Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

Popugaeva, Daria; Kreyman, Konstantin; Ray, Ajay K.

doi:10.1002/cjce.23601

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…Rs performs the correlation analysis based on ranked data, rather than actual values [ 22 ]. It does not require any assumptions regarding the frequency distribution of the variables, and is not sensitive to outliers [ 23 ]. Rs has been used in correlation analyses in groundwater quality studies, including [ 23 , 24 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

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Health Risks and Potential Sources of Contamination of Groundwater Used by Public Schools in Vhuronga 1, Limpopo Province, South Africa

Odiyo

Mathoni

Makungo

2020

IJERPH

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Public schools in most rural areas of South Africa depend on untreated groundwater due to unreliable water supply by the municipalities. This has the potential to cause water-related health problems to school children. Temperature, pH, and electrical conductivity (EC) were measured in situ. Chemical (fluoride, chloride, sulphate, phosphate, nitrate, magnesium, calcium, sodium, and potassium) and microbial (Escherichia coliform (E. coli), Salmonella typhimurium, and Shigella flexneri) water quality parameters were analysed in groundwater samples from 10 public schools in Vhuronga 1 to determine suitability for use. Quantitative microbial risk assessment was carried out to determine risks of infection and illness due to consumption of groundwater. Correlation analysis was used to identify potential sources of contamination. All physical and most chemical water quality parameters were within guidelines for domestic water use. A high proportion of schools had high levels of microbial organisms. Risks of infection per day were relatively low for all schools. The annual risks of infection due to E. coli and Shigella flexneri for most schools was high, with maximum values of 89.11 and 83.75%, respectively. Maximum risks of illness per year were 31.19, 30.37, and 29.31% for E. coli, Salmonella typhimurium, and Shigella flexneri, respectively. Correlation analysis indicated potential contamination of groundwater by agricultural activities, domestic waste, and faecal contamination from pit latrines. Preventive and mitigation measures to minimise such risks, including locating boreholes at safe distances from pit latrines, prevention/minimisation of pollution of groundwater from agricultural activities, and point-of-use treatment of groundwater by the schools are therefore essential.

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Section: Methodsmentioning

confidence: 99%

“…It does not require any assumptions regarding the frequency distribution of the variables, and is not sensitive to outliers [ 23 ]. Rs has been used in correlation analyses in groundwater quality studies, including [ 23 , 24 , 25 ]. Rs was calculated in Equation (4) in Microsoft Excel (2016, Microsoft, Johannesburg, South Africa).…”

Section: Methodsmentioning

confidence: 99%

Health Risks and Potential Sources of Contamination of Groundwater Used by Public Schools in Vhuronga 1, Limpopo Province, South Africa

Odiyo

Mathoni

Makungo

2020

IJERPH

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“…The range of r is from − 1 to + 1. The correlation between the water quality parameters is generally obtained to assess their inter-dependence, which helps in understanding the dominant parameters (Mohamed et al 2019;Popugaeva et al 2020). This ultimately helps in decision-making to monitor the water quality.…”

Section: Correlationmentioning

confidence: 99%

“…This can be achieved by the p value test. The lower the p value, the more significant is the correlation and vice-versa (Helena et al 2000;Popugaeva et al 2020;Rocha et al 2019). In this study, the significance of the correlation is tested at a confidence level of 90% (i.e., p value < 0.1).…”

Section: Correlationmentioning

confidence: 99%

Groundwater quality assessment using geospatial and statistical approaches over Faridabad and Gurgaon districts of National Capital Region, India

2022

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This study presents an assessment of groundwater quality over the two constituent districts (Faridabad and Gurgaon) of the National Capital Region (NCR), India, using geospatial and statistical approaches. These districts have been subjected to rapid urbanization and industrialization in recent years. The groundwater quality parameters viz., pH, electrical conductivity (EC), carbonate (CO32−), bicarbonate (HCO3−), chloride (Cl−), sulphate (SO42−), nitrate (NO3−), fluoride (F−), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), silica (SiO2), and total hardness (TH) are obtained for the year of 2017 from 28 sites over the study area. The suitability for human drinking purposes is assessed by comparing the concentration of parameters at each site with respect to their permissible limits recommended by the Bureau of Indian Standards (IS 10500: 2012). The geospatial mapping of the water quality parameters is carried out to visualize their variations, whereas their risk assessment is accomplished by the statistical approaches viz., water quality index (WQI), correlation, and principal component analysis (PCA). The number of sites exceeding the permissible limits of pH, EC, Cl−, SO42−, NO3−, F−, Ca2+, Mg2+, Na+, K+, and TH is obtained to be 7, 15, 5, 6, 8, 3, 5, 7, 18, 3, and 8, respectively. The WQI analysis revealed 10 out of the 28 sites to be unsuitable for drinking purposes. The parameters mostly exhibited positive correlations except for pH that showed a negative correlation with other parameters. The results of PCA revealed the first principal component (PC1) to explain more than 95% of the total variance, thereby significantly reducing the dimensionality. The deteriorated water quality may be mainly attributed to anthropogenic activities, i.e., reckless industrial growth, population explosion, and rapid urbanization. This study emphasizes the need for regular water quality monitoring, and the information reported will certainly help for water resources planning and management, especially over the industrial regions of NCR, India.

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“…There is no universally acknowledged best method to optimize the SVM parameters. At present, the [30] to determine whether the original variables are suitable for factor analysis. The calculated KMO value is 0.959, which means that the data be used for factor analysis.…”

Section: Svmmentioning

confidence: 99%

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

Ni¹,

Yao²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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The scientific evaluation of water quality change trends and pollution characteristics is of great significance to improving the current situation of water resources. The particle swarm optimization support vector machine based on principal component analysis (PCA-PSO-SVM) was used to conduct a comprehensive evaluation of groundwater quality in Xinzhou city, and the results were compared with those of a variety of traditional water quality evaluation methods. The evaluation results show that the water quality evaluation model based on PCA-PSO-SVM is more comprehensive and objective. The overall groundwater quality situation in Xinzhou city is generally good. The upper pore water quality is mainly class II, and the lower karst water is mainly class III. The water quality shows significant spatial differences. Compared with the traditional water quality evaluation method, the improved SVM algorithm compensates for the defects of the traditional method. The model structure is stable, and the accuracy and calculation efficiency are high, which makes the method worthy of promotion and application. The research results can provide a scientific basis and reference value for the water quality evaluation of groundwater-related projects.

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Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

Cited by 16 publications

References 33 publications

Health Risks and Potential Sources of Contamination of Groundwater Used by Public Schools in Vhuronga 1, Limpopo Province, South Africa

Health Risks and Potential Sources of Contamination of Groundwater Used by Public Schools in Vhuronga 1, Limpopo Province, South Africa

Groundwater quality assessment using geospatial and statistical approaches over Faridabad and Gurgaon districts of National Capital Region, India

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

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