Factor analysis and spatial distribution of water quality parameters of Aurangabad District, India.

Kale, Amol; Bandela, N. N.; Kulkarni, Jeetendra; Raut, Kishor

doi:10.1016/j.gsd.2020.100345

Cited by 27 publications

(10 citation statements)

References 14 publications

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“…As presented in Table 5, the results of PCA method could explain 99.2% of total variations in groundwater quality with 11 PCs. According to Kale et al (2020) [43], the eigenvalue is the criteria to decide the importance level of each component in the original data set. Since the eigenvalues from PC1 to PC5 were greater than 1, these PCs were further considered to determine potential groundwater pollution sources.…”

Section: ) Principal Component Analysis (Pca)mentioning

confidence: 99%

Groundwater Quality Assessment Using Groundwater Quality Index and Multivariate Statistical Methods and Human Health Risk Assessment in a Coastal Region of the Vietnamese Mekong Delta

2022

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This study aimed to evaluate the suitability of groundwater for drinking purposes and assess the associated human health risks for different age groups in a coastal province of Mekong Delta, Vietnam. Twenty groundwater samples were collected in Soc Trang Province, and various water quality parameters were analyzed. The data were employed to calculate entropy-weighted groundwater quality index (EWQI), principal component analysis (PCA), cluster analysis (CA), and non-carcinogenic and carcinogenic risks for adult and children health. The results revealed that groundwater in some locations, especially in GW19, was polluted by hardness, total dissolved solids, NH4+, Cl-, Fe, total coliform, and E. coli. In addition, 5 principal components from the PCA results could explain 84.5% of the total variation of groundwater quality, which also suggested that the potential groundwater pollution sources were geochemical processes, agricultural activities, domestic and industrial wastewater, seawater intrusion, and excessive nitrogen fertilizer application. The CA results showed that monitoring locations can be divided into 4 clusters based on their similarities in groundwater quality, and the most polluted group was found at cluster IV (GW19). The computed EWQI values ranged from 20.05 to 738.52, with approximately 45% of total samples classifying good to excellent water quality. The sampling points with undrinkable quality are mainly located in the northeast and center of the province. The ratio of children and adults under the threat of adverse health effects due to drinking groundwater contained non-carcinogenic substances (NH4+, NO2-, NO3-, Cd, Cu, F-, Mn, and As) ranged from 5 to 40%, and children had higher risks compared to adults. Additionally, the consumption of As-contaminated groundwater also poses carcinogenic risks for children, female and male adults ranging from 4.80×10-6 to 1.33×10-4. The findings of this study can provide helpful information for policymakers in the development of long-term water management strategies to protect community health.

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Section: ) Principal Component Analysis (Pca)mentioning

confidence: 99%

Groundwater Quality Assessment Using Groundwater Quality Index and Multivariate Statistical Methods and Human Health Risk Assessment in a Coastal Region of the Vietnamese Mekong Delta

2022

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“…The principal component analysis results revealed that 11 PCs contributed significantly and explained 90.7% of the total variation in surface water quality in Dong Thap province in 2019 (Table 2). For the extraction of each component in the PCA analysis, the eigenvalue coefficient was used as a criterion to determine the load or importance level of each component [45]. PC1 and PC2 contributed, respectively, 17.5% and 13.9% of surface water quality variation while PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10, and PC11 contributed 10.4%, 9.5%, 7.7%, 7%, 6.9%, 5.1%, 4.9%, 4.6%, and 3.4%, respectively.…”

Section: Key Water Variables Influencing Water Quality In the Water Bodies In Dong Thap Province In 2019mentioning

confidence: 99%

Spatiotemporal Analysis of Surface Water Quality in Dong Thap Province, Vietnam Using Water Quality Index and Statistical Approaches

Giao

Anh

Nhien

2021

Water

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The study was conducted to spatiotemporally analyze the quality, location and critical water variables influencing water quality using water monitoring data from the Department of Environment and Natural Resources, Dong Thap province in 2019. The water quality parameters including turbidity, pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), total nitrogen (TN), orthophosphate (P-PO43−), chloride (Cl−), oil and grease, sulfate (SO42−), coliforms, and Escherichia coli (E. coli) were collected at 58 locations with the frequency of four times per year (February, May, August, and November). These parameters were compared with national technical regulation on surface water quality—QCVN 08-MT: 2015/BTNMT. Water quality index (WQI) was calculated and spatially presented by geographical information system (GIS) tool. Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the correlation among water quality parameters, group and reduce the sampling sites, and identify key parameters and potential water pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, coliforms, and E. coli were the significant concerns impairing the water quality. Water quality was assessed from poor to medium levels by WQI analysis. CA suggested that the current monitoring locations could be reduced from 58 sites to 43 sites which can be saved the total monitoring budget up to 25.85%. PCA showed that temperature, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, TN, P-PO43−, coliforms, and E. coli were the key water parameters influencing water quality in Dong Thap province’s canals and rivers; thus, these parameters should be monitored annually. The water pollution sources were possibly hydrological conditions, water runoff, riverbank erosion, domestic and urban activities, and industrial and agricultural discharges. Significantly, the municipal and agricultural wastes could be decisive factors to the change of surface water quality in the study area. Further studies need to focus on identifying sources of water pollution for implementing appropriate water management strategies.

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“…In the study area, the main source for the availability of water for regular activity and agricultural purposes is groundwater. Though 90% of groundwater is used for irrigation purposes, about threequarters of total groundwater is consumed for agricultural purposes (Harun, et al, 2015;Kale, et al, 2020). The present study…”

Section: Introductionmentioning

confidence: 84%

Assessment of Groundwater Quality in parts of Jalna district of Maharashtra, India using Water Quality Index

Deshpande¹,

Bhagwat²,

Aher³

2021

JWEAM

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In the present study, fifty-five groundwater samples were collected from the parts of the Jalna district of Maharashtra, India. The water samples were analyzed to determine various physic-chemical parameters like pH, electrical conductivity, total dissolved solids, total hardness, alkalinity, chloride, sulphate, calcium, magnesium, sodium and potassium. The groundwater’s geochemical control and hydrogeochemistry were evaluated using the Piper plots and water quality index (WQI). The hydrochemical analysis reveals that the groundwater is predominantly of CaNaHCO3 type, Water quality index (WQI) indicates that 33% and 2% of groundwater samples fall in poor and very poor categories for drinking purposes. This study reveals that groundwater quality is unfit for drinking purposes at a few places. The effective leaching, dissolution process and rock-water interaction process are the main sources for degrading the groundwater quality.

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Factor analysis and spatial distribution of water quality parameters of Aurangabad District, India.

Cited by 27 publications

References 14 publications

Groundwater Quality Assessment Using Groundwater Quality Index and Multivariate Statistical Methods and Human Health Risk Assessment in a Coastal Region of the Vietnamese Mekong Delta

Groundwater Quality Assessment Using Groundwater Quality Index and Multivariate Statistical Methods and Human Health Risk Assessment in a Coastal Region of the Vietnamese Mekong Delta

Spatiotemporal Analysis of Surface Water Quality in Dong Thap Province, Vietnam Using Water Quality Index and Statistical Approaches

Assessment of Groundwater Quality in parts of Jalna district of Maharashtra, India using Water Quality Index

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