The objective of the present study was to evaluate the water quality data in the Minas Gerais portion of the Doce River basin in order to analyze the current monitoring network by identifying the main variables to be maintained in the network, their possible sources of pollution, and the best sampling frequency. Multivariate statistical techniques (factor analysis/principal components analysis, FA/PCA and cluster analysis, CA) complemented by the analysis of violation of the framing classes were used for this purpose. Water quality variables common to 64 monitoring sites were analyzed for the base period from 2010 to 2017. The water quality variables were analyzed considering the different monitoring campaigns: (a) partial campaigns; (b) total campaigns; and (c) monthly campaigns. It was identified from the FA/PCA results, that, when the partial campaign data were analyzed, the variables selected represent the high susceptibility that the basin presents to erosion and the release of domestic effluents in its water bodies. When the data of total campaigns were evaluated, representative variables of the contamination by heavy metals from industrial and mining activities were included. Therefore, the analysis of violation of the framing classes made possible to identify five critical variables: thermotolerant coliforms, dissolved iron, total phosphorus, and total manganese, which reinforced the results obtained in FA/PCA. Based on the results of the analyses, it was recommended to include variables associated with heavy metal contamination in the partial campaigns, prioritizing the dissolved iron and total manganese, as well as total chloride sampling only for the total campaigns. The evaluated data from the monthly campaigns, the CA showed that although the quarterly monitoring frequency is satisfactory, the monthly monitoring is more appropriate for the monitoring of water quality in the Minas Gerais portion of the Doce River basin.

本研究的目的是评估 Doce 河流域米纳斯吉拉斯州部分的水质数据，以便通过确定网络中要维护的主要变量、它们可能的污染源和最佳采样频率。为此目的使用了多变量统计技术（因子分析/主成分分析，FA/PCA 和聚类分析，CA），并辅以框架类违规分析。分析了 2010 年至 2017 年基期 64 个监测点共有的水质变量。分析了不同监测活动的水质变量：(a) 部分活动；(b) 活动总数；(c) 每月活动。从 FA/PCA 结果可以看出，在分析部分活动数据时，选定的变量代表该流域对侵蚀和向其水体排放生活污水的高度敏感性。在评估总活动的数据时，包括工业和采矿活动中重金属污染的代表性变量。因此，对违反框架类别的分析可以确定五个关键变量：耐热大肠菌群、溶解铁、总磷和总锰，这加强了在 FA/PCA 中获得的结果。根据分析结果，建议在部分活动中包括与重金属污染相关的变量，优先考虑溶解铁和总锰，以及仅针对总活动的总氯化物采样。CA 从月度活动评估数据显示，虽然季度监测频率令人满意，但月度监测更适合多切河流域米纳斯吉拉斯州部分的水质监测。