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dc.contributor.authorDelgado, Alexi
dc.contributor.authorM., Acuña
dc.contributor.authorN., Justano
dc.contributor.authorE., Llanos
dc.contributor.authorI., Puma
dc.contributor.authorCarbajal, Chiara
dc.date.accessioned2020-08-17T03:07:59Z
dc.date.accessioned2022-02-22T12:12:43Z
dc.date.available2020-08-17T03:07:59Z
dc.date.available2022-02-22T12:12:43Z
dc.date.issued2020-07
dc.identifier.isbn978-958-52071-4-1
dc.identifier.issn2414-6390
dc.identifier.otherhttp://laccei.org/LACCEI2020-VirtualEdition/meta/FP524.html
dc.identifier.urihttp://dx.doi.org/10.18687/LACCEI2020.1.1.524
dc.identifier.urihttp://axces.info/handle/10.18687/20200101_524
dc.description.abstractWater quality assessment is a current issue of increasing concern in many countries around the world for reasons such as population health, national economic development and the environmental quality of ecosystems. At this juncture, the Grey Clustering method is used to assess water quality at discharge points, from the beginning to the end of the environmental monitoring process in the area of influence of the Anabi mining unit in the Chonta and Milos micro-watershed. The parameters evaluated were pH, dissolved oxygen, total suspended solids (TSS), iron and manganese. The results obtained through the Grey Clustering methodology showed a monitoring point with contamination from a treated water discharge. On the other hand, in order to obtain greater efficiency in the evaluation of water quality, national standard DS 004-2017-Minam (Water Quality Standards) and international standards were used through the PRATI index. Through the results obtained it was observed that (by means of the Prati index ) there is a better classification of the water quality in each point, therefore this research becomes an important tool for future studies to consider the Prati index for greater reliability of results.en_US
dc.language.isoEnglishen_US
dc.publisherLACCEI Inc.en_US
dc.rightsLACCEI License
dc.rights.urihttps://laccei.org/blog/copyright-laccei-papers/
dc.subjectGrey Clusteringen_US
dc.subjectWater parametersen_US
dc.subjectWater qualityen_US
dc.titleApplying the grey clustering method to assess water quality in a watershed in Cusco, Peru
dc.typeArticleen_US
dc.description.countryPeruen
dc.description.institutionPontificia Universidad Católica del Perúen
dc.description.trackEnergy, Water and Sustainable Engineeringen
dc.journal.referatopeerReview


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  • 2020 LACCEI - Virtual Edition
    The Eighteen LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology.

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