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

• Published in: Ecological engineering Vol. 60; pp. 60 - 66
• Main Authors: Di Blasi, J.I. Piñeiro, Martínez Torres, J., García Nieto, P.J., Alonso Fernández, J.R., Díaz Muñiz, C., Taboada, J.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2013; Elsevier
• Subjects: Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; Conservation, protection and management of environment and wildlife; Control equipment; data collection; Environmental degradation: ecosystems survey and restoration; Freshwater; Functional data analysis; Functional depth; Fundamental and applied biological sciences. Psychology; Mathematical analysis; Mathematical models; Monitoring; Monitoring samples; Outliers; pollution; public health; River basins; Spain; surface water; Turbidity; Water quality; Water quality monitoring; water quality standards; watersheds; Spain; Southern Europe; Europe; Europe, Iberian Peninsula, Mino R; Outliers; Monitoring samples; Functional depth; Water quality monitoring; Functional data analysis; Data analysis; Environmental factor; Functional analysis; River basins; Depth; Surveillance; Water quality; Detection
• ISSN: 0925-8574; 1872-6992; 1872-6992
• DOI: 10.1016/j.ecoleng.2013.07.054

•A new methodology of the functional depth has detected outliers in the Miño river basin (NW Spain).•Detection of atypical observations is an important concern in water quality control.•The effectiveness and robustness of the proposed method are evaluated.•The method is able to identify outliers in multivariate data with success. 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.