Literature DB >> 28273593

Introducing a water quality index for assessing water for irrigation purposes: A case study of the Ghezel Ozan River.

Farhad Misaghi1, Fatemeh Delgosha2, Mostafa Razzaghmanesh3, Baden Myers4.   

Abstract

Rivers are one of the main water resources for agricultural, drinking, environmental and industrial use. Water quality indices can and have been used to identify threats to water quality along a stream and contribute to better water resources management. There are many water quality indices for the assessment and use of surface water for drinking purposes. However, there is no well-established index for the assessment and direct use of river water for irrigation purposes. The aim of this study was to adopt the framework of the National Sanitation Foundation Water Quality Index (NSFWQI) and, with adjustments, apply it in a way which will conform to irrigation water quality requirements. To accomplish this, the NSFWQI parameters for drinking water use were amended to include water quality parameters suitable for irrigation. For each selected parameter, an individual weighting chart was generated according to the FAO 29 guideline. The NSFWQI formula was then used to calculate a final index value, and for each parameter an acceptable range in this value was determined. The new index was then applied to the Ghezel Ozan River in Iran as a case study. A forty five year record of water quality data (1966 to 2010) was collected from four hydrometery stations along the river. Water quality parameters including Na+, Cl-, pH, HCO-3, EC, SAR and TDS were employed for water quality analysis using the adjusted NSFWQI formula. The results of this case study showed variation in water quality from the upstream to downstream ends of the river. Consistent monitoring of the river water quality and the establishment of a long term management plan were recommended for the protection of this valuable water resource.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Ghezel Ozan River; Irrigation; NSFWQI; Water quality index

Year:  2017        PMID: 28273593     DOI: 10.1016/j.scitotenv.2017.02.226

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  7 in total

1.  Ecological water health assessment using benthic macroinvertebrate communities (case study: the Ghezel Ozan River in Zanjan Province, Iran).

Authors:  Jaber Aazami; Naser KianiMehr; Abasali Zamani
Journal:  Environ Monit Assess       Date:  2019-10-29       Impact factor: 2.513

2.  Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia.

Authors:  Muhammad Mazhar Iqbal; Muhammad Shoaib; Hafiz Umar Farid; Jung Lyul Lee
Journal:  Int J Environ Res Public Health       Date:  2018-10-15       Impact factor: 3.390

3.  Geospatial Distributions of Groundwater Quality in Gedaref State Using Geographic Information System (GIS) and Drinking Water Quality Index (DWQI).

Authors:  Basheer A Elubid; Tao Huag; Ekhlas H Ahmed; Jianfei Zhao; Khalid M Elhag; Waleed Abbass; Mohammed M Babiker
Journal:  Int J Environ Res Public Health       Date:  2019-02-28       Impact factor: 3.390

4.  Evaluation on Early Drought Warning System in the Jinghui Channel Irrigation Area.

Authors:  Shibao Lu; Yizi Shang; Hongbo Zhang
Journal:  Int J Environ Res Public Health       Date:  2020-01-06       Impact factor: 3.390

5.  A Novel Method in Surface Water Quality Assessment Based on Improved Variable Fuzzy Set Pair Analysis.

Authors:  Yucheng Liu; Chuansheng Wang; Yutong Chun; Luxin Yang; Wei Chen; Jack Ding
Journal:  Int J Environ Res Public Health       Date:  2019-11-06       Impact factor: 3.390

6.  Regulation-based probabilistic substance quality index and automated geo-spatial modeling for water quality assessment.

Authors:  Artyom Nikitin; Polina Tregubova; Dmitrii Shadrin; Sergey Matveev; Ivan Oseledets; Maria Pukalchik
Journal:  Sci Rep       Date:  2021-12-10       Impact factor: 4.379

7.  Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China.

Authors:  Xiaoping Wang; Fei Zhang; Jianli Ding
Journal:  Sci Rep       Date:  2017-10-09       Impact factor: 4.379

  7 in total

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