Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Abstract

ABSTRACT:In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.

Assessment of Water Quality and Identification of Pollution Risk Locations in Tiaoxi River (Taihu Watershed), China

“…The CA enabled us to categorize sampling locations based on water quality, so that in future studies, the number of sampling locations can be minimized for cost-effective monitoring of water quality in Tiaoxi River by choosing a few locations from each cluster based on the distance distribution and pollution levels in those locations. Previous studies have reported that a similar strategy has been successfully applied in water quality monitoring programs elsewhere [12,13,61,62], and the Tiaoxi River Taihu catchment is therefore similarly amenable to this rational approach. the number of sampling locations can be minimized for cost-effective monitoring of water quality in Tiaoxi River by choosing a few locations from each cluster based on the distance distribution and pollution levels in those locations.…”

Environmental Assessment of Water Quality in Kuwait Bay

“…When the eigenvalue of a principal component is equal to, or greater than, 1, the result of the principal component analysis is considered significant [24], [25]. To minimize the variations among the variables for each factor, the factor axes were varimax-rotated.…”

Statistical properties of the Seasonal Fractionally Integrated Separable Spatial Autoregressive Model

“…Portmann et al (2009) studied the spatial and seasonal patterns for climate change, temperatures and precipitations. Nobre et al (2011) introduce an spatially varying Autoregressive Processes for satellite data on sea surface temperature for the North Pacific to illustrate how the model can be used to separate trends, cycles, and short-term variability for high-frequency environmental data; a multivariate GSTAR has been developed by Pejman et al (2009) for the study of the water quality.…”