Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: A case study on Jajrood River (Tehran, Iran)

Razmkhah, Homa; Ali, Ahmad; Torkian, Ayoob

doi:10.1016/j.jenvman.2009.11.001

Cited by 198 publications

(108 citation statements)

References 36 publications

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“…Each parameter contributes towards its respective PC and its contribution is determined by the loading values. PCA has been used in many water quality studies, such as 1) determining spatio-temporal changes in the water quality of Jajrood River [8]; 2) comparing water quality of regional sites of Canada for spatial and temporal changes [9]; 3) seasonal and spatial variations for surface water quality of Mid-Black Sea Coast in Turkey [10]; and 4) impact of agricultural activities for Nathan Creek Watershed, British Columbia, Canada [11].…”

Section: Introductionmentioning

confidence: 99%

Clusterization of Surface Water Quality and Its Relation to Climate and Land Use/Cover

Akbar¹,

Hassan²,

Achari³

2013

JEP

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The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 major rivers of Alberta on the basis of 17 parameters during the period of five years (i.e., 2004-2008) using principal component analysis (PCA), total exceedance model and clustering technique. Seven major principal components (PCs) with variability of about 89% were identified. These PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover. The seven dominant parameters revealed from the seven PCs were total dissolved solids (TDS), true color (TC), pH, iron (Fe), fecal coliform (FC), dissolved oxygen (DO), and turbidity (TUR). The normalized data of dominant parameters were used to develop a model for obtaining total exceedance. The exceedance values acquired from the total exceedance model were used to determine the patterns for the development of five clusters. The performance of the clusters was compared with the classes obtained in Canadian Water Quality Index (CWQI). Cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5 showed agreements of 85.71%, 83.54%, 90.22%, 80.74%, and 83.40% with their respective CWQI classes on the basis of the data for all rivers during [2004][2005][2006][2007][2008]. The water quality was deteriorated in growing season due to snow melting. This methodology could be applied to classify the raw surface water quality, analyze the spatio-temporal trends and study the impacts of the factors affecting the water quality anywhere in the world.

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Section: Introductionmentioning

confidence: 99%

Clusterization of Surface Water Quality and Its Relation to Climate and Land Use/Cover

Akbar¹,

Hassan²,

Achari³

2013

JEP

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“…(3) and Table ( (5), which summarizes the PCA, is including the loadings and eigenvalues of each PC, explaining the total variance and the cumulative variance. It must be noted that the rotation, which resulted in an increase in the number of factors, is necessary to explain the same amount of variance in the original dataset [6].…”

Section: B Application Of Cluster Analysismentioning

confidence: 99%

“…Due to these complexities, water quality specialists and decision-makers often are confronted with significant challenges in their efforts to control water pollution [3,4]. Through identifying spatial and temporal patterns in the river water quality, an improved understanding of the environmental conditions may help the stakeholder and decision-maker to establish priorities for sustainable of water management [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Assessment of Al-Gharraf River, South of Iraq Using Multivariate Statistical Techniques

Ewaid¹,

Abed²

2017

JNUS

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The multivariate statistical methods (principal component analysis "PCA" and hierarchical cluster analysis "CA"), were used to evaluate the water quality of Al-Gharraf River, the largest branch of Tigris River, south of Iraq. Water quality parameters were calculated monthly at each of the ten stations along the river during the year 2014-2015, to investigate the potential pollution sources and samples were analyzed for 18 physicochemical and biological parameters. The PCA supported in extracting and recognizing the factors that are responsible for river water quality variance over the year. Four factors identified that are responsible for 70% of the total variance in water quality of the river. The first factor is a multi-source factor that explains 31.6% of the total variation. The second factor is organic source factor that explains 14.6% of the total variance. The third factor is soil erosion source factor that explains 12.8% of the total variance. The fourth factor is caused by sulfates and calcium and explains 11% of the total variance. The hierarchical CA classified the 10 monitoring sites into three differentiated groups showed similar water quality features. The natural hydro-geochemical and anthropogenic activities such as domestic wastewater influents were the significant factors affect the water quality. The results of this work can be used to decrease the number of samples analyzed in both space and time to help the decision makers in recognizing priorities to improve water quality.

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“…Kazama 2007). PCA and FA methods have been applied to analyze and assess the water quality of several water bodies around the world over the last decade: River Elbe, Germany (Petersen et al 2001); Gomti River, India (Singh et al 2005); Jajrood River, Iran (Razmkhah et al 2010); Qiantan River, China (Huang et al 2010 ); Kinta River, Malaysia (Gazzaz et al 2012); Tigris River basin, Turkey (Varol et al 2012); and the Alqueva reservoir, Portugal (Palma et al 2014).…”

Section: Sampling and Measurementsmentioning

confidence: 99%

Assessment of water quality variations under non-rainy and rainy conditions by principal component analysis techniques in Lake Doam watershed, Korea

Bhattrai¹,

Kwak²,

Heo

2015

J. Ecol. Environ.

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This study was based on water quality data of the Lake Doam watershed, monitored from 2010 to 2013 at eight different sites with multiple physiochemical parameters. The dataset was divided into two sub-datasets, namely, non-rainy and rainy. Principal component analysis (PCA) and factor analysis (FA) techniques were applied to evaluate seasonal correlations of water quality parameters and extract the most significant parameters influencing stream water quality. The first five principal components identified by PCA techniques explained greater than 80% of the total variance for both datasets. PCA and FA results indicated that total nitrogen, nitrate nitrogen, total phosphorus, and dissolved inorganic phosphorus were the most significant parameters under the non-rainy condition. This indicates that organic and inorganic pollutants loads in the streams can be related to discharges from point sources (domestic discharges) and non-point sources (agriculture, forest) of pollution. During the rainy period, turbidity, suspended solids, nitrate nitrogen, and dissolved inorganic phosphorus were identified as the most significant parameters. Physical parameters, suspended solids, and turbidity, are related to soil erosion and runoff from the basin. Organic and inorganic pollutants during the rainy period can be linked to decayed matters, manure, and inorganic fertilizers used in farming. Thus, the results of this study suggest that principal component analysis techniques are useful for analysis and interpretation of data and identification of pollution factors, which are valuable for understanding seasonal variations in water quality for effective management.

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Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: A case study on Jajrood River (Tehran, Iran)

Cited by 198 publications

References 36 publications

Clusterization of Surface Water Quality and Its Relation to Climate and Land Use/Cover

Clusterization of Surface Water Quality and Its Relation to Climate and Land Use/Cover

Water Quality Assessment of Al-Gharraf River, South of Iraq Using Multivariate Statistical Techniques

Assessment of water quality variations under non-rainy and rainy conditions by principal component analysis techniques in Lake Doam watershed, Korea

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