Temporal-scale spectral variability analysis of water quality parameters to realize seasonal behavior of a tropical river system (original) (raw)
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Journal of environmental biology / Academy of Environmental Biology, India, 2009
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Sains Malaysiana, 2021
This study investigates the seasonal and spatial water quality patterns along a tropical river that continuously receives various pollution sources. Multivariate analysis was used to study the spatial and temporal variations of the water quality parameters and to determine the origin of the pollution sources. Three regions (low, moderate, and high pollution levels) were determined based on cluster analysis. The stepwise DA mode proposed six parameters (pH, EC, COD, NO3, TC, and Fe) with 75% correct assignations as the most significant water quality parameters to present the spatial variations. In the temporal discrimination, forward stepwise mode analysis showed eight parameters (EC, TUR, BOD,COD, AN, NO3, Cu, and Cr) with 92% correct assignations, while five parameters (EC, AN, Al, Cu, and Cr) affording 89% correct assignations in backward stepwise mode analysis. Principal component analysis and factor analysis were used to investigate the origins of each water quality parameter ba...
Applied Water Science
An account of seasonal water quality variability has been taken as a proxy for the changes of environmental setting occurring in the catchment areas and helps to illustrate the ecological system processes associated with it. The present study in Meenachil River (L = 78 km, A = 1272 km 2) comprising of stations from upstream to downstream for pre monsoon (PRM), monsoon (MON) and post monsoon (POM). Ca 2? and SO 4 2show an erratic trend while extreme deviations were observed at S6 and S7 stations. Na ? , K ? , Cl-, DIC and DOC showed a similar trend in most stations, i.e. (PRM [ POM [ MON). Significant rise of DIC and DOC at S7 during POM and PRM could explicate changes ensued in adjacent Vembanad lake system. Strong correlations of DIC and DOC for Na ? , K ? and Clions were noted in the study. HCA dendrogram reveals that ion chemistry in S6 and S7 was strictly controlled by neighbouring lake water dynamics. The results demonstrate high F1 variance of 73, 68 and 72% followed by F2 comprising of 17, 19 and 21% for PRM, MON and POM, respectively. General understanding into the autochthonous process associated within the river lake interface region was evident from the nutrient variability scenario.
Journal of Geoscience and Environment Protection, 2017
Surface water has become one of the most vulnerable resources on the earth due to deterioration of its quality from diverse sources of pollution. Understanding of the spatiotemporal distribution of pollutants and identification of the sources in the river systems is a prerequisite for the protection and sustainable utilization of the water resources. Multivariate statistical techniques such as Principal Component Analysis (PCA) and Factor Analysis (FA) were applied in this study to investigate the temporal and spatial variations of water quality and appoint the major factors of pollution in the Shailmari River system. Water quality data for 14 physicochemical parameters from 11 monitoring sites over the year of 2014 in three sampling seasons were collected and analyzed for this study. Kruskal-Wallis test showed significant (p < 0.01) temporal and spatial variations in all of the water quality parameters of the river water. Principal component analysis (PCA) allowed extracting the contributing parameters affecting the seasonal water quality in the river system. Scatter plots of the PCs showed the tidal and spatial variation within river system and identified parameters controlling the behavior in each case. Factor analysis (FA) further reduced the data and extracted factors which are significantly responsible for water quality variation in the river. The results indicate that the parameters controlling the water quality in different seasons are related with salinity, anthropogenic pollution (sewage disposal, effluents) and agricultural runoff in pre-monsoon; precipitation induced surface runoff in monsoon; and erosion, oxidation or organic pollution
Spatial-temporal analysis of the surface water quality of the Pará River Basin through statistical techniques, 2019
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The assessment and prediction of temporal variations in surface water quality—a case study
Environmental Monitoring and Assessment, 2018
In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.
Water research, 1998
AbstractÐ22 Physico-chemical variables have been analyzed in water samples collected every three months for two and a half years from three sampling stations located along a section of 25 km of a river aected by man-made and seasonal in¯uences. Exploratory analysis of experimental data have been carried out by box plots, ANOVA, display methods (principal component analysis) and unsupervised pattern recognition (cluster analysis) in an attempt to discriminate sources of variation of water quality. PCA has allowed the identi®cation of a reduced number of``latent'' factors with a hydrochemical meaning: mineral contents, man-made pollution and water temperature. Spatial (pollution from anthropogenic origin) and temporal (seasonal and climatic) sources of variation aecting quality and hydrochemistry of river water have been dierentiated and assigned to polluting sources. An ANOVA of the rotated principal components has demonstrated that (i) mineral contents are seasonal and climate dependent, thus pointing to a natural origin for this polluting form and (ii) pollution by organic matter and nutrients originates from anthropogenic sources, mainly as municipal wastewater. The application of PCA and cluster analysis has achieved a meaningful classi®cation of river water samples based on seasonal and spatial criteria. #
Exploratory spectral analysis of hydrological times series
Stochastic Hydrology and Hydraulics, 1995
Current methods of estimation of the univariate spectral density are reviewed and some improvements are suggested. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements rather than competes with the popular ARIMA model building approach. A new diagnostic check for ARMA model adequacy based on the nonparametric spectral density is introduced. Two new algorithms for fast computation of the autoregressive spectral density function are presented. A new style of plotting the spectral density function is suggested. Exploratory spectral analysis of a number of hydrological time series is performed and some interesting periodicities are suggested for further investigation. The application of spectral analysis to determine the possible existence of long memory in riverflow time series is discussed with long riverflow, treering and mud varve series. A comparison of the estimated spectral densities suggests the ARMA models fitted previously to these datasets adequately describe the low frequency component. The software and data used in this paper are available by anonymous ftp from fisher.stats.uwo.ca in the directory pub\mhts.