Correlation Analysis of Irrigation Water Quality Data in Huraimla governorate, Saudi Arabia

2012 ◽  
Vol 9 (1) ◽  
pp. 73-84
Author(s):  
Mohammed A. Al-Sulaiman
Keyword(s):  
Water Quality ◽  
Saudi Arabia ◽  
Correlation Analysis ◽  
Irrigation Water ◽  
Quality Data ◽  
Irrigation Water Quality ◽  
Water Quality Data

scholarly journals Mapping of use and occupation of the soil and irrigation water quality in the city of Salto do Lontra-Paraná, Brazil

2013 ◽  
Vol 33 (5) ◽  
pp. 1024-1037 ◽  
Author(s):  
Suzana C. Wrublack ◽  
Erivelto Mercante ◽  
Marcio A. Vilas Boas
Keyword(s):  
Water Quality ◽  
Irrigation Water ◽  
Bare Soil ◽  
Quality Parameters ◽  
Quality Data ◽  
Irrigation Water Quality ◽  
Water Quality Data ◽  
Thermotolerant Coliforms ◽  
Quality Of Irrigation Water

The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Paraná, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm - MAXVER, and the water quality parameters analyzed were pH, EC, HCO3-, Cl-, PO4(3-), NO3-, turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality.

scholarly journals Water Quality Assessment of 14 Reservoirs in Geum River Basin Using Multivariate Statistical Analysis

2021 ◽  
Vol 43 (3) ◽  
pp. 171-186
Author(s):  
Jin Ho Kim ◽  
Jin Chul Joo ◽  
Chae Min Ahn ◽  
Dae Ho Hwang
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Correlation Analysis ◽  
Principal Components ◽  
Organic Pollution ◽  
Quality Characteristics ◽  
Quality Data ◽  
Water Quality Data ◽  
River Watershed ◽  
Internal Production

Objectives : 14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality.Methods : Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering.Results and Discussion : From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants.Conclusions : Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis.

scholarly journals EVALUATION OF IRRIGATION WATER Quality INDEX (IWQI) FOR MAIN IRAQI RIVERS (TIGRIS, EUPHRATES, ShATT AL-ARAB AND DIYALA)

2017 ◽  
Vol 48 (4) ◽  
Author(s):  
Saloom & Oleiwi
Keyword(s):  
Water Quality ◽  
Irrigation Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Data ◽  
Irrigation Water Quality ◽  
The Impact ◽  
Quality Of Irrigation Water ◽  
Irrigation Water Quality Index

As a result of different quality standards for irrigation water and the varying ion composition, and the fact that classification of irrigation water consists of large and complex data, this study was conducted in order to find a way for combining the complex water quality data into a single value, a quality of irrigation water index (IWQI) which reflects the suitability of the water quality for irrigation. Irrigation water quality variables were divided into five groups according to Food and Agriculture Organization FAO guide. The order of the parameters were, Salinity expressed in electrical conductivity (EC), Sodium adsorption ratio (SAR), Toxicity of specific ions (boron, chloride, sodium, Toxic trace elements and Miscellaneous effects on sensitive crops (nitrates and bicarbonates and pH). Linear equations of each variable and the formulation of mathematical equations had been done to convert the actual concentration values in the classification adopted to estimate the values of the indicators (sub-indices) and then converting the actual values and different units for each variable to the estimated values under the general scheme consists of grades between (0 -100). For the purpose of calculating irrigation water quality index, a software was originated entitled IWQI program was applied to the data of the irrigation water samples for eighteen (18) locations of water sampling in the rivers: Tigris, Euphrates, Diyala and Shatt al-Arab. Results showed that the values of irrigation water quality index for the period March to December 2015 of the Tigris River were highest than the values of Euphrates River at all locations from the north to the south as it was estimated 94.38 and 88.6 in Muthana bridge site (Tigris) and sader Al- Yusufiya (Euphrates), respectively in Baghdad and reached 74.55 and 67.78 in Qurna (Tigris) and Qurna (Euphrates), respectively. Irrigation water quality index of Shatt al-Arab was at the site of Altnoma 39.78 and classified as almost unsuitable. In Diyala River, it has been observed that the impact of Rustumiya weste water station in reducing the quality of irrigation water quality index was relatively low and water in the two sites (before and after Rustumiya station) are classified as moderately suitable.

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