CHEMOMETRICS CHARACTERISATION OF GROUNDWATERS OF THE DRAVA RIVER BASIN

V Sloveniji je podzemna voda glavni vir pitne vode. Zaradi nadzora nad kakovostjo pitne vode in trendov indikatorskih parametrov, se monitoring podzemne vode izvaja v bližini virov pitne vode. Kot vir za kemometrijsko karakterizacijo podzemnih voda smo vključili vzorce devetnajstih merilnih mest na območju Dravske kotline. Z monitoringom vodonosnika Dravske kotline se ugotavljajo trenutne razmere, kvaliteta podzemne vode, obravnavajo se trendi onesnaženosti, ogrožena območja in zbirajo podatki za modeliranje. Opravili smo analizo 104 vzorcev podzemnih voda in izbrali 35 parametrov, ki najbolj značilno opisujejo kakovost podzemnih voda. 104 vzorce smo sledili skozi obdobje dveh let, 2007 in 2008. Po osnovni statistični obdelavi smo poiskali medsebojne korelacije za vse merjene parametre. Z uporabo kemometrijskih metod kot so metoda glavnih osi (PCA), analiza grup (CA), linearna diskriminantna analiza (LDA) in Kohonenove nevronske mreže smo analizirali okoljske podatke podzemnih vod. Z metodo glavnih osi in s Kohonenovimi nevronskimi mrežami smo poizkusili poiskati podobnosti med posameznimi merilnimi mesti. Naštete kemometrijske metode nam uspešno pomagajo pri spremljanju kakovosti podzemnih voda, še posebej v primerih, ko imamo na razpolago veliko število podatkov in parametrov na posameznih merilnih mestih. S pomočjo teh metod lahko ugotavljamo korelacije med parametri in podobnosti med merilnimi mesti ter ocenjujemo trende kvalitete oz. onesnaženosti podzemnega vodonosnika.The main source of drinking water in Slovenia comes from groundwater. Monitoring of groundwater is performed at end in the vicinity of water supply sources to determine the quality and trends of indicator of water quality. As a source for chemometrics characterisation of groundwaters were gathered samples from 19 different sampling sites on the area of the Drava river basin. By monitoring of the Drava river basin the current situations can be determined, groundwater pollution trends and the risk areas can be considered and quality of groundwater can be evaluated. We analysed one hundred and four samples of groundwaters in the time period 2007-2008. The classification is based on 35 parameters which mostly define the quality of groundwaters. Chemometric methods have been used for the classification and comparison of different samples. Multivariate methods like Principal Component Analysis (PCA), the Clustering method (CA), Kohonen Neural Network (ANN) and Linear Discriminant Analysis (LDA) were used. With basic statistical methods, PCA and Kohonen Neural Network we tried to find correlations between sampling sites and parameters. Chemometrics methods are successfully helping us to monitor quality of groundwaters, especially in cases when we have huge number of data obtained by measuring many parameters of samples from different sampling sites. Using these methods we can determine correlations between parameters, find the similarity between sampling sites and assess trends of quality and pollution of underground aquifer