Singh Kunwar P, Malik Amrita, Mohan Dinesh, Sinha Sarita
Environmental Chemistry Section, Industrial Toxicology Research Center, P.O. Box 80, MG Marg, Lucknow 226 001, India.
Water Res. 2004 Nov;38(18):3980-92. doi: 10.1016/j.watres.2004.06.011.
This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.
本案例研究报告了不同的多元统计技术，这些技术用于评估印度北部戈姆蒂河监测期间获得的大型复杂水质数据集的时空变化并进行解读。恒河的主要支流戈姆蒂河的水质，在相对低污染、中等污染和高污染区域选择的八个不同地点进行了监测，在5年期间（1994 - 1998年）定期监测24个参数。对复杂的数据矩阵（17790条观测数据）运用了不同的多元技术，如聚类分析、因子分析/主成分分析（FA/PCA）和判别分析（DA）。聚类分析（CA）显示出良好的结果，使采样点之间呈现出三组不同的相似性，反映了河流系统不同的水质参数。FA/PCA识别出六个因子，这些因子决定了数据结构，解释了数据集总方差的71%，并能根据共同特征对所选参数进行分组，同时评估每组对水质总体变化的影响程度。然而，并未能实现显著的数据简化，因为需要14个参数才能解释水质时空变化的71%。判别分析在时空分析的数据简化和模式识别方面显示出最佳结果。判别分析显示，在时间分析中，五个参数（pH值、温度、电导率、总碱度和镁）的正确分类率超过88%，而在流域三个不同区域的空间分析中，九个参数（pH值、温度、碱度、钙硬度、溶解氧、生化需氧量、氯化物、硫酸盐和总凯氏氮）的正确分类率为91%。因此，判别分析能够降低大型数据集的维度，确定一些导致水质大幅变化的指示参数。本研究展示了多元统计技术在评估和解读大型复杂数据集方面的必要性和实用性，以便更好地了解水质情况并设计监测网络，从而实现水资源的有效管理。
