Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Abstract

Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling

“…By contrast, the PLSR and PLR methods are easy and do not need several steps for calculating the PLI. The multivariate regression models such as PLSR and MLR have recently been used as alternative methods to predict the water quality indices based on data for several trace elements [37,39].…”

“…To the best of our knowledge, the issue of predicting PLI using PLSR and MLR models, based on trace element data, has not been addressed to date. The multivariate regression models were recently shown to perform well in predicting the water quality indices [37,39]. For example, Gad et al [37] found that PLSR based on data for several trace elements accurately estimated four pollution indices for water and the drinking water quality index (DWQI) for both the Cal.…”

“…and from 0.88 to 0.99 for the Val. Elsayed et al [39] found that principal component regression (PCR) and support vector machine regression (SVMR) represented robust models for predicting six water quality indices in the Cal. and Val.…”

“…Therefore, using these methods, the PLI or other pollution indices can be simultaneously estimated from data for several trace elements. PLSR can reduce many collinear factors to a few, non-correlated latent factors, preventing the overfitting or underfitting of the data, and avoiding redundant data [37,39]. There is little information available with which to evaluate the PLSR and MLR methods based on trace elements for assessing the PLI of soil in different layers.…”

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

“…Generally, users of irrigation water may experience a range of effects due to changes in WQ. The most common WQ-related problems in irrigated agriculture are those related to [32,37,38]:…”