Abstract

AbstractWith the introduction of the Water Framework Directive, the relative importance of smaller waterways increased. This statement is particularly true for Hungary, where water-quality monitoring of most smaller rivers only began 12 years ago. Due to their large number, and the lack of historical data concerning their state, systematic monitoring is a challenge.In the current study, 101 creeks are characterized on the one hand by 13 physico-chemical quality parameters (pH, electric conductivity, chloride ion concentration, dissolved oxygen, oxygen saturation, biochemical oxygen demand, chemical oxygen demand, total organic carbon, ammonium nitrogen, total inorganic nitrogen, total nitrogen, orthophosphate and total phosphorus), on the other hand by their watershed's relief, land use, and point sources' pollution indicators. Euclidean distance between water bodies (henceforth WBs) is calculated according to normalized physico-chemical monitoring values. They are grouped into clusters using the hierarchical clustering method. Watershed characteristics are used to explain the clustering via linear discriminant analysis.The investigation revealed that the main driver of cluster group creation is related to human impact: diffuse agricultural and point-source pollution. The first of the three clusters involved water bodies with low or no human impact; the second cluster contained those with medium-level anthropogenic disturbance, while waters with high pollution values formed the third cluster. Mean distance between heavily polluted waters was 1.5 times higher than that between those showing no or low disturbance, meaning that pristine waters are more similar to one another than polluted ones. The current number of samples per river is twice as high in cluster 1 as in cluster 3, revealing that there is room for optimization of the monitoring system. This contribution uses Hungary as a case study.

Highlights

  • Water quality issues are receiving rising attention worldwide

  • 101 creeks are characterized on the one hand by 13 physico-chemical quality parameters, on the other hand by their watershed’s relief, land use, and point sources’ pollution indicators

  • After grouping the water bodies with Hierarchical Cluster Analysis (HCA) based on the distance matrix (DM), a dendrogram was obtained (Fig. 4)

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Summary

Introduction

Water quality issues are receiving rising attention worldwide. Among the many types of natural water bodies (rivers, lakes, estuaries, shallow and deep groundwater, sea, etc.), surface freshwaters are among the most exposed, and, in particular, rivers the most variable ones (Clement and Buzas 1999; Wetzel 2001). The primary source of such information is on-site water quality measurements along with in-laboratory investigation of samples taken from them (Hatvani et al 2014; Trasy et al 2018). One of the primary driving forces of water quality monitoring (WQM) has always been environmental problems. First WQM efforts date back to the late 19th century (Novotny and Olem 1994; Johnson 2006). The continuous spread of water quality problems

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