Spatial and Temporal Assessment of Water Quality data Using Multivariate Statistical Techniques


  • Nícolas Reinaldo Finkler Universidade de Caxias do Sul
  • Taison Anderson Bortolin Universidade de Caxias do Sul
  • Jardel Cocconi Universidade de Caxias do Sul
  • Ludmilson Abritta Mendes Universidade Federal de Sergipe
  • Vania Elisabete Schneider Universidade de Caxias do Sul



Principal Component Analysis (PCA). Cluster Analysis (CA). Urban Hydrographic Basin.


The natural factors and anthropogenic activities that contribute to spatial and temporal variation in superficial waters in Caxias do Sul’s urban hydrographic basins were determined applying multivariate analysis of data. The techniques used in this study were Principal Component Analysis and Cluster Analysis. The monitoring was executed in 12 sampling stations, during January, 2009 to January, 2010 with monthly periodicity in total of 13 campaigns. Between chemical, biological and physical, 20 parameters were analyzed. The results state that with the use of ACP, a data variance of 70.94% was observed. Therefore, it testifies that major pollutants that contribute to a water quality variation in the county are classified as domestic and industrial pollutants, mainly from galvanic industry. Moreover, two clusters were found which differentiated regarding their location and distance from areas with a high human density, corroborating on identifying of impact due to human activities in urban rivers.


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How to Cite

Finkler, N. R., Bortolin, T. A., Cocconi, J., Mendes, L. A., & Schneider, V. E. (2016). Spatial and Temporal Assessment of Water Quality data Using Multivariate Statistical Techniques. Ciência E Natura, 38(2), 577–587.




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