DOI: /10.5513/JCEA01/21.4.3013

Original scientific paper

Some preliminary investigations of water quality parameters in a Hungarian thermal lake, Hévíz

2020, 21 (4)   p. 896-904

Szabina Simon, Brigitta Simon, Gábor Soós, Tamás Kucserka, Angéla Anda

Abstract

Recently, there were only a few investigations published related to thermal waters in all over the world. In this study, an assessment on lake water quality was carried out in Lake Hévíz and its effluent, to provide valuable information about the present lake water quality in the winter season. In Hévíz, the thermal lake has an economic, geological and medical significance, and plays an important role in the tourism industry. Monitoring the quality of water resources - where thermal waters are present - is of primary importance. The chemical composition of geothermal waters often differs markedly from surface waters. The direct drainage of used geothermal waters getting into freshwaters is usually not authorized, so the excess thermal water of Lake Hévíz dilutes in the outflow effluent. A principal component analysis (PCA) was applied to explore the surface water quality dataset. Three sampling points were chosen from the effluent characterized by different water temperatures. The study took place between 9th December, 2019 and 16th March, 2020. There were 9 sampling times to measure conductivity, pH, biological and chemical oxygen demand, total organic carbon, ammonium-, phosphate-, sulphate concentration at the same sampling time. The highest water quality values were determined in the third sampling point of the outflow effluent, where the water temperature was the lowest. Temperature is an important factor in the aquatic environment since it affects directly or indirectly the aquatic flora and fauna. This study is important in providing comprehensive information on water quality for decision makers in particular the thermal waters and valuable reference for international researchers.

Keywords

Lake Hévíz, Hévíz effluent, water quality, principal component analysis (PCA)

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