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Spatial and seasonal variation of organic pollutants in surface water using multivariate statistical techniques

机译:利用多元统计技术研究地表水中有机污染物的时空变化

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In this study, analysis of variance (ANOVA), cluster analysis (CA) and principal component analysis (PCA) were employed in order to evaluate the concentration profile of organic contaminants found in three main river from central Transylvania, Romania. Samples were collected from nine sampling stations, in two different sampling campaigns (wet season and dry season). Water samples were extracted using solid-phase extraction and analyzed using gas chromatography coupled with mass spectrometry (GC/MS). Twelve organic pollutants belonging to different classes were used for further interpretations. ANOVA highlighted compounds which distinguished Olt River from Mures River, and compounds that are influenced by increased river flow from the wet season. CA was applied to group the sampling stations. Three clusters were obtained, according to their organic load. PCA extracted five principal components explaining 87.330% from data set variability. Based on these results, a future monitoring study may be optimized by reducing the sampling points and compounds to those that are representative for each river, thereby reducing costs, without any information loss.
机译:在这项研究中,采用方差分析(ANOVA),聚类分析(CA)和主成分分析(PCA)来评估罗马尼亚特兰西瓦尼亚中部三个主要河流中发现的有机污染物的浓度分布。在两个不同的采样活动(湿季和旱季)中,从九个采样站采集了样本。使用固相萃取法提取水样品,并使用气相色谱法和质谱分析法(GC / MS)进行分析。属于不同类别的十二种有机污染物被用于进一步解释。方差分析突出显示了区分奥尔特河和穆雷斯河的化合物,以及受雨季河流流量增加影响的化合物。应用CA对采样站进行分组。根据其有机负荷获得了三个簇。 PCA从数据集的可变性中提取了五个主要成分,解释了87.330%。根据这些结果,可以通过将采样点和化合物减少到代表每条河流的采样点和化合物来优化未来的监测研究,从而降低成本,而不会造成任何信息损失。

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