Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1039
Title: The using of principal component analysis for the assessment of water quality in KIRMIR BASIN
Authors: Dede, Ozlem Tunc
Misir, Filiz Yazici
Telci, Ilker
Keywords: Factor analysis
Multivariate statistic
Water pollution
Issue Date: 2017
Publisher: National Research and Development Institute for Industrial Ecology, INCD-ECOIND
Abstract: Water is one of the most important nutrient for human and also for aquatic organism. Poor water quality has adverse effects on human health and aquatic life. Hence, protecting the water resources from pollutants and the monitoring of water quality is important. In recent years, some kind of methods such as water quality index model, regression analysis, factor analysis, principal component analysis, cluster analysis, etc. has been developed for easy assessment and interpretation of large amount of water quality data. Among these methods, multivariate statistical analysis has an advantage of giving an idea about possible sources of pollution. In this study, the assessment of water quality of 10 different sampling station in Kirmir Basin which is one of the most significant drinking water resources of Ankara, the capital city of Turkey has been investigated by using multivariate statistical methods (principal component analysis-PCA and factor analysis-FA). 18 water quality parameters were analysed for each sampling station and used for the statistical analysis. The correlations between parameters and sampling stations were evaluated by using statistical techniques in terms of underlying factors. FA/PCA identified water quality parameters in five groups. The results revealed that Kirmir Basin was mainly affected from agricultural activities, urban land uses and livestock activities. The improving of the water quality in this region can be achieved by controlling these activities.
Description: International Symposium "The Environment and the Industry", SIMI 2017
URI: http://hdl.handle.net/123456789/1039
DOI:10.21698/simi.2017.0025
ISSN: L : 1843-5831 (on-line): 2457-8371
Appears in Collections:SIMI 2017

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