Application of multivariate statistical methods in determining spatial changes in water quality in the Austrian part of Neusiedler See

作者：Magyar Norbert; Hatvani Istvan Gabor^*; Szekely Ilona Kovacsne; Herzig Alois; Dinka Maria; Kovacs Jozsef

来源：Ecological Engineering, 2013, 55: 82-92.

DOI：10.1016/j.ecoleng.2013.02.005

• Cluster analysis
• Neusiedler See
• Principal component analysis
• Spatial variability
• Water quality

The aim of the present study was to distinguish areas with different chemical properties in Neusiedler See, to determine which background processes are responsible for this pattern, and to discover their spatial distribution. Uni- and multivariate data analysis was applied to the data concerning 13 mainly chemical and some biological parameters for the time period 2000-2009 from 33 sampling sites. The sampling sites were first clustered then grouped. Besides reed belt and open water areas, smaller localities, which are influenced by water inputs (the treatment plant, the river Wulka, the channels of weekend houses) were also distinguished. Using Wilks' lambda distribution it was determined that the main components (ions) have a greater effect on forming the cluster groups than those parameters which stand in close relation to biological processes. These results concurred with those obtained from the principal component analysis (PCA) conducted on the whole lake and on the groups as well. It can be stated that most of the variance in the dataset can be explained by the main components (ions). The spatial distribution of the principal component scores was visualized with isoline maps. The results of this research lead us to the view that Neusiedler See cannot be treated as one homogeneous system. This exceptional variability originates from the lake's shallow water depth, its unstable water balance, and anthropogenic activity (agriculture, tourism, sewage treatment) in the lake's vicinity.

• 出版日期2013-6