Multivariate statistics and entropy theory for irrigation water quality and entropy-weighted index development in a subtropical urban river, Bangladesh.

Md Abu Bakar Siddique,Md Younus Mia,Md Bodrud-Doza,Javed Mallick,Abu Reza Md Towfiqul Islam,Md Saddam Hossain,Rahat Khan,Md Ahedul Akbor,Md Wasiq Mamun Sajid,M Safiur Rahman,Md Mostafizur Rahman,Md Hasanuzzaman

doi:10.1007/s11356-021-16343-7

Abstract

Currently, a well-developed combination of irrigation water quality index (IWQIs) and entropy water quality index (EWQIs) for surface water appraisal in a polluted subtropical urban river is very scarce in the literature. To close this gap, we developed IWQIs by establishing statistics-based weights of variables recommended by FAO 29 standard value using the National Sanitation Foundation Water Quality Index (NSFWQI) compared with the proposed EWQIs based on information entropy in the Dhaleshwari River, Bangladesh. Fifty surface water samples were collected from five sampling locations during the dry and wet seasons and analyzed for sixteen variables. Principal component analysis (PCA), factor analysis (FA), Moran's spatial autocorrelation, and random forest (RF) model were employed in the datasets. Weights were allocated for primary variables to compute IWQI-1, 2 and EWQI-1, 2, respectively. The resultant IWQIs showed a similar trend with EWQIs and revealed poor to good quality water, with IWQI-1 for the dry season and IWQI-2 for the wet season is further suggested. The entropy theory recognized that Mg2+, Cr, TDS, and Cl- for the dry season and Cd, Cr, Cl-, and SO42- for the wet season are the major contaminants that affect irrigation water quality. The primary input variables were lessened to ultimately shortlisted ten variables, which revealed good performance in demonstrating water quality status since weights have come effectively from PCA than FA. The results of the RF model depict NO3-, Mg2+, and Cr as the most predominant variables influencing surface water quality. A significant dispersed pattern was detected for IWQImin-3 in the wet season (Moran's I>0). Overall, both IWQIs and EWQIs will generate water quality control cost-effective, completely objective to establish a scientific basis of sustainable water management in the study basin.

Highlights

Water quality means its qualification for a particular reason and is controlled by class and number of disintegrated compositions (Ewaid et al 2019)
We developed irrigation water quality index (IWQI) by establishing statistics-based weights of variables recommended by FAO 29 standard value using the National Sanitation Foundation Water Quality Index (NSFWQI) compared with the proposed entropy-weighted water quality (EWQI) based on information entropy in the Dhaleshwari River, Bangladesh
We developed a well-establish IWQIs and EWQIs for surface water suitability for agricultural purposes concerning representative variables suggested by FAO29 standard and a well-accepted method, namely, NSFWQI and entropy theory

Summary

Introduction

Water quality means its qualification for a particular reason and is controlled by class and number of disintegrated compositions (Ewaid et al 2019) This composition acts a pivotal role in plant development and advancement either legitimately as far as insufficiency or in a roundabout way through influencing supplement accessibility (Salem et al 2019). One of the key potential issues of water quality studies is the set of variables that can be continuously monitored and related costing, collecting, analyzing and interpreting these datasets To solve these issues, a particular water quality index (WQI) has been employed to handle the effective water quality classification using many parameters that have been widely established as informative for the end-user. It gives a comprehensive scenario to decision-makers to take the necessary steps for conserving a surface water body

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Discussion

Conclusion