Assessment of surface water quality using multivariate statistical techniques: case study of the Nampong River and Songkhram River, Thailand

Abstract

Multivariate statistical techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA) were applied for the assessment of spatial and temporal variations of a large complex water quality data set of the Nampong River and Songkhram River, generated for more than 10 years (1996-2012) by monitoring of 16 parameters at different sites. According to the water quality characteristics, hierarchical CA grouped 13 sampling sites of the Nampong River into two clusters, i.e., upper stream (US) and lower stream (LS) sites, and five sampling sites of the Songkhram River into three clusters, i.e., upper stream (US), middle stream (MS) and lower stream (LS) sites. PCA/FA applied to the data sets thus obtained five latent factors explaining 69.80 and 69.32 % of the total variance in water quality data sets of LS and US areas, respectively, in the Nampong River and six latent factors explaining 80.80, 73.95, and 73.78 % of the total variance in water quality data sets of LS, MS, and US areas, respectively, in the Songkhram River. This study highlights the usefulness of multivariate statistical assessment of complex databases in the identification of pollution sources to better comprehend the spatial and temporal variations for effective river water quality management.

Quality characterization and pollution source identification of surface water using multivariate statistical techniques, Nalagarh Valley, Himachal Pradesh, India

“…Conversion of agricultural land to non-agricultural land, often illegally, for various industrial activities are a common scenario that has led to various environmental problems including pollution of the water resources (GoI 2012). Therefore, the fundamental understandings of hydro-morphological, chemical and biological characteristics are important aspect for virtual and long-term management of surface water (Muangthong and Shrestha 2015). An effective monitoring program is necessary that includes large and complex physicochemical parameters to draw meaningful information of surface water quality related to spatial and temporal variations (Shrestha and Kazama 2007;Guangjia et al 2010;.…”

Assessment of Near-Bottom Water Quality of Southwestern Coast of Sarawak, Borneo, Malaysia: A Multivariate Statistical Approach

“…A dendrogram was used to interpret the result of the cluster analysis. The cluster was statistically significant at a linkage distance < 60% and the number of clusters was decided by the practicality of the results [25]. The Spearman rank correlation was performed to determine the relationship among all the parameters.…”

“…Figure 5 illustrates that near-bottom water quality of the southwestern coast of Sarawak can be grouped into four significant clusters at a linkage distance < 60% [25]. Cluster 1 consisted of station 1 which was located near the river mouth of Batang Saribas while cluster 2 consisted of station 2 and station 3 which were located near Batang Lupar and Batang Sadong, respectively.…”

Spatial and temporal water quality dynamics of Awash River using multivariate statistical techniques

“…The PCs are orthogonal (non-correlated), linear combinations of the originally observed water quality data and are arranged in decreasing order of importance (Shrestha and Kazama, 2007;Singh et al, 2004). The PCs can be expressed as: (2) where Z is the component score, a is the component loading, x is the measured value of variable, i is the component number, j is the sample number and m is the total number of variables (Muangthong, 2015;Shrestha and Kazama, 2007).…”