Spatio-temporal evaluation of Yamchi Dam basin water quality using Canadian water quality index

Farzadkia, Mahdi; Djahed, Babak; Shahsavani, Esmaeel; Poureshg, Yousef

doi:10.1007/s10661-015-4379-8

Cited by 20 publications

(4 citation statements)

References 29 publications

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“…Currently, the WQQR model is mainly applied to China, while other countries have other water quality indexes, such as the Canadian Water Quality Index (CWQI) and Oregon Water Quality Index (OWQI) 18 , 19 . The study area of this study is Jiangxi Province, China, so the WQQR model is used to analyze water quality.…”

Section: Methodsmentioning

confidence: 99%

Improved grey water footprint model based on uncertainty analysis

Feng

2023

Sci Rep

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In the practical water resources management, the allowable thresholds of pollutants are not unique. However, the conventional grey water footprint (GWF) model cannot deal with this uncertainty in the controlling threshold. To solve this problem, an improved GWF model and pollution risk evaluation method is designed according to the uncertainty analysis theory and maximum entropy principle. In this model, GWF is defined as the mathematical expectation of virtual water to dilute the pollution load within the allowable threshold, and the pollution risk is deduced by the stochastic probability by which GWF exceeds the local water resources. And then, the improved GWF model is applied in the pollution evaluation of Jiangxi Province, China. The results show that: (1) From 2013 to 2017, the annual GWF values of Jiangxi Province were 136.36 billion m3, 143.78 billion m3, 143.77 billion m3, 169.37 billion m3 and 103.36 billion m3, respectively. And their pollution risk values and grades were 0.30 (moderate), 0.27 (moderate), 0.19 (low), 0.22 (moderate), and 0.16 (low), respectively. In 2015, the determinant of the GWF was TP, and TN in other years. (2) The improved GWF model has an evaluation result which is basically consistent with WQQR, and it is an effective water resource evaluation method to deal with the uncertainty in controlling thresholds. (3) Compared with the conventional GWF model, the improved GWF model has better capacities in identifying pollution grades and recognizing pollution risks.

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Section: Methodsmentioning

confidence: 99%

Improved grey water footprint model based on uncertainty analysis

Feng

2023

Sci Rep

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“…Earlier, Islam et al [8] modified Equation (1) by allocating weights to water quality parameters and used it for assessing the spatiotemporal variations of disinfection byproducts (DBPs) in distribution networks. Farzadkia et al [9] used Equation 1for spatiotemporal water quality assessment of the Yamchi Dam in Iran. In addition to the inadequate promulgations of environmental laws, they found higher concentrations of suspended solids as the main sources of pollution (for agricultural use) through discharge of wastewater from pisciculture and aquaculture centers along with urban areas in the dam's catchment.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Water Quality Variations in Smaller Water Supply Systems: Using Modified CCME WQI from Groundwater Source to Distribution Networks

Haider

Alkhowaiter²,

Shafiquzzaman

et al. 2019

Water

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Original Canadian Council of Minster of the Environment Water Quality Index (CCME WQI) is being used for assessing the water quality of surface water sources and distribution systems on a case by case basis. Its full potential as a management tool for complete water supply systems (WSSs) has yet to be recognized at the global level. A framework is developed using the modified CCME WQI to assess spatiotemporal water quality from groundwater source to treatment and distribution networks in smaller systems. The modified index resolves a limitation of the original index by also evaluating the microbiological water quality parameters which have to be completely absent for meeting desired drinking water quality standards. The framework divides the distribution network in different zones, which are further segregated into districts, to improve the decision-making process. Temporal assessment identifies the seasons with higher probabilities of failures, while the spatial assessment provides an insight on the performance (i.e., Excellent to Poor) of each district in a distribution network. In addition to failure probability, risk mapping gives appropriate attention to the number of consumers in different districts. Application of the framework on two smaller WSSs (population less than 50,000) in Qassim region revealed that the remotely located districts from the treatment facility underperform in comparison to the closely situated districts. Managers can effectively apply the proposed framework to identify the locations and periods of water quality failures in each component (i.e., source, treatment, and distribution) of a smaller WSS for effective utilization of their resources in Saudi Arabia and elsewhere with similar conditions.

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“…There are various water quality indices available worldwide that can be used for water quality assessment, e.g., the composite water quality identification index (CWQII) (Ban et al, 2014); river pollution index (RPI), forestry water quality index (FWQI) and NSF-WQI (Hoseinzadeh et al, A. K. Shukla et al: Population growth, land use and land cover transformations, and water quality nexus 4747 2014), Canadian water quality index (CWQI) (Farzadkia et al, 2015), comprehensive water pollution index of China (Li et al, 2015), Prati's implicit index of pollution (Prati et al, 1971), Horton's index, Nemerow and Sumitomo pollution index, Bhargava's index, Dinius second index, Smith's index, Aquatic toxicity index, Chesapeake Bay water quality indices, modified Oregon WQI, Li's regional water resource quality assessment index, Stoner's index, two-tier WQI, CCME-WQI, DELPHI water quality index, universal WQI, overall index of pollution (OIP) and coastal WQI for Taiwan (Abbasi and Abbasi, 2012;Rai et al, 2011). Currently, there is not a sufficient amount of literature available on comparisons between all the abovementioned water quality indices based on clusters, differences, validity, etc.…”

Section: Introductionmentioning

confidence: 99%

Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin

Shukla

Ojha

Mijic

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The Upper Ganga River basin is socioeconomically the most important river basin in India and is highly stressed in terms of water resources due to uncontrolled land use and land cover (LULC) activities. This study presents a comprehensive set of analyses to evaluate the population growth, LULC transformations, and water quality nexus for sustainable development in this river basin. The study was conducted at two spatial scales: basin scale and district scale. First, population data were analyzed statistically to study demographic changes, followed by LULC change detection over the period of February–March 2001 to 2012 (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data) using remote sensing and geographical information system (GIS) techniques. Trends and spatiotemporal variations in monthly water quality parameters, viz. biological oxygen demand (BOD), dissolved oxygen (DO, measured in percentage), fluoride (F), hardness (CaCO3), pH, total coliform bacteria and turbidity, were studied using the Mann–Kendall rank test and an overall index of pollution (OIP) developed specifically for this region, respectively. A relationship was deciphered between LULC classes and OIP using multivariate techniques, viz. Pearson's correlation and multiple linear regression. From the results, it was observed that population has increased in the river basin. Therefore, significant and characteristic LULC changes were observed. The river became polluted in both rural and urban areas. In rural areas, pollution is due to agricultural practices, mainly fertilizers, whereas in urban areas it is mainly contributed from domestic and industrial wastes. Water quality degradation has occurred in the river basin, and consequently the health status of the river has also changed from acceptable to slightly polluted in urban areas. Multiple linear regression models developed for the Upper Ganga River basin could successfully predict status of the water quality, i.e., OIP, using LULC classes.

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Spatio-temporal evaluation of Yamchi Dam basin water quality using Canadian water quality index

Cited by 20 publications

References 29 publications

Improved grey water footprint model based on uncertainty analysis

Improved grey water footprint model based on uncertainty analysis

Spatiotemporal Water Quality Variations in Smaller Water Supply Systems: Using Modified CCME WQI from Groundwater Source to Distribution Networks

Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin

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