A probabilistic water quality index for river water quality assessment: a case study

Nikoo, Mohammad Reza; Kerachian, Reza; Malakpour‐Estalaki, Siamak; Bashi-Azghadi, Seyyed Nasser; Azimi-Ghadikolaee, Mohammad Mahdi

doi:10.1007/s10661-010-1842-4

Cited by 65 publications

(29 citation statements)

References 34 publications

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“…The two other steps (including classifying for interpretation) are completely obtained by rules (using expert's judgement) and sets of linguistic computation, e.g., fuzzification, evaluation of inference rules, and defuzzification. The development and application of this index has been applied in Spain (in Ocampo-Duque et al 2006), in Iran (in Nikoo et al 2011), in Brazil (in Lermontov et al 2009). …”

Section: Fuzzy-based Indicesmentioning

confidence: 99%

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Development of river water quality indices—a review

Sutadian

Muttil

Yilmaz

et al. 2015

Environ Monit Assess

341

210

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The use of water quality indices (WQIs) as a tool to evaluate the status of water quality in rivers has been introduced since the 1960s. The WQI transforms selected water quality parameters into a dimensionless number so that changes in river water quality at any particular location and time could be presented in a simple and easily understandable manner. Although many WQIs have been developed, there is no worldwide accepted method for implementing the steps used for developing a WQI. Thus, there is a continuing interest to develop accurate WQIs that suit a local or regional area. This paper aims to provide significant contribution to the development of future river WQIs through a review of 30 existing WQIs based on the four steps needed to develop a WQI. These steps are the selection of parameters, the generation of sub-indices, the generation of parameter weights and the aggregation process to compute the final index value. From the 30 reviewed WQIs, 7 were identified as most important based on their wider use and they were discussed in detail. It was observed that a major factor that influences wider use of a WQI is the support provided by the government and authorities to implement a WQI as the main tool to evaluate the status of rivers. Since there is a lot of subjectivity and uncertainty involved in the steps for developing and applying a WQI, it is recommended that the opinion of local water quality experts is taken, especially in the first three steps (through techniques like Delphi method). It was also observed that uncertainty and sensitivity analysis was rarely undertaken to reduce uncertainty and hence such an analysis is recommended for future studies.

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Section: Fuzzy-based Indicesmentioning

confidence: 99%

“…The pair-wise comparison matrix in the AHP can be used for obtaining different weighs for individual parameters (Ocampo-Duque et al 2006) or for a different set of parameters (Nikoo et al 2011).…”

Section: C) Establishing Weightsmentioning

confidence: 99%

Development of river water quality indices—a review

Sutadian

Muttil

Yilmaz

et al. 2015

Environ Monit Assess

341

210

View full text Add to dashboard Cite

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“…The WQI index produces a number between 0 (worst quality) and 100 (best quality) comprised of 5 categories as presented in Table 5 (CCME 2004; Nikoo et al 2011). …”

Section: Water Quality Index (Wqi)mentioning

confidence: 99%

Spatio-temporal variations in hydro-geochemistry of groundwater at rural, urban and industrial areas of Kanpur, India

Kumar

et al. 2018

Environmental Sustainability

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Physicochemical characteristics of groundwater provide vital information regarding prevailing geochemical, geomorphological and predominant pollution conditions in an area. In the present work, hydro-geochemical properties of ground water were examined at rural, urban and industrial areas of Kanpur to determine its aptness for drinking and irrigation. Groundwater samples were randomly collected from deep borewell pumps across the city. The samples were examined for pH, electrical conductivity (EC), nitrate, sulphate, chloride, hardness, alkalinity, total dissolved solids, sodium, potassium, calcium, which ranged between 7.10 and 8. −1 in post-monsoon season respectively. Water Quality Index of groundwater during post-monsoon were recorded to be better than the pre-monsoon season, reason being percolation of water during rainfall in monsoon. The pre and post-monsoon data provided valuable information regarding the suitability of groundwater for various purposes such as drinking and irrigation. Results showed 'fairly' and 'good' categories of water quality in the pre-monsoon season which further improved in post monsoon season. Results clearly specify that the natural process of groundwater recharge is an important mechanism to maintain the quality of groundwater.

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“…In a recent study, (Gharibi et al 2012) developed a FWQI for which the water quality indicators were practical and easy to measure, including heavy metals, and used the index to assess water quality in the Mamloo dam for drinking purposes. A different approach based on hybrid fuzzyprobability models was adopted in (Ocampo-Duque et al 2013;Nikoo et al 2011). In a recent work, (Wang et al 2014) used variable fuzzy set and the information entropy theory as an assessment model to evaluate water quality of the Meiliang Bay in Taihu Lake Basin in China.…”

Section: Introductionmentioning

confidence: 99%

River water quality index for Morocco using a fuzzy inference system

2014

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Background: The aim of this work is to propose a new river water quality index using fuzzy logic. The proposed fuzzy index combines quality indicators' prescribed thresholds extracted mainly from the Moroccan and the Quebec water legislations. The latter is reputed for its strict water quality assessment. The proposed index combines six indicators, and not only does it exhibit a tool that accounts for the discrepancy between the two base indices, but also provides a quantifiable score for the determined water quality. These classifications with a membership grade can be of a sound support for decision-making, and can help assign each section of a river a gradual quality sub-objective to be reached. Results: To demonstrate the applicability of the proposed approach, the new index was used to classify water quality in a number of stations along the basins of Bouregreg-Chaouia and Zizi-Rhéris. The obtained classifications were then compared to the conventional physicochemical water quality index currently in use in Morocco. The results revealed that the fuzzy index provided stringent classifications compared to the conventional index in 41% and 33% of the cases for the two basins respectively. These noted exceptions are mainly due to the big disparities between the different quality thresholds in the two standards, especially for fecal coliform and total phosphorus. Conclusions: These large disparities put forward an argument for the Moroccan water quality legislation to be upgraded to align water and environmental assessment methods with other countries in order to mitigate the risks of failing to achieve a good ecological status.

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A probabilistic water quality index for river water quality assessment: a case study

Cited by 65 publications

References 34 publications

Development of river water quality indices—a review

Development of river water quality indices—a review

Spatio-temporal variations in hydro-geochemistry of groundwater at rural, urban and industrial areas of Kanpur, India

River water quality index for Morocco using a fuzzy inference system

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