary particulate emissions.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Urban scale air pollution has become a significant environmentalproblem in the world with the increase of urbanization. The globalurbanpopulation has risen fromabout 3 per cent of theworld’s totalpopulation (Brunn and Willimas, 1983) to about 49 per cent in theyear 2005 (UN, 2007). Air quality in megacities is of great concerndue to migration towards urban areas and the health issues (Gurjaret al., 2008). Particulate air pollutants involve a complex mixture ofsmall and large particles of varying origin and composition,including fossil fuel emissions, industrial fugitive dust, windblowndust and secondary pollutants.

Istanbul is a megacity with population well over 12 million. Airpollution is one of the challenging environmental problems inIstanbul. Especially at the end of 1980’s and the beginning of 1990’s,SO2 and PM concentrations have exceeded the short-term air quality

rsity of Crete, Department ofatory, P.O. Box 2208, 71003

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standards in many days due to the consumption of the low qualitylocal lignite during the heating seasons, particularly when theanticyclonic pressure systems prevail (_Incecik, 1996; Tayanc, 2000;Topcu et al., 2003). Karaca et al. (2005) found that the annualarithmetic mean of PM10 was 47.1 mgm�3, higher than the EuropeanUnion (EU) air quality annual PM10 standard of 40 mgm�3. Accordingto the air quality network of the Ministry of Environment andForestry, the daily mean PM10 concentrations exceeded the limit of50 mgm�3 at all municipality stations with an average of 157 days,which is well above the EU limit of 35 days. Although there havebeen improvements in primary pollutants levels of SO2 and TSP byshifting to natural gas as the primary residential and industrial fuel,secondary pollutants such as ozone and secondary organic aerosols(SOA) became more significant due to the increase in natural gascombustion (Im et al., 2006, 2008).

These high levels of both primary (TSP and SO2) and secondary(ozone) air pollutants were investigated through statistical analysesof measurements in a number of studies (Tayanc, 2000; Topcu andIncecik, 2002; Im et al., 2006, 2008). However, sophisticated airquality modeling systems have not been employed sufficiently toinvestigate these levels. The first modeling study focusing on

Table 1Geographical information of the monitoring stations.

Station Location Latitude Longitude Elevation(m)

Type

Alibeykoy Istanbul-Europe 41.40 28.56 60 Urban CenterBesiktas Istanbul-Europe 41.30 29.00 94 Urban

Center/TrafficESC Istanbul-Europe 41.09 29.05 80 Urban BackgroundEsenler Istanbul-Europe 41.20 28.53 59 Urban CenterKartal Istanbul-Asia 40.53 29.12 25 IndustrialSarachane Istanbul-Europe 41.00 28.57 16 Urban

Center/TrafficSariyer Istanbul-Europe 41.70 29.20 103 Urban BackgroundUmraniye Istanbul-Asia 41.00 29.90 163 SuburbanUskudar Istanbul-Asia 41.00 29.10 70 Urban CenterYenibosna Istanbul-Europe 40.59 28.49 23 Industrial

U. Im et al. / Atmospheric Environment 44 (2010) 3085e30943086

Istanbul was conducted by Anteplioglu (2000), where the SAIMMand UAM models were used to simulate ozone. Topcu et al. (2003)studied the ozone levels using MM5/CAMx modeling system,including only the traffic emissions. Kindap et al. (2006) and Kindap(2008) used MM5/CMAQ modeling system to study the PM10 levelsin Istanbul and the contribution of long-range transport fromEuropeto these levels on coarse resolution and using previous aerosolmodule (AERO4). Most recently, Elbir et al. (2010) used the CALPUFFmodeling system to investigate the major pollutants in Istanbul.

The motivation of this study was to examine the PM10 levelsand the effects of the anthropogenic emissions on these levels inIstanbul, using a first-time comprehensive and state-of-the-arthigh resolution and model-ready anthropogenic emission inven-tory. Themodel system provides advances in terms of the capabilityto simulate the dynamic coarse-fine mode interaction by usingthe final version of the CMAQ model (version 4.7) on a higherspatial resolution e which were the major deficiencies in theprevious studies -, together with a better representation of mete-orology through the advanced parameterization schemes of theWRFmodel. Besides the PM10mass; sulfate, nitrate and ammoniumaerosols have also been investigated for the first time. Additionally,the hypothesis of dominant contribution of local primary particu-late emissions has been investigated through sensitivity runs. Theuncertainties originated from the emissions and the possible effectsof these uncertainties on the model concentrations are discussed.

The model evaluation has been conducted for the periodbetween 13th and 17th of January, 2008, on 24-h basis for PM10

concentrations at nine municipality air quality stations andBogazici University (ESC) sampling station. A spin up period of 3days was used to minimize the effects from initial and boundaryconditions. Additional analyses were performed on sulfate, nitrateand ammonium concentrations, provided from an extensivemeasurement campaign at ESC sampling station. Finally, BruteForce sensitivity analyses have been conducted in order to under-stand the sensitivity of aerosol levels at the ESC station to differentemission scenarios of �10 per cent changes in emissions of SO2,NOx, NH3, and VOC, individually and all together.

2. Materials and methods

2.1. Simulation period and aerosol data

To evaluate the chemical composition of aerosols and identifytheir sources in Istanbul, PM10 samples have been collected at ESC(41.09 N and 29.05 E) for a period betweenNovember 2007 and June2009. The aerosol samples were analyzed for the main ions, tracemetals and organic and elemental carbon at Environmental Chem-ical Processes Laboratory, University of Crete. The details of thisstudy are given in Theodosi et al. (2010). Several episodes wereidentified over the year of 2008. A 5-day winter episode of highPM10 levels, between 13th and 17th of January, 2008, was investi-gated in this present study. This periodwas chosen because the 24-hPM10 levels at the ESC station all 5 days were above the EU limitvalue of 50 mgm�3, with amaximum of 129 mgm�3. This was also thecase for the nine urban air quality stations operated by the GreaterMunicipality of _Istanbul. PM10 measurements from 6 stations onEuropean side (Alibeykoy, Besiktas, Esenler, Sarachane, Sariyer andYenibosna), and 3 stations on Asian side (Kartal, Umraniye, Uskudar)were provided from the Istanbul Metropolitan Municipality(Table 1). Additionally, source apportionment results from Kocaket al. (submitted for publication) showed that anthropogenic sour-ces accounted for about 90 per cent of the observed PM10, makingthe episode suitable for testing the new anthropogenic emissioninventory. During the episode, average sulfate, nitrate and ammo-nium concentrations were measured to be 9.8, 6.8 and 4.1 mgm�3.

The cation/anion balance, using the complete set of ions, showedeither complete neutralization ormissing cations (carbonates due tothe dust). The results showed that ammonium was 75 per centresponsible for neutralization, whereas calcium or sodium by only25 per cent.

The episode was characterized with the southerly and easterlywinds; opposite to the long term observations and previous studiesconducted for the area by Kindap et al. (2006), Kindap (2008),which showed northerly transport. There was no significantprecipitation observed during the episode, which led to increase inPM10 concentrations in absence of wet deposition.

2.2. Meteorological modeling

The non-hydrostatic, mesoscale Advanced Research WeatherResearch and Forecasting (WRF-ARW) model, version 3.1 is used asthemeteorologicalmodel (Skamarock and Klemp, 2008; Borge et al.,2008), which is developed based on the Fifth-Generation PennState/NCAR Mesoscale Model (MM5) (Grell et al., 1994). Threedomains have been set up for the meteorological modeling systemon Lambert Conformal projection: a mother domain coveringEurope of 199 by 175 grid cells on 30 km spatial resolution, thesecond domain covering the Balkan region of 181 by 202 grid cellson 10 km resolution, and finally, the innermost domain covering theGreater Istanbul Area (GIA) of 136 by 111 grid cells on 2 km reso-lution (Fig. 1). As seen from the figure, the important dust reservoirsin northern Africa were not included in the study. However, theeffect of dust to is expected to be low in January. The NationalCenters for Environmental Prediction (NCEP) Final Analyses (FNL)data of 1 � 1� have been used to provide the initial and boundaryconditions required by the WRF model while 37 sigma layers havebeen used for the vertical resolution of the model. The verticalresolution was stretched from approximately 20 m close to thesurface and gradually decreased to 1400 m close to the model top(10 hPa). The physical options used in this study were WRF SingleMoment 6-class microphysics scheme (Hong and Lim, 2006),RRTM (rapid radiative transfer model) longwave radiation scheme(Mlawer et al., 1997), Dudhia shortwave radiation scheme (Dudhia,1989), NOAH land surface model (Chen and Dudhia, 2001), YonseiUniversity Planetary Boundary Layer scheme (Hong et al., 2006) andKain-Fritsch cumulus parameterization scheme (Kain, 2004). Addi-tionally, analysis nudging was performed on temperature, wind andmoisture parameters for the mother domain.

2.3. Chemistry and transport modeling

The US EPA Community Multiscale Air Quality (CMAQ) model,version 4.7, was used in this study as the chemistry and transportmodel (Byun and Ching, 1999). CMAQ is a three-dimensional

Fig. 1. Meteorological modeling domains used in the WRF model.

U. Im et al. / Atmospheric Environment 44 (2010) 3085e3094 3087

Eulerian atmospheric chemistry and transport modeling systemthat simulates ozone, acid deposition, visibility, and fine particulatematter throughout the troposphere. CMAQ has beenwidely used tosimulate the atmospheric composition (Hogrefe et al., 2001;Jimenez and Baldasano, 2004; Zhang et al., 2004; Unal et al.,2005; Gego et al., 2006; Odman et al., 2007).

CMAQ model treats PM size distribution in three modes; Aitkenand accumulation modes for particles with diameter under 2.5 mm,and a dynamic coarse mode. CMAQ version 4.7 employs a newaerosol treatment module, AERO5, which introduces several path-ways that the previous CMAQ aerosol modules have not included,particularly for secondary organic aerosols (SOA) (Edneyet al., 2007).Previous aerosol modules in CMAQ treated all SOA as semi-volatile,whereas AERO5 introduces formation of non-volatile SOA throughcloud oxidation, isoprene-derived SOA under acidic conditions, SOAoriginated from aromatic oxidation and oligomerization of particle-phase semi-volatile organic material. Additionally, AERO5 moduleenables separate tracking of gas and particle-phase of semi-volatileoxidation products instead of lumping into singe species. Besidesthe new SOA treatment, CMAQ v4.7 covers the dynamic aerosolformation through various other processes including primaryemissions, nucleation, growth, coagulation and evaporation/condensation.

The boundary and initial conditions for themother domainwereprovided from the default CMAQ files. For the inner domains, the BCand IC are interpolated from the outer domain of the previous day’ssimulation using ICON and BCON modules of the Models-3 system.The chemical mechanism used for the gaseous species was CB-V(Yardwood et al., 2005), whereas AERO5 was employed as theaerosol mechanism,which also calculates sea-salt emissions. CMAQis run on three domains, covering Europe in a 163�150 grid systemof 30 km resolution, Balkan region in a 140 � 155 grids domain of10 km resolution and GIA in a 92 � 57 grids domain of 2 kmresolution; and 20 vertical layers. The first layer extended up to

93 m whereas the top of the domain extended up to 16 km.Yamartino scheme for advection (Yamartino, 1993) and ACM2scheme (Pleim, 2007) for vertical diffusion has been used in thestudy. The aqueous cloud chemistry has also been employed.

2.4. Emission inventory

A GIS-based, state-of-the-art and model-ready emissioninventory for the GIA has been developed for the first time in theframework of this study. The emissions have been spatially allo-cated over the GIA using a resolution of 2 km. The inventoryincludes emissions of CO, NOx, SO2, NH3, 6 PM species (organic andelemental carbon, sulfates, nitrates, ammonium and other parti-cles) and 23 NMVOCs species (Olivier et al., 2001) in monthly, dailyand diurnal resolutions (Friedrich,1997). Awide variety of emissionsources have been covered including energy production, residentialand industrial combustion, road traffic and shipping. A bottom-upmethodologywas adopted for themost contributing anthropogenicemission sources such as residential and industrial coal combus-tion, international cargo shipping, utilizing official activity infor-mation and high resolution digital maps of population density, roadnetwork and land uses. Road transport emissions were calculatedbased on compiled emissions factors specifically for the area whichreflect both the status of the circulating fleet as well as the drivingconditions within the city. More details are given in Markakis et al.(2009) and references therein. To cover the remaining sources,gridded annual emission data for the European and the Balkandomainwere obtained from Visschedijk et al. (2007). The inventorywas originally prepared for the Global and Regional Earth-SystemMonitoring using Satellite and in-situ data project (GEMS project)(Hollingsworth et al., 2008) for the reference year of 2003 witha grid spacing of 1/8 by 1/16�.

The new high resolution emission inventory developed for ourstudy resulted in 2e7 times higher emissions compared with EMEP

U. Im et al. / Atmospheric Environment 44 (2010) 3085e30943088

(Vestreng et al., 2006) and TNO (Visschedijk et al., 2007) emissions(Table 2). These large differences can be attributed to the differ-ences in methodology and input data. While the emissions used inthis studywere estimated through a bottom-upmethodology usingactual activity data for most of the major source categories,the previously compiled EMEP or TNO inventories mostly takeinto account information from experts, national and internationalorganizations and production statistics, which may be highlyuncertain. The use of these emissions may lead to significantunderestimations in modeling studies, depending on the focusedarea. Kindap et al. (2006) have shown poor agreement of PM10magnitudes basically because the EMEP emissions for the area ofinterest were practically based on assumptions of the EMEP expertpanel, due to lack of officially reported emissions of Turkey to EMEP,and were highly uncertain.

3. Results and discussions

3.1. Meteorological simulations

Themodel calculations ofwind speed at 10m and temperature at2 m were compared with Kandilli meteorological station (41.06 Nand 29.06 E) in the GIA domain. The wind speeds throughout thesimulationwere below 4ms�1 and temperaturesweremostly below10 �C degrees. The model did not simulate any convective precipi-tation, whereas it simulated very low amounts of non-convectiveprecipitation (below 10 mm). The PBL heights throughout theepisode barely exceeded 500 m, mostly below 200 m, leading topoor mixing of pollutants and accumulation close to surface. Thecomparison of model calculated and observed temperature andwind speed, as well as simulated PBL height, precipitation and cloudfractions are presented in Fig. 2. The statistical results presented inTable 3 show that themodel did fairly well in producing the fields inthe 2-km GIA domain. The correlation factors show that the modelcaptured the temporal variability, particularly for temperature(corr¼ 0.86) at the Kandilli station. Themean and standard variationratios were calculated to be 1 for both variables. High index ofagreement (IOA) values of 0.92 and 0.77 and low biases of 0.36 �Kand �0.03 ms�1 for temperature and wind speeds, respectively,show that the model results reproduced realistic meteorologicalfields.

3.2. Chemistry and transport simulations

3.2.1. Comparison of model results with observationsThe highest PM10 concentrations were calculated at the regions

thatwell agreewith the spatial distribution of emissions. Fig. 3 showsthat the highest concentrations were calculated for the areas on thecoastline surrounding the Bosporus Strait, where most of the pop-ulation is located, and at Ikitelli area, which is one of the largest

Table 2Comparison of the total annual emissions (in tnyr�1) with EMEP and TNO databases(Values outside brackets include land emissions. Values in brackets include bothinternational shipping and land emissions).

Pollutant EMEP TNO This study

CO 126,728 (128,165) 221,875 344,729 (346,102)NOx 19,943 (23,365) 69,522 110,552 (140,814)SOx 10,216 (10,491) 104,231 73,400 (131,954)NMVOC 16,536 (16,857) 95,685 108,164 (108,660)NH3 23 (23) 7266 6505 (6514)PM10 37,974 (39,837) 33,022 88,650 (90,159)PM2.5 29,142 (30,906) 23,520 64,442 (66,283)

* Values inside parenthesis are emissions including shipping.

industrial combustion sources in the Istanbul domain. The temporalvariations of calculated daily mean of PM10 concentrations as well asthe components well-agreed with the observations for the ESCstation, as seen in Fig. 4 (correlation factors of 0.83, 0.96, 0.95and 0.88 for PM10, sulfate, nitrate and ammonium, respectively).Previous studies resulted in large differences between modeled andobserved PM10 concentrations in terms of magnitude. Kindap et al.(2006) simulated order of magnitude lower PM10 concentrationsusing the 50 km resolution EMEP emissions. However, in our study,PM10 concentrationswere underestimated on average by 10 per cent,whereas sulfate by 23per cent, nitrate by 75per cent and ammoniumby 33 per cent (Table 4). The difficulty ofmodels to simulate PM10 hasbeen recognized and discussed by a number of studies (Beekmanet al., 2007; Vautard et al., 2009). The differences may originatefrom the lack of emissions from wind erosion processes and trafficinduced resuspended dust (Vautard et al., 2005), inaccuracy ofsecondary organics formation (Simpson et al., 2007) and in general,the lack of process knowledge (Stern et al., 2008).

Fig. 4 shows an artificial increase of the second peak (15th ofJanuary), particularly for PM10 and sulfate aerosol concentrations.WRF model estimated no precipitation for this day, whereasobservations showed a total precipitation of 8.6 mm. Although it isnot a significant amount of precipitation, this led to an underesti-mation of wet deposition of the particles, increasing theirconcentrations. The overestimation of sulfate was most probablydue to the chemical mechanism of sulfate in-cloud production,where SO2 reacts with H2O2 radical in aqueous phase to producesulfate. The WRF model calculated low PBL heights (Fig. 2c), veryhigh cloud fractions, almost equal to 1 (Fig. 2d), and no precipita-tion throughout the sampling period for January 15th (Fig. 2e),which led to excessive amounts of sulfate in-cloud production. Thisis confirmed by the Integrated Process Rate Analysis (IPR) tool ofCMAQ model. The analysis results showed that significant amountof sulfate aerosols was produced through emissions and cloudprocesses, whereas gas-phase production was very limited, whichled to very small amount of SOA production (Fig. 5).

The episode-average underestimation of sulfate aerosols (23 percent)might be related to underestimation of coarsemode sulfate thatis not associatedwith ammoniumbutmay be associatedwith Ca, Mgor Na from soil dust or resuspended dust, which are lacking in ouremission inputs. On the otherhand, the lack of sulfuric acid emissions(SULF) has probably led to underestimations of sulfate aerosols.Including SULF emissions together with coarse particle emissionswould also improve the nitrate aerosol estimations by simulating theinteraction between fine and coarse modes. The model calculated 75per cent lower nitrate concentrations than the observations, wherealmost 94per cent of thenitratewas infinemode. This lowamount ofcoarsemode nitrate can be attributed to the emission inputs. Only 25per cent of the total anthropogenic particulatematter emissionswerein coarse mode. Based on the chemical composition data, whichshows that ammonium was responsible for 25 per cent of nitrateneutralization. Thus, a rough calculation, assuming that the restof the free nitrate would react entirely with coarse particles, leads to3 times higher nitrate aerosols. This shows that inclusion ofsoil emissions to the model would significantly improve the nitrateunderestimation. Factor analysis (PMF) from Kocak et al. (inpreparation) identified two groups that were mainly characterizedwith sulfate and ammonium. They showed that considering thewinter period, a complete neutralization of sulfate occurred, whichshould lead to nitrate associate with the coarse particles. Due to thelack of natural coarse emissions, the model was not able to properlycalculate the coarse mode nitrate, and in return, produced largelyunderestimated nitrate concentrations. The results also suggest thatammonia emissions might be well underestimated, which leads tounderestimations in all sulfate, nitrate and ammonium aerosols.

Fig. 2. Simulated versus observed a) 2-m temperature and b) 10-m wind speed and simulated c) PBL height, d) cloud fraction and e) precipitation above the ESC sampling stationbetween 13th and 17th of January, 2008.

U. Im et al. / Atmospheric Environment 44 (2010) 3085e3094 3089

The predicted day-to-day variability of PM10 levels for allmunicipality air quality stations overestimated the observations by12 per cent on average, whereas the average bias was 8.7 mgm�3

(Table 5). The IOA values overall showed that the model perfor-mance varied from poor to good with an average IOA value of 0.42.

Table 3Statistical comparison of WRF simulated temperature and wind speed with obser-vations for Kandilli, Istanbul.

Measures Kandilli

Temp 2 m (�K) Wind Speed (ms�1)

Correlation 0.86 0.60Observed Mean 278.13 1.84Model Mean/Obs. Mean 1.00 0.98Observed STDEV 2.97 1.13Model STDEV/Obs. STDEV 1.00 0.92BIAS 0.36 �0.03ABSE 1.23 0.75RMSE 1.60 0.96IOA 0.92 0.77

This table shows that almost all stations on European sidewere overestimated whereas stations on Asian side were under-estimated. Particularly, Alibeykoy stationwas highly overestimated(factor of 2.8 on average), which is at the very center of the emis-sions hot spot. These discrepancies can be attributed to the fact thathigher emissions were calculated for the European side of the citywhere most of the traffic and industrial activities take place. Theroad-traffic emissions, being the dominant source category forPM10 emissions, as well as the industrial combustion, are higher inthe European site than on the Asian site (Markakis et al., 2009). Itshould be noted that traffic emissions introduces high uncer-tainties, particularly for PM emissions from heavy duty vehicles,where the activity levels were very uncertain. The traffic originatedPM emissions comprised 69 per cent of the total primary organicaerosol emissions (Markakis et al., 2009). It should be noted that alltraffic originated PM emissions were located to the fine mode PMemissions in the inventory. Residential coal combustion emissionswere calculated based on the total amount of coal consumedon annual basis, which was the only available information. Thisamount was distributed spatially based on a function which takes

Fig. 3. 24-h average PM10 concentrations for a) 13th, b) 14th, c) 15th, d) 16th and e) 17th of January, 2008 for the Istanbul domain.

U. Im et al. / Atmospheric Environment 44 (2010) 3085e30943090

into account the consumption of natural gas as well as populationdensity (Markakis et al., 2009). This uncertainty may have intro-duced the over estimations of PM and sulfate levels, due to possibleoverestimated EC and SO2 emissions.

Considering all stations, on average 91 per cent of the particulatematter was calculated to be in fine mode (PMfine). This may beoriginated from an overestimation of fine mode emissions, partic-ularly traffic, as well as the lack of natural coarse emissions. Totalorganic aerosols (OCij) and elemental carbon (EC) comprised 30and 17 per cent of the total PM10 mass, respectively. Model results

showed 99.8 per cent of total organic aerosols consisted of primaryanthropogenic organic aerosols (POA). The contribution of the totalinorganic species (PM INORT) and total coarse mode aerosols (PMCTOT) were calculated to be 12 and 9 per cent, respectively. Theunspecified coarse mode anthropogenic mass (ACORS) accountedfor 1 per cent of the total aerosol mass. The model calculated 0.12per cent contribution from SOA of biogenic origin and 0.04 per centcontribution from SOA of anthropogenic origin, which agrees withFig. 5. The biogenic SOA were most probably originated from thedefault IC and BC profiles provided by CMAQ.

Fig. 4. Comparison of 24-h averaged CMAQ calculations and observations for a) PM10, b) SO4, c) NO3� and NH4

þ at Bogazici University station.

U. Im et al. / Atmospheric Environment 44 (2010) 3085e3094 3091

The spatial-averaged distributions of OC/PM10 and EC/PM10 ratiosfor the 5-day episode are presented in Fig. 6. The figure shows thataround the emission spots, the OC/PM10 ratios were highest (0.40).The EC/PM10 distribution shows the industrial activity aroundKocaeli Gulf, which is the highest industrialized area in Turkey.The figure also points out the highly populated areas aroundBosporus strait, whichmight be attributed to residential combustionand shipping emissions. The figure clearly shows the importance ofprimary organic aerosols. It should be noted that the lack of biogenicemission data led to very low OC/PM10 ratios outside the populatedand industrial areas, particularly on the large forested areas innorthern parts of _Istanbul, where anthropogenic emissions are alsounderestimated.

3.2.2. Sensitivity analysisThe Brute force sensitivity simulations showed that the most

significant changes in PM10 concentrations at the ESC stationwere inresponse to the changes done simultaneously in all emissions. Thehighest increase of daily PM10 concentrations during the episodeoccurred due to the 10 per cent increase and decrease in all emis-sions (�8.90 per cent), which was also the case for the inorganicaerosol concentrations (Table 6). This significant response of PM10

Table 4Comparison of the base case CMAQ results with the observations for the ESC station.

Measures PM10 SO42� NO3

� NH4þ

Observed Mean (mgm�3) 107.1 9.8 6.8 4.1Model Mean/Obs. Mean 0.9 0.8 0.3 0.7Observed STDEV (mgm�3) 15.14 1.08 1.22 1.16Model STDEV/Obs. STDEV 1.3 1.1 0.5 0.9BIAS (mgm�3) �8.9 �2.1 �4.6 �1.28ABSE (mgm�3) 20.7 2.7 4.6 1.28RMSE (mgm�3) 24.5 2.9 4.5 1.44IOA 0.9 0.8 0.5 0.8

concentrations to changes in all emissions can be attributed to thefact that along with all emissions, PM emissions, which might besignificantly overestimated due to traffic and coal combustion,are also changed. This contributes significantly to the primaryanthropogenic aerosols. The increase of SO2 concentrations slightlyincreased the sulfate aerosol concentrations whereas led toa decrease in ammonium and nitrate concentrations. Ammoniumfirst neutralizes sulfate producing ammonium sulfate (NH4)2SO4 andthen the remaining amount produces ammonium nitrate (NH4NO3)if temperature conditions favor its presence in the atmosphere(Smyth et al., 2006). By increasing SO2 emissions, more (NH4)2SO4aerosols are produced, thus reducing the available ammoniumcations to produce NH4NO3 aerosols.

Reducing ammonia emissions led to significantly reducednitrate, sulfate and ammonium aerosols. Due to complete neutral-ization of sulfate with ammonium, the increase of ammonia led to

Fig. 5. Contribution of emissions, chemistry and cloud processes to sulfate aerosollevels at ESC station.

Table 5Comparison of the base case 24-h averaged predicted PM10 results with observations for the municipality air quality stations.

Alibeykoy Besiktas Sarachane Sariyer Yenibosna Esenler Kartal Umraniye Uskudar

Obs. Mean (mgm�3) 92.7 105.5 88.5 95.2 126.1 202.2 116.8 92.7 103.2Model Mean/Obs Mean 2.8 1.1 1.2 1.1 1.1 1.0 0.4 0.4 1.0BIAS (mgm�3) 179.6 14.4 15.5 9.9 22.0 �4.6 �96.8 �57.9 �4.2ABSE (mgm�3) 179.6 48.0 67.7 25.0 62.3 34.6 100.4 57.9 70.8RMSE (mgm�3) 178.5 63.7 101.2 32.8 65.1 42.3 105.9 58.1 81.4IOA 0.2 0.4 0.1 0.8 0.6 0.8 0.4 0.5 0.2

Fig. 6. Spatial distributions of a) OC/EC, b) OC/PM10 and c) EC/PM10 mean ratios from 13th to 17th of January, 2008.

U. Im et al. / Atmospheric Environment 44 (2010) 3085e30943092

more free nitrate to react with ammonium (5.16 per cent increase).Decrease of ammonia led to less ammonium to combine with nitricacid to form ammonium nitrate (4.61 per cent decrease). Sulfatewas reduced because there were more acidic water droplets due toless buffering by ammonia that inhibits aqueous sulfate productionthat is pH dependent. Ammonium reductions are associated withboth nitrate and sulfate reductions. Reducing VOC emissionsreduced organic matter and production of nitric acid from NOx dueto reduced photochemical activity, leading to decreases in sulfateand nitrate concentrations. The results overall suggested that a 10per cent change in individual pollutants did not give significantchanges in PM10 concentrations during the specific winter episode.Significant improvement in PM10 concentrations is achieved whenchanges in all precursor emissions together are assumed, particu-larly the PM emissions. These results also suggest that the PMconcentrations over Istanbul are significantly local and primary inorigin, rather than long-range transport or chemistry (SOA). Miti-gations option must consider the local PM emissions itself in order

Table 6Response (per cent) of aerosol concentrations at the Bogazici University station to�10 per cent changes in the emissions.

Scenarios PM10 SO4 NO3 NH4

Increase all 8.90 5.84 6.81 6.72Decrease all �8.89 �5.76 �6.72 �6.75Increase SO2 0.18 0.85 �0.75 �0.01Decrease SO2 �0.29 �1.00 0.46 0.04Increase NOx 0.01 �0.05 2.65 �0.27Decrease NOx �0.02 0.05 �2.65 0.27Increase NH3 0.31 0.53 5.16 4.00Decrease NH3 �0.27 �0.33 �4.61 �4.10Increase VOC 0.02 0.02 0.39 0.02Decrase VOC �0.02 �0.01 �0.45 �0.02

to accomplish reductions. However, it should be noted that theseresponses cannot be extrapolated to larger changes in emissions.

4. Conclusions

The presented study produced new results for the assessment ofPM10 levels together with the first attempt to model the speciatedaerosols and compare with observations from the first compre-hensive PM database produced for the region. An offline-coupledWRF/CMAQ high resolution modeling system has been used inorder to analyze the observed exceedences in PM10, in conjunctionwith the aerosol chemical characterization, sulfate, nitrate andammonium concentrations during a five-day winter episode of13e17 January 2008. Istanbul and surrounding areas were used inthe innermost domain of the model on 2 km resolution, employinga first-time model-ready emission inventory developed for the cityof _Istanbul.

Themodel produced PM10 concentrationswere comparablewiththe observations in terms of magnitude. The results point that theemission estimates from traffic and residential coal combustionmight be overestimated due to the fact that the over estimations ofPM10 and sulfate aerosols occurred at regions where these sourcesare dominant. The very high contribution of fine mode (91 per cent)clearly suggest that the emissions should be reconsidered andmodified, particularly the traffic emissions, which alone provided 70per cent of the organic portion of the fine mode aerosol. Due to thelack of activity data outside the populated urban and industrial areasof the city, the emissions aremost probably underestimated, leadingto low aerosol concentrations around these areas. Important emis-sions such as sulfuric acid and biogenics, which may play significantrole in treatment of secondary aerosols, were not included study.These results suggest that the chemical speciation in the emissioninventory should be improved. The 3-days spin up period can be

U. Im et al. / Atmospheric Environment 44 (2010) 3085e3094 3093

short for the aerosols to reach equilibrium. As the main focus is theinnermost domain, which roughly starts from 1000 km fromthe southern and eastern boundaries and more than 4000 km fromthe western and northern boundaries of the mother domain, theeffect of boundary conditions is small and the spin up period canminimize the effects. However, regarding the initial conditions,longer spin up periods can be used in order to reach equilibrium.

The Brute force sensitivity simulations showed the highestincrease in PM10 concentrations occurs in the city center around thecoastal area where most of the population is located. PM10concentrations respond most to the simultaneous changes in allemissions. Changes in availability of sulfate, nitrate and ammoniumspecies through emission controls lead to the perturbation of gas-to-particle conversion. The concentration responses to reductions incombinations of precursor emissions are generally consistent withour understanding of the chemistry mechanism, but do notreflect linearity or additivity in the effects. The results show that PMepisodes in GIA are largely local-originated, as changes in theprimary particulate emissions led to significant responses of theaerosol levels compared to responses to the individual emissionchanges of the precursors. As a conclusion, the study shows thenecessity to develop emission controls strategies that target directlyto the primary particulate emissions in the region, more than thegaseous precursor emissions that contribute to the SOA levels.

Overall, it is concluded that the first results of the WRF/CMAQmodeling system, using a recently developed inventory for Istan-bul, present significantly improved results for Istanbul, especially interms of magnitude, when compared to earlier modeling studiesfor the area, as well as providing an insight to chemical compositionof the aerosols in Istanbul atmosphere. Better representationof aerosol chemistry is achieved by simulating the interactionsbetween the fine and coarse modes, compared to the earlierstudies. However, the reproduction of the observed magnitudessuggests that the exceedences of PM levels in Istanbul are mostlylocal and anthropogenic originated. Further improvement can beachieved by the inclusion of biogenic and natural sources in modelruns, especially for the secondary aerosols as well as coarse modeparticulate matter. The study also shows that a more developedmonitoring network should be established that provides chemicalcomposition of both fine and coarse aerosols, together with therelevant gaseous precursor emissions, on a wide spread domain.

Acknowledgements

The authors would like to acknowledge the financial supportby the Scientific and Technological Research Council of Turkey(TUBITAK, COST project no: 105Y005), Bogazici University ResearchFund projects 07YD and 05Y105, and the EU funded CITYZENproject for making the aerosol samplings and the analysis possible.

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