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Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin, Brazil

  • Giovanna Moura Calazans
  • Carolina Cristiane Pinto
  • Elizângela Pinheiro da Costa
  • Anna Flávia Perini
  • Sílvia Corrêa OliveiraEmail author
Article

Abstract

Surface water quality monitoring networks are usually deployed and rarely re-evaluated with regard to their effectiveness. In this sense, this work sought to evaluate and to guide optimization projects for the water quality monitoring network of the Velhas river basin, using multivariate statistical methods. The cluster, principal components, and factorial analyses, associated with non-parametric tests and the analysis of violation to the standards set recommended by legislation, identified the most relevant water quality parameters and monitoring sites, and evaluated the sampling frequency. Thermotolerant coliforms, total arsenic, and total phosphorus were considered the most relevant parameters for characterization of water quality in the river basin. The monitoring sites BV156, BV141, BV142, BV150, BV137, and BV153 were considered priorities for maintenance of the network. The multivariate statistical analysis showed the importance of a monthly sampling frequency, specifically the parameters considered most important.

Keywords

Cluster analysis Principal component analysis Factorial analysis Network monitoring assessment Brazilian watershed 

Notes

Acknowledgments

We would like to thank the Institute of Water Management of Minas Gerais (Igam) and its technical team for providing the monitoring data and for the constant support and service.

Funding information

This study received financial supports from the National Counsel of Technological and Scientific Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES), and the Foundation of Support Research of the State of Minas Gerais (FAPEMIG).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Universidade Federal de Minas Gerais - Escola de Engenharia - Campus PampulhaBelo HorizonteBrazil

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