DOI: 10.5593/SGEM2014/B31/S12.104


I. G. Breaban, M.Paiu, A.M.Pintilie, I.Cretescu
Wednesday 1 October 2014 by Libadmin2014

References: 14th International Multidisciplinary Scientific GeoConference SGEM 2014, www.sgem.org, SGEM2014 Conference Proceedings, ISBN 978-619-7105-13-1 / ISSN 1314-2704, June 19-25, 2014, Book 3, Vol. 1, 815-822 pp

The quality of drinking-water is a powerful environmental determinant of health. The World Health Organization, in Water Quality and Health Strategy 2013-2020, highlights that the quality of water has an important impact on health, whether used for drinking, domestic purposes, food production or recreational purposes. Romania obtained, under the terms of the Accession Treaty, transition periods to comply compliance with the regulations regarding drinking water quality standards until 2015. Regarding the quality of drinking water, the main task under the Drinking Water Directive is to meet the quality standards of drinking water provided by centralized systems. Drinking water supply of Iasi is made from ground and surface sources, stored in 25 tanks (9 underground and 15 for the surface sources). The quality of drinking water supplied in each storage tank is daily monitored, aiming on mainly physico-chemical indicators (temperature, water hardness, pH, oxidability, ammonium, nitrites) and microbiological ones. The research methodology follow three main stages: collecting daily data sets about water quality, ascending hierarchy of water quality from storage tanks, and selecting the most important indicators for assessing drinking water quality. For the evaluation of spatial variation of water quality data of Iasi between 2007 and 2014, multivariate statistical methods were used in finding hidden relationships among them as well as data clustering. After the determination of the parameter correlation matrix, eigenvalues and factor loadings have been determined. All statistical computation was made using SPSS software. PCA (Principal Component Analysis) is a data analysis technique used to describe the multivariate structure of the data, being helpful in identifying meaningful underlying variables and to reduce the dimensionality of a set of data and determine linear relationships between the variables. The aim of this paper is to use PCA for reducing the number of indicators, which must be determined during a regular monitoring, and to recognize basic features of drinking water quality. The fairness of the PCA results was verified by the cluster and factor analysis.

Keywords: drinking water quality, Iasi city, multivariate statistical methods 815