Proposição de índice de qualidade de água subterrânea (IQAsub) para aplicação em áreas com potencial minerário

Abstract

In some regions of the world, groundwater represents the main alternative supply. However, this resource has a direct link with local geology, and the dissolution of elements can make it naturally unsuitable for its intended uses - human consumption, irrigation and animal watering, for example. Therefore, it is necessary to control and monitor these waters in order to guarantee the standards recommended by current legislation, just as it is essential to pass on information about the quality of the water offered to the population for the transparency and reliability of the process. Most of the research carried out on water quality indices focused on surface waters, while studies on groundwater were little explored. In this context, the current research proposed the development of a Groundwater Quality Index (GWQI) for application in areas with mining potential, based on data from the groundwater monitoring network of the “Instituto Mineiro de Gestão das Águas” (IGAM) between the years 2018 and 2022. Secondly, it is intended to classify such points using the proposed index, as well as compare the results obtained from the GWQI with the results resulting from the application of the CCME WQI developed by the Canadian Council of Ministers of the Environment, which allows flexibility in the selection of parameters, matrices and water quality references. Additionally, cluster analysis and principal component analysis (PCA) were performed. The development of GWQI considered 10 parameters – arsenic, lead, mercury, nitrate, uranium, hardness, iron, manganese and zinc – which may arise from the dissolution of components of local geology. For each of these parameters, curves were constructed, weights were assigned and the wells were classified by GWQI and CCME WQI. Furthermore, to apply cluster analysis and PCA, the Rstudio software was used, with k-means being the clustering method chosen. Considering a database with 795 records of analyzes carried out in the Guarani, Norte de Minas, PANM and Velhas Networks, it was observed that the results obtained with the application of GWQI were mostly more conservative than those obtained with the application of CCME WQI, a fact associated with to the weighting of parameters in GWQI. The cluster analysis resulted in three clusters: cluster 1 – terrible GWQI quality, cluster 2 – regular GWQI quality and cluster 3 – terrible GWQI quality. Finally, the PCA considered five main components that were able to explain 79.9% of the total variance.