Abstract
Water quality measurements in rivers are usually performed at intervals of days or months in monitoring campaigns, but little attention has been paid to the spatial and temporal dynamics of those measurements. In this work, we propose scrutinizing the scope and limitations of state-of-the-art interpolation methods aiming to estimate the spatio-temporal dynamics (in terms of trends and structures) of relevant variables for water quality analysis usually taken in rivers. We used a database with several water quality measurements from the Machángara River between 2002 and 2007 provided by the Metropolitan Water Company of Quito, Ecuador. This database included flow rate, temperature, dissolved oxygen, and chemical oxygen demand, among other variables. For visualization purposes, the absence of measurements at intermediate points in an irregular spatio-temporal sampling grid was fixed by using deterministic and stochastic interpolation methods, namely, Delaunay and k-Nearest Neighbors (kNN). For data-driven model diagnosis, a study on model residuals was performed comparing the quality of both kinds of approaches. For most variables, a value of k = 15 yielded a reasonable fitting when Mahalanobis distance was used, and water quality variables were better estimated when using the kNN method. The use of kNN provided the best estimation capabilities in the presence of atypical samples in the spatio-temporal dynamics in terms of leave-one-out absolute error, and it was better for variables with slow-changing dynamics, though its performance degraded for variables with fast-changing dynamics. The proposed spatio-temporal analysis of water quality measurements provides relevant and useful information, hence complementing and extending the classical statistical analysis in this field, and our results encourage the search for new methods overcoming the limitations of the analyzed traditional interpolators.
Highlights
Pollution is related to the introduction into the environment of substances, from anthropogenic or natural origin, which are harmful or toxic to humans and ecosystems
We propose scrutinizing the scope and limitations of state-of-the-art interpolation methods aiming to estimate the spatio-temporal dynamics of relevant variables for water quality analysis usually taken in rivers
In order to establish a comparison between deterministic and statistical interpolation, we started by scrutinizing the value of k to be used as a free parameter in the k-Nearest Neighbors (kNN) algorithm
Summary
Pollution is related to the introduction into the environment of substances, from anthropogenic or natural origin, which are harmful or toxic to humans and ecosystems. Pollution usually alters the chemical, physical, biological, or radiological integrity of soil, water, and living species, resulting in alterations of the food chain, with effects on human health [1,2]. Water pollution is mainly due to the increment in urban and industrial density. Water 2016, 8, 507 to public health and jeopardizes the continuous use of water reserves [3]. Contamination of watercourses is a consequence of wastewater discharge, from municipal, industrial, or farming runoffs [4]. Urban wastewater is a complex mixture containing water (usually over 99%)
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