Use of principle component analysis to evaluate the effects of agricultural pollution on the aquatic habitats of thrace region: Macro and micro elements - Heavy metals Tarımsal kirliliğin trakya bölgesi sucul habitatları üzerine etkilerinin temel bileşen analizi kullanılarak değerlendirilmesi: Makro ve mikro elementler - Ağır metaller
Journal of Tekirdag Agricultural Faculty, vol.17, no.2, pp.137-148, 2020 (ESCI, Scopus, TRDizin)
- Publication Type: Article / Article
- Volume: 17 Issue: 2
- Publication Date: 2020
- Doi Number: 10.33462/jotaf.561653
- Journal Name: Journal of Tekirdag Agricultural Faculty
- Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
- Page Numbers: pp.137-148
- Keywords: Agricultural pollution, Ergene River Basin, Macro - micro elements, Principle Component Anlaysis
- Open Archive Collection: AVESIS Open Access Collection
- Trakya University Affiliated: Yes
Abstract
In recent years, the use of statistical applications to assess the quality of aquatic ecosystems has increased. Principal Component Analysis (PCA) is a powerful multivariate statistical technique commonly used to assess environmental contamination. Ergene River Basin is the most important aquatic habitat of Thrace Region and is located in the north-western part of the Marmara Region of Turkey. The basin is intensively exposed to agricultural, domestic and industrial pollution. The aim of this study was to evaluate the macro and micro element accumulations (Li, Be, B, Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Cd, Sb, Ba, Tl, Pb) in water of the lotic (lakes) and lentic (reservoirs) components of the Ergene River Basin by using Pearson Correlation Index (PCI) and Principal Component Analysis (PCA). For this purpose, water samples were collected in the spring season of 2018 from a total of 36 stations selected on the basin and some micro and macro element accumulations were determined. According to the results of PCI, significant relationships were found between the investigated macro and microelements in p<0.05 and p<0.01 significance levels. According to the PCA results, four factors explained 87% of the total variance in terms of total percentage variances after rotation.