Department of Agricultural Engineering, Universidade Federal de Viçosa - UFV, Viçosa, MG, Brazil.
Department of Soil, Universidade Federal de Viçosa - UFV, Viçosa, MG, Brazil.
Environ Monit Assess. 2021 Feb 15;193(3):125. doi: 10.1007/s10661-021-08918-1.
This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in 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 in all monitoring stations require greater availability of financial resources.
本研究采用多元统计技术对巴西主要河流流域之一——多西河（Doce River）流域进行了研究，旨在选择和评估当前水质方面最具代表性的参数，并根据所选参数的相似性对各监测站进行分组，分别考虑旱季和雨季。本研究使用了来自米纳斯吉拉斯州水资源管理研究所（Minas Gerais Water Management Institute）网络的 63 个定性监测站的数据，考虑了 2017/2018 水文年的 38 个参数。主成分分析（PCA）和层次聚类分析（HCA）分别用于减少变量的总数并对具有相似特征的监测站进行分组。通过 PCA，选择了四个主成分作为水质指标，在雨季解释了总方差的 68%，在旱季解释了总方差的 65%。HCA 在雨季将监测站分为四个组，在旱季分为三个组，表明季节性对监测站分组的影响。此外，HCA 还能够区分流域上游、主河道和城市中心附近的水质监测站。多元统计分析的结果证明，对于了解流域当前的水质状况非常重要，并且可以用于改进水资源管理，因为在所有监测站收集和分析所有参数都需要更多的财政资源。
