Evaluation of the water quality of the Pardo River (MG) based on physical, chemical and microbiological parameters

• Autor(a) principal: Collares, Maria Fernanda Antunes
• Data de Publicação: 2021
• Outros Autores: Silva, Leonardo França da, Barbosa, Rubens Barrichello Gomes, Dourado, Ana Carolina Chaves, Rezende, Bruna Nogueira, Nascimento, João Antônio Costa do
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/15532
• topic: Hydrographic basin; Multivariate statistics; Monitoring network; Cluster analysis.; Red de monitoreo; Cuenca hidrográfica; Estadística multivariante; Análisis de conglomerados.; Rede de monitoramento; Bacia hidrográfica; Estatística multivariada; Análise de cluster.

This article presents an analysis of the monitoring of the water quality index and the use of multivariate statistical techniques in the mining portion of the Pardo River, in order to select the most significant parameters in the current aspects of water quality, grouping the stations according to the similarity of the studied parameters. The data used in the study were obtained from the Minas Gerais Water Management Institute - IGAM for the months of January to October of the year 2018. The water quality index was calculated for the 5 monitoring points and classified according to the IQA -NSF. Principal component analysis (PCA) and Cluster analysis (CA) were used to reduce the number of variables and to group stations with similar characteristics, respectively. The PD005 station presented the lowest average of the water quality index, this is due to the fact that the parameter of faecal coliforms stands out negatively in great quantity in the station. Using the PCA, two main components were selected as indicators of water quality explaining the cumulative variance of 78%. The CA grouped the stations into three groups, being able to identify the most polluted and the least polluted stations. The results obtained through multivariate statistics have proved to be important for understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters at all monitoring stations requires greater availability of financial resources.