Multivariate mixing and mass balance (M3) calculations, a new tool for decoding hydrogeochemical information

Laaksoharju, Marcus; Skårman, Christina; Skårman, Erik

doi:10.1016/s0883-2927(99)00024-4

Cited by 150 publications

(58 citation statements)

References 4 publications

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“…The PCs were identified with eigen value >1.0 and factor loading matrix was calculated on the basis of the variables. The application of multivariate statistical technique assists to simplify and organize large data sets by data reduction and interpretation of the variables [29]- [31]. The multivariate statistical techniques were performed on experimental data standardized through z-scale transformation in order to avoid misclassification emerged from wide differences in the dimension of data both numerical values and variance of the variables [32] [33].…”

Section: Statistical Treatment Of Data and Multivariate Analysismentioning

confidence: 99%

Assessment of Heavy Metals in Deep Groundwater Resources of the Kathmandu Valley, Nepal

Shrestha¹,

Rijal²,

Pokhrel³

2016

JEP

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A study was carried out to address distribution of some heavy metals in deep groundwater resources of the Kathmandu Valley. Groundwater samples were analyzed for pH, ORP, EC, iron, manganese, zinc, and arsenic in 41 deep groundwater wells during pre monsoon and post monsoon seasons for two consecutive years. The study showed elevated concentrations of iron and manganese in the groundwater of the valley. The occurrence of elevated concentrations of arsenic was also exhibited and observed up to 0.160 mg/L. The spatial distribution patterns demonstrated elevated levels of EC, iron, manganese, zinc, and arsenic in central groundwater district (CGWD) of the valley. The monitored parameters except ORP are not significantly correlated with studied time series, inferring similar distribution of the metals. Correlation analysis and principal component analysis (PCA) were performed to find out relationships among examined parameters and metals. The ORP has strong negative correlations with iron, manganese, and arsenic, suggesting reductive mobilization mechanism of the metals in the groundwater. PCA results showed that iron and manganese with high positive loading factors were due to common natural source of origin of these metals in the groundwater, while negative loading factors of pH and ORP indicated that iron and manganese mobilization was favorable in low pH and reducing environment. Cluster analysis (CA) evidenced high mineralization in most of the wells in the CGWD.

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Section: Statistical Treatment Of Data and Multivariate Analysismentioning

confidence: 99%

Assessment of Heavy Metals in Deep Groundwater Resources of the Kathmandu Valley, Nepal

Shrestha¹,

Rijal²,

Pokhrel³

2016

JEP

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“…To investigate the source of CRL groundwaters (mixing scenarios), the computer code M3 (multivariate mixing and mass-balance calculations) [49][50][51] was used to analyze and evaluate geochemical datasets of groundwater samples from the Chalk River site. The model compared the geochemistry and possible mixing relationships between end member (source) waters for each sampled borehole and interval.…”

Section: Groundwater Origin and Evolution Modelsmentioning

confidence: 99%

Synthesis of Geochemical and Fracture Mineral Studies Relevant to a Deep Geological Repository for Nonfuel Wastes at Chalk River

King-Sharp¹,

Frape²,

Peterman³

et al. 2016

CNL Nuclear Review

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“…The variables and the axis are weighted in proportion to the amount of the total variance that they describe [19]. Often the first two principal components generally describe most of the information in the data set [20]. PCA results (Figure 5(a), Figure 5(b)) show that most of ions contributes significantly to the factorial axis F1.…”

Section: Nomentioning

confidence: 99%

Evaluation of Water Resources Quality in Sabodala Gold Mining Region and Its Surrounding Area (Senegal)

Mall¹,

Mor²,

Maguette³

et al. 2015

JWARP

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Geochemical and Geostatistical tools were used to assess: 1) the chemical quality and, geochemical processes in crystalline rock aquifers in Sabodala (Eastern Senegal) and its surroundings and 2) the impact of mining activities on their quality. A total of 26 water samples collected at boreholes, dug wells and stream, were analyzed to determine major and trace elements concentration focused on elements that represent more threats on human health. Boxplots define chemical characteristics of water for each aquifer formation compared to surface waters. Geostatistical analysis show two sources of water mineralization with regard to major elements: a first natural source characterized by Ca-Mg-HCO3 water type from boreholes and unpolluted surface water and a second group characterized by polluted water by nitrates with Na-NO3-Cl type mainly observed in upper weathered aquifers. However, considering trace element, geostatistical analysis showed three water groups: water with very low trace element concentrations encountered in boreholes and unpolluted surface waters, and waters with relatively high trace element concentrations such as Al observed in areas affected by gold mining activities and finally, polluted waters by Ni, Co, Mn and Cr observed at Sabodala. Results show that in eastern Senegal well waters are vulnerable and often affected by pollution.

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Multivariate mixing and mass balance (M3) calculations, a new tool for decoding hydrogeochemical information

Cited by 150 publications

References 4 publications

Assessment of Heavy Metals in Deep Groundwater Resources of the Kathmandu Valley, Nepal

Assessment of Heavy Metals in Deep Groundwater Resources of the Kathmandu Valley, Nepal

Synthesis of Geochemical and Fracture Mineral Studies Relevant to a Deep Geological Repository for Nonfuel Wastes at Chalk River

Evaluation of Water Resources Quality in Sabodala Gold Mining Region and Its Surrounding Area (Senegal)

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