engleski

Multivariate statistical methods for spatial characterization of surface water quality

Introduction: The aim of this work is focused on spatial water quality classification based on the monitoring dataset for different location. Dataset for spatial classification of surface water consist of physico chemical water quality parameters monitored in monthly periods for seven locations. Material and Methods: For handling the dataset different chemometrics methods were employed, such as basic statistical methods, Pearson`s correlation coefficients, the principal component analysis (PCA) and cluster analysis (CA). Results: The obtained results show and explain the value movement of quality indicators in relation to the maximum permissible concentration prescribed in the Regulations as well as some significant correlation of individual variables for a specified period. Discussion: This study allows drawing out new information from the monitoring datasets such as patterns of similarity between different locations, sesonal behavior of physico-chemical contents and time trends. The implementation of these analyses is necessary for the preliminary study for water usage and healthcare protection of the population in the region.

Water quality, statistical methods, Pearson`s correlations, cluster analysis (CA), Principal component analysis (PCA).

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