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Water Quality Assessment After The Spill Of The Former Mining Company In Peru Using The Grey Clustering Method

Published in: Prospective and trends in technology and skills for sustainable social development. Leveraging emerging technologies to construct the future: Proceedings of the 19th LACCEI International Multi-Conference for Engineering, Education and Technology
Date of Conference: July 19-23, 2021
Location of Conference: Virtual
Authors: Chiara Carbajal (Universidad de Ciencias y Humanidades, PE)
Alexi Delgado (Pontificia Universidad Católica del Perú, PE)
Hassan Zarria (Pontificia Universidad Católica del Perú, PE)
Johan Ramirez (Pontificia Universidad Católica del Perú, PE)
Gresli Camargo (Pontificia Universidad Católica del Perú, PE)
Angela Cornelio (Pontificia Universidad Católica del Perú, PE)
Full Paper: #171

Abstract:

The area that involves the Santa river watershed is characterized by its mining potential, this fact has generated the existence of environmental mining liabilities. Therefore, it is necessary to evaluate water quality in areas where there may be an impact caused by a former mine harming mains rivers in the surrounding areas. In this work, we apply the center-point triangular whitenization weight functions (CTWF) method, which is based on the grey systems theory that is an approach from artificial intelligence. In the case study, we analyzed monitoring points (after the accident) near the area affected by a tailings spill, these points correspond to the Pelagatos ravine that co-flows with the Santa River. The monitoring data were obtained from Water National Authority of Peru (ANA by its Spanish acronym). The CTWF method was applied using parameters of water quality such as Ph, OD, SS, Fe, and Mn. Then, the results were ranked using the Prati scale. Consequently, the results showed that 80% of the monitoring points were classified as contaminated including points highly contaminated. Finally, the results of this study could be used by local authorities, supervision organisms, government or the company in charge of closing the liabilities to make the best decision on the affected area.