Multivariate and principal component statistical analysis of contaminationin urban and agricultural soils from north Jordan

A Multivariate analysis and principal component analysis was performed on 3 7 samples gathered from different regions in north Jordan. Twenty-eight des criptors (variables) for each sample were used in these calculations, among them were metal ion concentration, such as Pb, Cr, Co, Zn, Ca, Mg, Fe, Na, K, Al, Cu, Ni, Ti, Si and Mn. Other descriptors were pH, electrical conduc tivity others (EC), and grain sizes. It was found that the samples form thr ee clusters, namely, agriculture, industrial and waste treatment plane regi ons. Statistical analysis showed that the samples are classified into seven classes, with the majority of the samples classified into three clusters. The first three principal correlation be components explained 99.2% of the variance. This method offers a method to classify geographical regions on a pollution bases and to determine the possible sources of pollution for suc h regions. This study showed that some of the agricultural farms were remot e from the pollution sources clusters with polluted regions such as industr ial or waste plants, petrol treatment complexes.