water Article Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany Thuy Hoang Nguyen 1,2,*, Björn Helm 2 , Hiroshan Hettiarachchi 1 , Serena Caucci 1 and Peter Krebs 2 1 Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES), United Nations University, Ammonstrasse 74, 01067 Dresden, Germany;
[email protected] (H.H.);
[email protected] (S.C.) 2 Institute for Urban Water Management, Department of Hydrosciences, Technische Universität Dresden (TU Dresden), Bergstrasse 66, 01069 Dresden, Germany;
[email protected] (B.H.);
[email protected] (P.K.) * Correspondence:
[email protected]; Tel.: +49-35189219370 Received: 11 November 2019; Accepted: 3 February 2020; Published: 5 February 2020 Abstract: Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability.