Water quality indicators: Comparison of a probabilistic index and a general quality index. The case of the Confederación Hidrográfica del Júcar (Spain)

Córdoba, Eduardo Beamonte; Martínez, Alejandro Casino; Ferrer, Ernesto Veres

doi:10.1016/j.ecolind.2010.01.013

Cited by 45 publications

(9 citation statements)

References 34 publications

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“…Finally, nitrate concentrations in the rivers and aquifers displayed a tendency to increase from the upstream to the downstream, except with the Júcar system midstream (also polluted), as presented by the authors in the Refs. [36,52,53].…”

Section: Nitrate Transfer From Gw Into Riversmentioning

confidence: 99%

Integrated Surface-Groundwater Modelling of Nitrate Concentration in Mediterranean Rivers, the Júcar River Basin District, Spain

et al. 2021

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High nutrient discharge from groundwater (GW) into surface water (SW) have multiple undesirable effects on river water quality. With the aim to estimate the impact of anthropic pressures and river–aquifer interactions on nitrate status in SW, this study integrates two hydrological simulation and water quality models. PATRICAL models SW–GW interactions and RREA models streamflow changes due to human activity. The models were applied to the Júcar River Basin District (RBD), where 33% of the aquifers have a concentration above 50 mg NO3−/L. As a result, there is a direct linear correlation between the nitrate concentration in rivers and aquifers (Júcar r2 = 0.9, and Turia r2 = 0.8), since in these Mediterranean basins, the main amount of river flows comes from groundwater discharge. The concentration of nitrates in rivers and GW tends to increase downstream of the district, where artificial surfaces and agriculture are concentrated. The total NO3− load to Júcar RBD rivers was estimated at 10,202 tN/year (239 kg/km2/year), from which 99% is generated by diffuse pollution, and 3378 tN/year (79 kg/km2/year) is discharged into the Mediterranean Sea. Changes in nitrate concentration in the RBD rivers are strongly related to the source of irrigation water, river–aquifer interactions, and flow regulation. The models used in this paper allow the identification of pollution sources, the forecasting of nitrate concentration in surface and groundwater, and the evaluation of the efficiency of measures to prevent water degradation, among other applications.

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Section: Nitrate Transfer From Gw Into Riversmentioning

confidence: 99%

Integrated Surface-Groundwater Modelling of Nitrate Concentration in Mediterranean Rivers, the Júcar River Basin District, Spain

et al. 2021

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“…Subsequently, Spearman's correlation coefficient (rs) [38] was used to study the association between water quality parameters at each of the monitoring stations. PCA and Spearman's coefficient were used to detect possible water quality indicator parameters [39] in the DWSS under study. Namely, it was assumed as a hypothesis that the water quality parameters that showed the best significant correlations were those that could be suggested as water quality indicators.…”

Section: Discussionmentioning

confidence: 99%

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

Zafra-Mejía,

Rondón-Quintana,

Urazán-Bonells

2024

Hydrology

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The objective of this paper is to use autoregressive, integrated, and moving average (ARIMA) and transfer function ARIMA (TFARIMA) models to analyze the behavior of the main water quality parameters in the initial components of a drinking water supply system (DWSS) of a megacity (Bogota, Colombia). The DWSS considered in this study consisted of the following components: a river, a reservoir, and a drinking water treatment plant (WTP). Water quality information was collected daily and over a period of 8 years. A comparative analysis was made between the components of the DWSS based on the structure of the ARIMA and TFARIMA models developed. The results show that the best water quality indicators are the following: turbidity > color > total iron. Increasing the time window of the ARIMA analysis (daily/weekly/monthly) suggests an increase in the magnitude of the AR term for each DWSS component (WTP > river > reservoir). This trend suggests that the turbidity behavior in the WTP is more influenced by past observations compared to the turbidity behavior in the river and reservoir, respectively. Smoothing of the data series (moving average) as the time window of the ARIMA analysis increases leads to a greater sensitivity of the model for outlier detection. TFARIMA models suggest that there is no significant influence of past river turbidity events on turbidity in the reservoir, and of reservoir turbidity on turbidity at the WTP outlet. Turbidity outlier events between the river and reservoir occur mainly in a single observation (additive outliers), and between the reservoir and WTP also have a permanent effect over time (level shift outliers). The AR term of the models is useful for studying the transfer of effects between DWSS components, and the MA term is useful for studying the influence of external factors on water quality in each DWSS component.

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“…A recent survey of approaches for constructing indices (Bierman et al, 2011) cite the techniques of cluster analysis (Hargiss et al, 2008;Khalil et al, 2010;Styers et al, 2010), principal components analysis, factor analysis (Blocksom and Johnson, 2009;Leunda et al, 2009;Liou et al, 2004;Ou et al, 2010;Tran et al, 2010), discriminant analysis (Feio et al, 2009;Kane et al, 2009), and fuzzy logic (Ghosh and Mujumdar, 2010). This work has been extended to include errors in measurement of the constituent variables used to construct the water quality index (Beamonte Córdoba et al, 2010;Castoldi et al, 2009;Ghosh and Mujumdar, 2010;Sin et al, 2009;Taheriyoun et al, 2010).…”

Section: Introductionmentioning

confidence: 95%

“…Water quality indices have been developed and used to characterize a wide variety of phenomena including drinking water (Beamonte Córdoba et al, 2010), bioassessment (Aguiar et al 2014;Blanchet et al, 2008;Kanno et al, 2010;Stoddard et al, 2008), fresh water habitat (Pinto et al, 2009;Simaika and Samways, 2011), effect of agriculture on stream water (Justus et al, 2010;Shiels, 2010), river water quality (Feio et al, 2009;Navarro-Llácer et al, 2010), ecological condition (Jordan et al, 2010;Marchini et al, 2009;Seilheimer et al, 2009;Tran et al, 2008) and variable reduction for selection of variables to monitor (Kantoussan et al, 2010). A recent survey of approaches for constructing indices (Bierman et al, 2011) cite the techniques of cluster analysis (Hargiss et al, 2008;Khalil et al, 2010;Styers et al, 2010), principal components analysis, factor analysis (Blocksom and Johnson, 2009;Leunda et al, 2009;Liou et al, 2004;Ou et al, 2010;Tran et al, 2010), discriminant analysis (Feio et al, 2009;Kane et al, 2009), and fuzzy logic (Ghosh and Mujumdar, 2010).…”

Section: Introductionmentioning

confidence: 99%

Application of index number theory to the construction of a water quality index: Aggregated nutrient loadings related to the areal extent of hypoxia in the northern Gulf of Mexico

Whittaker

Barnhart

Färe

et al. 2015

Ecological Indicators

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Water quality indicators: Comparison of a probabilistic index and a general quality index. The case of the Confederación Hidrográfica del Júcar (Spain)

Cited by 45 publications

References 34 publications

Integrated Surface-Groundwater Modelling of Nitrate Concentration in Mediterranean Rivers, the Júcar River Basin District, Spain

Integrated Surface-Groundwater Modelling of Nitrate Concentration in Mediterranean Rivers, the Júcar River Basin District, Spain

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

Application of index number theory to the construction of a water quality index: Aggregated nutrient loadings related to the areal extent of hypoxia in the northern Gulf of Mexico

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