Origin of uncontrolled water emissions in Alicante: use of discriminant analysis

• Published in: Environmental earth sciences Vol. 61; no. 5; pp. 1005 - 1012
• Main Authors: Chinchón-Payá, Servando, De Blas, Bravo I., Nueda, Roldán M. J., Garcia, Andreu F.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.09.2010; Springer; Springer Nature B.V
• Subjects: Aquifers; Biogeosciences; Drinking water; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental Science and Engineering; Exact sciences and technology; Geochemistry; Geology; Groundwater flow; Hydrology. Hydrogeology; Hydrology/Water Resources; Mineralogy; Original Article; Pollution, environment geology; Prediction models; Silicates; Terrestrial Pollution; Water analysis; Water flow; Water geochemistry; Water resources; Water resources management; Water sampling; Water supply; Iberian Peninsula; Southern Europe; Alicante Spain; Europe; Spain; Spain, Alicante; Discriminant analysis; Water origin; Water network management; Urban groundwater; Water leaks; models; outcrops; discriminant analysis; urban environment; inductively coupled plasma spectroscopy; ground water; water supply; concentration; urban areas; classification; networks; aquifers; hydrochemistry; water resource management; drinking water; statistical analysis; inductively coupled plasma; flow
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-009-0419-4

A model has been developed to predict the origin of uncontrolled water flows. For this purpose, we analysed a total of 52 elements using ICP techniques on 72 water samples in all: 40 of them from leaks in the drinking water supply network and the remaining 32 from groundwater outcrops. The study focused on the cases registered within the Alicante town limits (Spain). The use of multivariate statistical classification tools such as discriminant analysis made it possible not only to reduce the number of analytes, but also to design a predictive model capable of assigning the group to which a new sample belongs. The elements that comprise the model are B, Mn, Te, Ce, Re, Sr, Li and Tl. Given the concentration of each element in the sample, the model can assign its precedence with a total success rate higher than 94%.