Water quality monitoring using cluster analysis and linear models

Abstract

The development of statistical methodologies based on spatial and temporal hydrological data is a very important tool in the monitoring of surface water quality in a river basin. This paper uses cluster analysis and linear models to describe hydrological space–time series of quality variables and to detect changes in surface water quality data collected in the River Ave hydrological basin, located in the north-west region of Portugal. This area receives many untreated effluent discharges from textile industries, which result in extreme pollution. Because of this problematic environmental situation, local authorities installed a network of 20 monitoring sites, producing monthly measurements of quality variables and later began to operate three wastewater treatment plants (WTP) at the end of the 1998 hydrological year. In this work, we propose a two-step methodology to analyse these data which use cluster analysis to classify the quality monitoring sites into spatial homogeneous groups. Then we adjust linear models to the quality variables associated with the clusters, taking into account the seasonal variations throughout the year, different trends for each period of time (before and after the installation of WTPs), and the hydro-meteorological factor. Finally, statistical tests are performed to evaluate the effective role of theWTPs’ performance.