Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

Abstract

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.

Application of Multivariate Statistical Methods for Water Quality Assessment of Karasu-Sarmisakli Creeks and Kizilirmak River in Kayseri, Turkey

“…They reported that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the most important parameters in assessing variations of water quality in the river. HCA, DA, and PFA were applied for the assessment of seasonal variations in the surface water quality of tropical pastures in Kuala Lumpur [17]. The study was conducted with a dataset consisting of one-year monitoring of 14 parameters at six sampling sites.…”

Spatial and Temporal Variation of Parameters in Wadi Andlou, Tunisia‐Pollution by Pulp Mill Discharge

“…This is different from the cluster obtained in the dry season, where S3 was grouped along with S4 in cluster 2. It is evident that the season affects the water quality of this stream . This may be due to the flow of runoff from S3 during rainfall events common in the rainy season.…”

“…In addition, CA may reduce the number of samples, so only representative sites from each cluster are used (not all sampling stations). This can reduce the cost of the analysis . CA was useful for giving a first exploratory indication of the difference between the studied sites and also a second exploratory indication of the difference between the dry and rainy seasons for the polluted area in the vicinity of paper industry .…”

Spatial Variability and Associated Risk Assessment of Nitrate and Ammonium Concentration in Hail Haor Wetland, Northeastern Bangladesh

“…Hierarchical cluster analysis was carried out to classify the wetland according to their nutrient status and geospatial location seasonally. Ward's method of linkage was used, with the squared Euclidean distance, as a measure of similarity, to determine the distance between clusters [24].…”