ArticleEnvironmental monitoring and assessment2018
Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin, Brazil.
Article in Environmental monitoring and assessment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil.Environmental science and pollution research international · 2024Article
- Performance of sewage treatment plants and impact of effluent discharge on receiving water quality within an urbanized area.Environmental monitoring and assessment · 2021Article
- Evaluation of the Swat River, Northern Pakistan, water quality using multivariate statistical techniques and water quality index (WQI) model.Environmental science and pollution research international · 2020Article
- Use of multivariate statistical methods to analyze the monitoring of surface water quality in the Doce River basin, Minas Gerais, Brazil.Environmental science and pollution research international · 2020Article
- Methodological proposal for the allocation of water quality monitoring stations using strategic decision analysis.Environmental monitoring and assessment · 2019Article
- Assessment of spatial variations in the surface water quality of the Velhas River Basin, Brazil, using multivariate statistical analysis and nonparametric statistics.Environmental monitoring and assessment · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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