Multivariate statistical methods for characterization of waste water quality

Authors

  • Darja Kavšek Regional Technological Centre Zasavje, Chemical-Technological Laboratory, Nasipi 48, 1420 Trbovlje, Slovenia
  • Darinka Brodnjak Vončina Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia

DOI:

https://doi.org/10.36547/nbc.1135

Keywords:

waste waters, water quality, chemometrics, principal component analysis, classification

Abstract

The aim of this work is focused on water quality classification of the waste waters and evaluation of pollution by the monitoring measurements during period 2006-2008. Environmental monitoring was performed in the region of Trbovlje, Slovenia, with two sampling sites and 15 chemical and physicochemical water quality parameters (pH, temperature, suspended solids, settling matter, chemical oxygen demand, biochemical oxygen demand, AOX (adsorbable organic halogens), total phosphorus, ammonium, nitrite, sulphate, chloride, fluoride, sulphide and mineral oil content) monitored in monthly periods (total of 60 objects x 15 variables). For handling the results different chemometric methods were employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). Monitoring of general pollution of waste waters and following measuring parameters which are above permitted concentration level can be used for searching of pollution source and for planning prevention measures from pollution, as well. The study allows drawing new information from the data sets such as patterns of similarity between sampling locations, sources of pollution in the environment, seasonal behavior of chemical contents and time trends.

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Published

2021-09-02

How to Cite

Kavšek, D., & Brodnjak Vončina, D. (2021). Multivariate statistical methods for characterization of waste water quality. Nova Biotechnologica Et Chimica, 9(3), 265-270. https://doi.org/10.36547/nbc.1135

Issue

Section

Research Articles
Received 2021-09-02
Published 2021-09-02