Laboratory of Biology Prof. E. Caviedes Codelia, Facultad de Ciencias Humanas, Universidad Nacional de San Luis, San Luis, Argentina.
Anal Chim Acta. 2011 Oct 31;705(1-2):243-52. doi: 10.1016/j.aca.2011.06.013. Epub 2011 Jun 15.
Temporal and spatial patterns of water quality of an important artificial water reservoir located in the semiarid Midwest of Argentina were investigated using chemometric techniques. Surface water samples were collected at 38 points of the water reservoir during eleven sampling campaigns between October 1998 and June 2000, covering the warm wet season and the cold dry season, and analyzed for dissolved oxygen (DO), conductivity, pH, ammonium, nitrate, nitrite, total dissolved solids (TDS), alkalinity, hardness, bicarbonate, chloride, sulfate, calcium, magnesium, fluoride, sodium, potassium, iron, aluminum, silica, phosphate, sulfide, arsenic, chromium, lead, cadmium, chemical oxygen demand (COD), biochemical oxygen demand (BOD), viable aerobic bacteria (VAB) and total coliform bacteria (TC). Concentrations of lead, ammonium, nitrite and coliforms were higher than the maximum allowable limits for drinking water in a large proportion of the water samples. To obtain a general representation of the spatial and temporal trends of the water quality parameters at the reservoir, the three-dimensional dataset (sampling sites×parameters×sampling campaigns) has been analyzed by matrix augmentation principal component analysis (MA-PCA) and N-way principal component analysis (N-PCA) using Tucker3 and PARAFAC (Parallel Factor Analysis) models. MA-PCA produced a component accounting for the general behavior of parameters associated with organic pollution. The Tucker3 models were not appropriate for modelling the water quality dataset. The two-factor PARAFAC model provided the best picture to understand the spatial and temporal variation of the water quality parameters of the reservoir. The first PARAFAC factor contains useful information regarding the relation of organic pollution with seasonality, whereas the second factor also encloses information concerning lead pollution. The most polluted areas in the reservoir and the polluting sources were identified by plotting PARAFAC loadings as a function of the UTM (Universal Transverse Mercator) coordinates.
利用化学计量学技术研究了位于阿根廷中西部半干旱地区的一个重要人工水库的水质时空分布。在 1998 年 10 月至 2000 年 6 月的 11 次采样期间，在水库的 38 个点采集了地表水样本，涵盖了温暖湿润的季节和寒冷干燥的季节，并对溶解氧（DO）、电导率、pH 值、铵、硝酸盐、亚硝酸盐、总溶解固体（TDS）、碱度、硬度、碳酸氢盐、氯、硫酸盐、钙、镁、氟化物、钠、钾、铁、铝、二氧化硅、磷酸盐、硫化物、砷、铬、铅、镉、化学需氧量（COD）、生化需氧量（BOD）、可培养需氧细菌（VAB）和总大肠菌群（TC）进行了分析。在很大一部分水样中，铅、铵、亚硝酸盐和大肠菌群的浓度高于饮用水的最大允许限值。为了获得水库水质参数的时空趋势的总体表示，使用 Tucker3 和 PARAFAC（并行因子分析）模型通过矩阵扩充主成分分析（MA-PCA）和 N 向主成分分析（N-PCA）对三维数据集（采样点×参数×采样时间）进行了分析。MA-PCA 生成了一个成分，该成分与与有机污染相关的参数的一般行为相关。Tucker3 模型不适用于水质数据集的建模。两因素 PARAFAC 模型提供了理解水库水质参数时空变化的最佳画面。第一个 PARAFAC 因子包含有关有机污染与季节性之间关系的有用信息，而第二个因子还包含有关铅污染的信息。通过将 PARAFAC 加载作为 UTM（通用横轴墨卡托）坐标的函数进行绘图，可以识别水库中污染最严重的区域和污染源。
