Universidade Federal de Viçosa, Viçosa, MG, 36570-900, Brazil.
Universidade Federal de Pelotas, Pelotas, RS, 96010-020, Brazil.
Environ Sci Pollut Res Int. 2020 Oct;27(28):35303-35318. doi: 10.1007/s11356-020-09783-0. Epub 2020 Jun 26.
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米纳斯吉拉斯州多斯雷斯流域的水质数据，以通过识别网络中需要保留的主要变量、其可能的污染源以及最佳采样频率来分析当前的监测网络。为此，使用了多元统计技术（因子分析/主成分分析、FA/PCA 和聚类分析、CA），并辅以框架类别的违规分析。对 2010 年至 2017 年的基础期进行了 64 个监测站点的水质变量分析。根据不同的监测活动分析了水质变量：(a) 部分活动；(b) 总活动；和 (c) 每月活动。从 FA/PCA 的结果中可以看出，当分析部分活动数据时，所选变量代表了该流域对侵蚀的高敏感性以及其水体中生活污水的排放。当评估总活动数据时，包含了工业和采矿活动中重金属污染的代表性变量。因此，违反框架类别的分析使得能够识别出五个关键变量：耐热大肠菌群、溶解铁、总磷和总锰，这加强了 FA/PCA 中的结果。基于分析结果，建议在部分活动中包含与重金属污染相关的变量，优先考虑溶解铁和总锰，以及仅在总活动中采样总氯。对每月活动的数据进行 CA 分析表明，尽管季度监测频率令人满意，但每月监测更适合多斯雷斯流域米纳斯吉拉斯州部分地区的水质监测。
