Analysis and detection of outliers in water quality parameters from different automated monitoring stations in the Miño river basin (NW Spain)

Abstract

Water quality controls help to prevent pollution and to protect public health as well as to maintain and improve the biological integrity of the water bodies, for which, authorities establish water quality standards. Water quality controls involve a large number of variables and observations, often subject to some outliers. An outlier is an observation that is numerically distant from the rest of the data or that appears strongly deviate from other members of the sample in which it occurs. Therefore, identification of atypical observations is an important concern in water quality monitoring and a difficult task because of the multivariate nature of water quality data. Our study provides a new method for detecting outliers in water quality monitoring parameters, using turbidity, conductivity and ammonium as indicator variables. Up to now, methods were based on considering the different parameters as a vector whose components were their concentration values. This innovative approach lies in considering water quality monitoring over time as continuous curves instead of as discrete points, i.e., the dataset studied is considered as a time-dependent function instead of as a set of discrete values in different time instants. This new methodology, which is based on the concept of functional depth, was applied to the detection of outliers in water quality monitoring samples in the Miño river basin with success. Results of this study are discussed here in terms of origin, causes, etc. Finally, the conclusions as well as the advantages of the functional method are exposed.