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Zheng, L.-Y., Yu, H.-B. and Wang, Q.-S. (2016) Application of Multivariate Statistical Techniques in Assessment of Surface Water Quality in Second Songhua River Basin, China. Journal of Central South University, 23, 1040-1051.
https://doi.org/10.1007/s11771-016-0353-z

has been cited by the following article:

  • TITLE: Emerging Water Quality Issues along Rio de la Sabana, Mexico

    AUTHORS: Daniel Pineda-Mora, Jeiry Toribio-Jiménez, Teresa Leal-Ascencio Ma, Ana Laura Juárez-López, Justiniano González-González, Jesús Carlos Ruvalcaba-Ledezma, Ramón Alberto Batista-García, Paul Alexander Arp

    KEYWORDS: Rio de la Sabana, Urban Developments, Water Pollution, Sampling Locations, Dry/Rainy Season, Multivariate Analyses

    JOURNAL NAME: Journal of Water Resource and Protection, Vol.10 No.7, July 4, 2018

    ABSTRACT: The basin of Rio de la Sabana is the largest tributary of the Tres Palos coastal lagoon in Southwest Mexico, east of Acapulco. This lagoon and its upstream basin areas have become a high priority area for the preservation of coastal and marine environments. To obtain information about water quality as affected by urban expansion since 2002, fourteen physicochemical parameters (temperature, pH, electrical conductivity, dissolved oxygen, ammonium, nitrate, nitrite, sulphate, phosphate), biochemical (biological and chemical oxygen demand, methylene blue active substances) and bacteriological parameters (total and fecal coliforms) were determined. This sampling was done for dry and rainy season conditions at seven locations (S1, S2, S3, …, S7) along the river, spaced 3 to 6 km apart to a total of 30.4 km. The results were grouped into four zones: (Z1) reference, (Z2) transition, (Z3) polluted, (Z4) recovery. The Alborada (S5) and Tunzingo (S6) sites, adjacent to dense high-class residential areas (Z3), had the greatest pollution charges in both seasons, while the La Poza (S7) site near the Tres Palos lagoon (Z4) showed a decrease in pollution. All parameters correlated with increasing head- to down-river sampling distance by following linear (pH, DO) or curvilinear patterns (all other parameters). Using sampling location and dry versus rainy sampling season as multivariate regression (predictor) variables led to least-squares capturing: 1) 66% to 95% of the T(°C), pH, DO, and PO3-4 variations, and 2) 57% to 96% of the log-linear variations of the other parameters. Among the parameters, T(°C), DO, and PO3-4 were not significantly affected by sampling season, while pH became so after deleting two higher than usual pH values at the S5 and S6 locations during the dry season.