Fig. 1; Study Area, Source (PPC), Meteorological Stations (Souda and Airport) and Modeling Domain.

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1 ON THE SENSITIVITY OF AERMOD TO SURFACE PARAMETERS UNDER VARIOUS ANEMOLOGICAL CONDITIONS Geoge Kavounis, Despina Deligiogi and Kostas Philippopoulos National and Kapodistian Univesity of Athens, Depatment of Physics, Physics of Atmospheic Envionment Lab., Athens, Geece INTRODUCTION Gaussian type models ae widely used in atmospheic dispesion modeling fo egulatoy puposes. AERMOD is a steady-state plume model fo shot-ange dispesion studies (up to 50km) fom stationay industial-type souces. Its meteoological pepocesso, AERMET, in ode to constuct Planetay Bounday Laye (PBL) similaity pofiles, equies land use suface chaacteistics namely, Albedo, Bowen Ratio and Suface Roughness Length. These paametes ae not diectly measued at the meteoological stations and a subjective estimation is often necessay. This study, by examining the sensitivity of AERMOD to land use paametes, evaluates the impotance of thei epesentative selection. The analysis is focused on the esulting uppe end modeled concentations, which ae the most impotant in applied egulatoy modeling. The study was caied out fo sulfu dioxide on a daily aveaging peiod basis, using the vaious pevalent anemological conditions inheent in the aea of study. AREA OF STUDY The modeling aea is at the southeasten pat of the Chania Plain, located on the island of Cete, Geece (Figue 1). The geate aea is consticted by physical boundaies. These ae the White Mountains Range on its Southen side and the Aegean coastline which lies towads the Noth and East. The topogaphy in the aea is faily complex. This is due to the poximity to the sea and also because of the hills which ise shaply into mountainous heights. Fig. 1; Study Aea, Souce (PPC), Meteoological Stations (Souda and Aipot) and Modeling Domain. AERMOD MODELING SYSTEM The AERMOD modeling system, developed by the Ameican Meteoological Society (AMS) and the U.S. Envionmental Potection Agency (EPA), consists of two pe-pocessos and the dispesion model. Its meteoological pe-pocesso, AERMET, povides AERMOD with the meteoological infomation it needs to chaacteize the PBL. The atmosphee is descibed in AERMET by similaity scaling elationships using outine meteoological data (wind speed and diection, ambient tempeatue and cloud cove), suface chaacteistics (Albedo, Bowen Ratio and Suface Roughness Length) and uppe ai sounding data. AERMAP, the teain pepocesso, chaacteizes the teain, using a Digital Elevation Model (DEM) and geneates Page 43

2 ecepto gids fo the dispesion model. The dispesion model in the Stable Bounday Laye (SBL) assumes both the vetical and hoizontal distibutions to be Gaussian. In the Convective Bounday Laye (CBL) the hoizontal distibution is also assumed to be Gaussian, but the vetical distibution is descibed by a bi-gaussian pobability density function. The AERMOD modeling system may be used fo flat and complex teains as it incopoates the concept of a citical dividing steamline (Cimoelli, A.J. et al., 2004, Pey S.G. et al., 2005, Holmes N.S. and L.Moawska, 2006). AERMOD INPUT The modeled industial souce is a diesel powe geneating plant opeated by the Public Powe Coopeation S.A. (PPC). It is situated in a sububan aea, on the outskits of the city of Chania (35.49 o N and o E) and its seven buoyant point souces have diffeent technical and opeational chaacteistics (Table 1). Table 1. Technical chaacteistics of the seven buoyant point souces Point Souce Stack Height (m) Diamete (m) Exit Speed (m/sec) Exit Tempeatue ( o C) Stack Stack Stack Stack Stack Stack Stack The model was un to estimate daily aveaged concentations of sulfu dioxide (SO 2 ) at each ecepto, coveing an aea of 51km 2. The powe plant is situated at the Notheasten pat of modeling domain (Figue 1). A Catesian gid was used containing 20,691 eceptos with a esolution of 50m. The input meteoological data was obtained fom two monitoing stations. A two-yea data set (August 2004 July 2006) of mean houly values of wind speed, wind diection and ambient tempeatue ae povided fom the station of Souda (35.50 o N and o E), while the equied cloud cove obsevations ae acquied fom the neaby Militay Aipot of Souda station. The monitoing station of Souda is found to be epesentative of the modeling domain, due to its spatial poximity to the modeled aea (within its boundaies) and its esemblance in land-use chaacteistics. METHODOLOGY Pevious studies have shown that AERMOD is highly sensitive to wind speed and diection (Steib, 2005) and to Suface Roughness Length (Cape E. and E. Ottesbug, 2004, Gosch T.G. and R.F.Lee, 1999). This study aims to identify the degee of sensitivity of AERMOD to the changes in Albedo, Bowen Ratio and Suface Roughness Length unde the vaious anemological conditions, pevalent in the Plain of Chania. In ode to detemine the pedominant anemological conditions in the aea, a k-means clusteing algoithm was applied to the daily wind sequences. Fo each of the fomed goups a epesentative day is selected, using as a citeion the esemblance to the centoid of the goup (measued by the Euclidian distance, given in Equation 1). Fo evey day, multiple uns of AERMOD ae pefomed with vaying suface paamete values in the whole ange poposed by EPA fo vaious land types. The two highest estimated concentations of each simulation ae compaed with those of a efeence case, which coesponds to fixed values fo Albedo, Bowen Ration and Suface Page 44

3 Roughness Length, appopiate fo the aea of study. The ange of suface chaacteistics value used in this study is poposed by EPA. K-Means Clusteing The tem Cluste Analysis encompasses a numbe of diffeent methods and algoithms fo gouping objects into espective categoies. K-means is an unsupevised clusteing algoithm which classifies a given data set into a cetain pedetemined numbe of clustes. The main idea is to define k centoids, one fo each cluste, and associate each obsevation of the data set to the neaest centoid. K-means uses an iteative algoithm which minimizes the sum of distances fom each object to its cluste centoid, ove all clustes, using the following metic: whee J = k n x i c j j= 1 i= 1 x i c is the distance between obsevation j x i (1) and centoid c j. The algoithm moves objects between clustes until the sum can no longe be deceased. The esult is a set of clustes that ae as compact and well-sepaated as possible. (Ankest M. et al., 1999). Since we wee inteested in finding goup of days with simila daily wind evolution, the objects xi to be clusteed ae defined as: xi = ( ui1, vi1, K ui24, vi24 ) whee (u ij,v ij ) ae the j-th hou mean wind components of the i-th day. The days wee initially classified into 30 clustes and an additional subjective gouping was pefomed by examining the centoids of those clustes. The outcome was 10 diffeent clustes, which cove the main anemological chaacteistics of the egion. In Figue 2 the centoid of each cluste is pesented along with the membes of each cluste. Fig. 2; Cluste Centoids and Wind Vectos of Cluste Membes. The 1 st cluste coesponds to a medium intensity andom wind field, while the 2 nd cluste to a daily anemological patten dominated by local flows, such as sea and land beeze ciculations. Local flows ae dominant in the membes of the 3 d cluste with the wind vecto gadually shifting fom noth to east between the hous of 11:00 and 16:00 UTC. The membes of the Page 45

4 4 th cluste ae days with medium to high intensity westely winds, obseved duing the moning and aftenoon with vey low winds duing the night. In the case of the 5 th cluste, we obseve the opposite evolution of the wind vecto whee low winds pevail duing the night until mid-day and medium intensity westely winds fo the emainde of the day. In cluste 6, light winds ae obseved thoughout the day, intensifying between the hous of 9:00 and 16:00 UTC with an easten diection. The daily anemological patten of cluste 7 is a nothen flow, intensifying fom mid-moning until 14:00 UTC. In cluste 8 we obseve nothwesten winds thoughout the day, becoming stonge between 8:00 and 14:00 UTC. Westely winds pevail in the 9 th and 10 th cluste, the diffeence being that in the 9 th cluste the wind speed is stonge. Sensitivity Analysis Results The following figues exhibit that AERMOD is highly sensitive to changes in Suface Roughness Length and athe indiffeent to Albedo and Bowen Ratio vaiations. Fig. 3; Sensitivity of AERMOD to changes in Suface Roughness Length. The uppe diagams efe to the maximum concentations nomalized by the maximum concentation of the base case C 0, while the lowe ones coespond to the second maximum estimated concentations. Fig. 3; Sensitivity of AERMOD to changes in Albedo. In Figue 3 it is obseved that fo vey low values of Suface Roughness Length the peak Page 46

5 estimated concentations might get up to six times highe than the estimation fo the base case. Also, in the ange of values aound 0.2, which ae typical fo cultivated land, an eo in Suface Roughness Length s estimation in the ode of 0.1, popagates to estimated concentations, esulting in an oveall eo even highe than 20%. Futhemoe, as expected, sensitivity to all paametes is not unifom acoss diffeent anemological conditions. Fig. 5; Sensitivity of AERMOD to changes in Bowen Ratio. CONCLUSIONS This study shows that estimated concentations by AERMOD can vay substantially due to nomal vaiations in the Albedo, Bowen Ratio and Suface Roughness Length. Theefoe, thei epesentative selection should be consideed as an impotant pat of the modeling pocess. Reasonably and accuate estimations of these paametes should be not only based on poposed tables, but also on field measuements. ACKNOWLEDGEMENTS The wok descibed in this pape has been co-funded by the Euopean Social Fund and Hellenic National Resouces unde the PYTHAGORAS II pogamme (EPEAEK II). REFERENCES Holmes N.S. and L.Moawska, 2006: A eview of dispesion modelling and its application to the dispesion of paticles: An oveview of diffeent dispesion models, Atmospheic Envionemnt, 40, Steib R., 2005: Regulatoy Modelling Activity in Hungay, Advances in Ai Pollution Modelling fo Envionmental Secuity, Gosch G.T. and R.F. Lee, 1999: Sensitivity of the AERMOD ai quality model to the selection of land use paametes, WIT Tansactions on Ecology and the Envionm., 37. Cape E. and E. Ottesbug, 2004: Sensitivity Analysis Study Consideing the Selection of Appopiate Land-Use Paametes in AERMOD Modeling Analyses, Tinity Consultants, Technical Pape. Ankest M., M.M.Beunig, H.P.Kiegel and J. Sande, 1999: OPTICS: Odeing Points to Identify the Clusteing Stuctue., Poc. ACM SIGMOD 99 Int. Conf. on Management of Data. Cimoelli, A. J., S. G. Pey, A. Venkatam, J. C. Weil, R. J. Paine,R. B. Wilson, R. F. Lee, and W. D. Petes, 2003: AERMOD desciption of model fomulation. U.S. EPA. Page 47