Water quality evaluation by using multivariate statistical techniques and pressure-impact analysis in wetlands: Ahlat Marshes, Turkey

Aysegul Demir Yetis () and Fatma Akyuz
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Aysegul Demir Yetis: Bitlis Eren University
Fatma Akyuz: Bitlis Eren University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 1, No 45, 969-988

Abstract: Abstract This study presents an assessment of surface water quality using multivariate statistical techniques for the Ahlat Marshes in Turkey. In total, 23 physicochemical parameters were analyzed by taking seven different sample sites seasonally between November 2015 and August 2016 for a period of 1 year. The values obtained were compared with the guidelines for inland water resources as suggested by the national standards. At the same time, a pressure-impact analysis was performed by using water quality data obtained for the Ahlat Marshes. As a result, the Ahlat Marshes showed Class IV water quality feature for TOC and Class III for temp, pH and NO2−, according to the national regulations. In an impact analysis, the same four parameters had a “high impact.” It was determined through factor analysis that seven factors explained 80.78% of the total variation in the data. Cluster analysis yielded good results with four different similarity groups, among the seven sampling sites reflecting the water quality characteristics of the wetland systems. It was concluded that fish farms, mining sites, septic tanks, an agricultural area near to the marshes and geogenic origin were all factors affecting the water quality of the Ahlat Marshes. This study was performed the first time; the result shows that there is a no serious risk situation. However, this does not mean that the marshes are clean. It is of vital importance not to act too late in taking the necessary precautions and is monitored at regular intervals for the effective management and sustainability of these wetlands.

Keywords: Ahlat Marshes; Water quality; Pressure-impact analysis; Cluster analysis; Principal component analysis (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00690-5

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