Characterization of the hydrochemistry of water resources of the Weibei Plain, Northern China, as well as an assessment of the risk of high groundwater nitrate levels to human health

Liu, Jiutan; Peng, Yuming; Li, Changsuo; Gao, Zongjun; Chen, Shaojie

doi:10.1016/j.envpol.2020.115947

Cited by 119 publications

(37 citation statements)

References 52 publications

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“…The most important source of nitrate is the agriculture, which generates diffuse pollution followed by point pollution with urban and industrial discharge [8][9][10]. The nitrogen accumulated in soil leaches to water bodies through runoff or percolation, and then, hydrology is the means of transport until it is seen as a pollutant [11].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…e methodology for evaluating groundwater quality has experienced several stages from the traditional water quality index (WQI) to entropy water quality index (EWQI). e EWQI with entropy values involving various hydrochemical parameters has been believed to be a more robust approach due to its more comprehensive computation [19,[25][26][27][28][29]. In addition, the Geography Information System software (GIS) is helpful to reveal the spatial distribution of EWQI values.…”

Section: Introductionmentioning

confidence: 99%

Hydrochemistry and Entropy-Based Groundwater Quality Assessment in the Suining Area, Southwestern China

Zhang

Luo

et al. 2021

Journal of Chemistry

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Groundwater is an essential resource for sustainable development, whose quality is significant for human health. In the present study, twenty-eight groundwater samples were collected from domestic tube wells and public water supply wells in the Suining area, southwestern China. The integration of statistical analysis, correlations of ions, geomodelling, and entropy-weighted water quality index (EWQI) was carried out to clarify the hydrochemistry and groundwater quality in the study area. By the statistical analysis, the cations followed the concentration order as Ca2+> Na+> Mg2+> K+, while anions’ concentrations were HCO3− > SO42− > Cl− > NO3− > F−. Piper trilinear diagram showed the hydrochemical type was characterized as Ca-HCO3. Correlations of ions and geomodelling revealed the concentrations of major ions were mainly determined by carbonate dissolution and ion exchange process, and NO3− concentrations were controlled by agriculture activities. EWQI computation demonstrated that most of the groundwater samples possessed EWQI values higher than 100. Therefore, groundwater quality is lower than the permissible limit of the World Health Organization (WHO), suitable for drinking purposes in the Suining area. Our study provides vital knowledge for groundwater management in the Suining and other similar areas.

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“…WQI‐based maps are readily understandable and help to raise awareness of groundwater contamination and to impose rules on proper waste management from various sources, enforcing restrictions on the extraction of groundwater, and may also assist in formulating effective management strategies to protect aquifers from contamination (Abbasi & Abbasi, 2012). Over the years, the WQI approach has been followed by many researchers globally (Amiri et al, 2021; Liu et al, 2021; Patil et al, 2020; Sener et al, 2017). A detailed review of WQI developments in recent years has been provided by Lumb et al (2011), and Mohebbi et al (2013), highlighting key shortcomings in the index development process.…”

Section: Introductionmentioning

confidence: 99%

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

Chaudhry

Sachdeva

2021

Water & Environment J

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This study proposes the evaluation of GIS‐based groundwater quality index (GQI) and spatial variability with a hybrid system that combines Fuzzy Logic with GIS‐based GQI. A case study in the semi‐arid area of Punjab, namely Rupnagar, illustrates the proposed hybrid outline. Two GIS‐based GQI models GQI‐10 (using ten water quality parameters (WQPs)) and GQI‐6 (using six “apprehensive/crucial” WQPs), as well as hybrid Fuzzy Logic GIS‐based GQI (FGQI) (using six crucial WQPs) models, were developed using trapezoidal membership functions and rule sets based on expert's knowledge. Performance comparison of GQI models showed that the FGQI model predicts improved groundwater quality than the GQI‐10 and GQI‐6. FGQI model variance, RMSE, and R2 values are 84.25, 6.49, 0.89, respectively. Results have shown that the developed FGQI model is successful and GQI modelling suggests the use of northern and central groundwater without treatment to be unsafe for drinking purposes.

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Characterization of the hydrochemistry of water resources of the Weibei Plain, Northern China, as well as an assessment of the risk of high groundwater nitrate levels to human health

Cited by 119 publications

References 52 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

Hydrochemistry and Entropy-Based Groundwater Quality Assessment in the Suining Area, Southwestern China

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

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