Fisheries Resource Management, Central Institute of Fisheries Education, Indian Council of Agriculture Research, Panch Marg, Off-yari Road, Versova, Mumbai, 400 061, India.
Centre of Studies in Resources Engineering, Indian Institute of Technology (IIT), Bombay, Powai, Mumbai, 400 076, India.
Environ Sci Pollut Res Int. 2021 Jun;28(24):31013-31031. doi: 10.1007/s11356-020-12001-6. Epub 2021 Feb 17.
With the use of different multivariate statistical analysis methods, spatio-temporal fluctuations in the water parameters of Tiru reservoir located at the Marathwada drought-prone area of Maharashtra, India, have been analysed and reported in this case study. Tiru reservoir, situated on the tributary of the Godavari River, was regularly monitored at five different sites from August 2017 to January 2019 for the estimation of 20 water quality parameters. Various multivariate methods such as pattern reorganisation using cluster analysis (CA), factor analysis/principal component analysis (FA/PCA), and discriminant analysis (DA) were used for handling complex datasets. CA extracted three different clusters from five sampling sites with similar water quality characteristics. FA/PCA extracted thirteen factors (65% of 20 measured) required to explain 74% of the data variability and identified the factors accountable for variation in water quality and also evaluated the prevalence of each cluster on the overall dissimilarity at five different sampling sites. Discriminant analysis extracted a total of 16 parameters with 97.7% right assignations. Varifactors (VFs) acquired by factor analysis recommended that the water quality parameters accounted for variation were linked to two groups. The first group included water quality parameters like T, DO, SDD, turbidity, TDS, PA, and MA, whereas the second group covered most of the nutrients Cl, silicates, PP, TP, NO-N, NO-N, and NH-N; hardness; and CHL-a and mainly entered the reservoir during surface runoff from agriculture fields and the surrounding area containing domestic as well as animal waste. Thus, the present work showed the efficiency of multivariate methods for the assessment of spatial as well as a temporal variation in the water quality of a small reservoir.
本案例研究分析并报告了位于印度马哈拉施特拉邦马哈拉施特拉邦干旱地区的蒂鲁水库的水参数的时空波动情况，使用了不同的多元统计分析方法。蒂鲁水库位于戈达瓦里河的支流上，2017 年 8 月至 2019 年 1 月，在五个不同地点对 20 个水质参数进行了定期监测。使用聚类分析（CA）、因子分析/主成分分析（FA/PCA）和判别分析（DA）等各种多元方法来处理复杂数据集。CA 从五个采样点中提取了三个具有相似水质特征的不同聚类。FA/PCA 提取了 13 个因子（20 个测量因子的 65%），这些因子需要解释 74%的数据变异性，并确定了导致水质变化的因子，还评估了五个不同采样点上每个聚类对整体差异的普遍性。判别分析共提取了 16 个参数，正确分配率为 97.7%。因子分析获得的变因子（VF）表明，水质参数的变化与两组有关。第一组包括 T、DO、SDD、浊度、TDS、PA 和 MA 等水质参数，而第二组涵盖了大部分营养物 Cl、硅酸盐、PP、TP、NO-N、NO-N 和 NH-N；硬度；以及 CHL-a，主要是在农业用地和周围地区的地表径流期间进入水库，这些地区含有生活污水和动物粪便。因此，本工作表明多元方法在评估小型水库水质的时空变化方面的效率。
