Variability of the Water Quality of High Andean Lagoons of Tourist Use Evaluated Through Multivariate Statistical Methods, Junín, Peru
 
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1
Universidad Nacional del Centro del Perú, Centro de Investigación en Alta Montaña, Av. Mariscal Castilla N° 3989-4089, Huancayo, Perú
2
Universidad de Guanajuato, Departamento de Estudios Culturales Demográficos y Políticos, División de Ciencias Sociales y Administrativas, México
CORRESPONDING AUTHOR
María Custodio   

Universidad Nacional del Centro del Perú, Centro de Investigación en Alta Montaña, Av. Mariscal Castilla N° 3989-4089, Huancayo, Perú
Publish date: 2019-08-01
 
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The spatial-temporal variability of the water quality of high Andean lagoons for tourist use was evaluated using multivariate statistical methods during 2017 and 2018. Water samples were collected from 14 sampling sites, with three replicates each. The water quality indicators determined were: pH, temperature, DO, COD, BOD5, P, N, Fe, Cu, Cr, Cd, Pb, Zn and chlorophyll-a. The flat cluster analysis (k R cluster) according to Ward's algorithm showed six significantly differentiated groups (α=0.01). While the real similarity profile (SIMPROF) moves markedly away from the obtained low permutation with a large excess of Euclidean similarity with a Pi value of 0.627. The PCA showed that the first two components recommended by the sedimentation analysis (Scree test) indicated 61.52% of the total variation of the observations. According to the Spearman range correlation selection criterion, the variables that best interpret the sample distributions are COD, DTS, P, Cd and Zn with a correlation of 0.893, the DTS being the most important variable with a correlation value of 0.795. The PERMANOVA analysis according to the flat cluster factor indicated that at least one of the groups is different from the others in relation to the levels of physical-chemical characteristics studied. Therefore, all the configured groups are statistically different, demonstrating that each lagoon of tourist use is different in relation to its physical-chemical indicators, according to the season in which it is found.