Achieng' A O, Raburu P O, Kipkorir E C, Ngodhe S O, Obiero K O, Ani-Sabwa J
School of Natural Resource Management, Department of Fisheries and Aquatic Sciences, University of Eldoret, P.O. Box 1125, Eldoret, Kenya.
School of Engineering, Department of Civil and Structural Engineering, Moi University, P.O. Box 3900, Eldoret, Kenya.
Environ Monit Assess. 2017 Jun;189(6):280. doi: 10.1007/s10661-017-5992-5. Epub 2017 May 22.
Multivariate techniques can infer intrinsic characteristics of complex data by generating correlation, similarity, dissimilarity, and covariance vector matrix to ascertain their relationships. The study evaluated the effect of anthropogenic activities by analyzing selected physicochemical water quality parameters (WQP) as indicators of pollution in River Sosiani, located in western Kenya, at six stations from August 2012 to February 2013 (Aug-Oct ≡ wet season, Nov-Feb ≡ Dry season). Temperature, pH, Total Dissolved Solids (TDS), conductivity, and Dissolved Oxygen (DO) were measured in situ while Total Phosphorus (TP), Total Organic Nitrogen (TON), and Biologial Oxygen Demand (BOD) were measured in vitro using standard methods. Except for DO and pH, the other variables were increasing in concentration downstream. Cluster analysis grouped stations with municipal discharge, to be the most distant linked to other stations in both seasons. Multidimensional scaling had four categories of stations with similar WQP: before, after, and wet and dry seasons. Principal component analysis with (60.5 and 26.1% for components 1 and 2) evaluated TON and TP as key pollutants in both seasons. Factor analysis with varifactor two at 35.3 and 27.1% variance in wet and dry seasons, respectively, had strong absolute factor loading of BOD (wet 0.878, dry 0.915) and TP (wet 0.839, dry 0.709) inferring sites with organic pollution also had nutrient pollution. Assessment of pollution with the selected WQP identified two major effects: nutrient and organic. Additional variables may identify other pollutants along the river. Multiple pollution effects, changing environment, and intrinsic characteristics of aquatic ecosystems generate complex data which are better assessed with multivariate techniques.
多元技术可以通过生成相关性、相似性、相异性和协方差向量矩阵来推断复杂数据的内在特征，以确定它们之间的关系。该研究通过分析选定的理化水质参数（WQP）来评估人为活动的影响，这些参数作为肯尼亚西部索西亚尼河污染的指标，于2012年8月至2013年2月在六个站点进行监测（8月至10月≡雨季，11月至2月≡旱季）。现场测量了温度、pH值、总溶解固体（TDS）、电导率和溶解氧（DO），而总磷（TP）、总有机氮（TON）和生物需氧量（BOD）则采用标准方法在体外进行测量。除了DO和pH值外，其他变量在下游的浓度都在增加。聚类分析将有城市污水排放的站点归为一类，在两个季节中，这类站点与其他站点的联系最为疏远。多维标度分析有四类具有相似WQP的站点：上游、下游以及雨季和旱季的站点。主成分分析（第一和第二成分分别为60.5%和26.1%）评估了TON和TP在两个季节中都是关键污染物。因子分析中，因子二在雨季和旱季的方差分别为35.3%和27.1%，BOD（雨季0.878，旱季0.915）和TP（雨季0.839，旱季0.709）具有很强的绝对因子载荷，这表明存在有机污染的站点也存在营养物污染。用选定的WQP评估污染确定了两个主要影响：营养物和有机物。其他变量可能会识别出河流沿线的其他污染物。多种污染影响、不断变化的环境以及水生生态系统的内在特征产生了复杂的数据，使用多元技术能更好地对这些数据进行评估。
