A Comprehensive Evaluation and Assessment of Surface Water Quality Using Multivariate Techniques

Surface water pollution is now a major environmental concern on a worldwide scale. To maintain water quality at a specific desired level and to understand the current state of the water resource, surface water monitoring and assessment are crucial. This investigation relies on multivariate statistical techniques including cluster analysis, discriminant analysis, principal component analysis to assess the variations of surface water quality in the Mathura region. Water samples were collected from eleven different natural kunds, Mathura district. CA employed all water quality parameters to generate three clusters according to the similarity of their respective characteristics. A total of five components, explained 86.72 percent of the observed variance. PCA's utility for assessing and interpreting sizable, complex water quality data sets, and allocating water pollution sources/factors, Surface runoff, storm runoff, soil weathering, leaching, domestic discharge and agricultural runoff were identified as significant contributors to the deterioration of surface water quality. By employing Discriminant Analysis, five water quality parameters (TH, TDS, Na ⁺ , DO, and BOD ₅ ) were successfully identified during the spatial evaluation of natural kunds, with 100% assignment rate. Therefore, this study emphasizes relevance and efficacy of MSTs for accomplishing sustainable water resource management while recognizing the sources of water quality variations.