Microtrace Metalloids Speciation in Lakes Water Samples (Poland)

Niedzielski, Przemysław

doi:10.1007/s10661-006-1498-2

Cited by 10 publications

(1 citation statement)

References 11 publications

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“…As(V) concentration (0.97 ± 0.33) μg·L -1 dominated over As(III) concentration (0.37 ± 0.13) μg·L . Marked As(V) prevalence in water reservoirs under oxidizing conditions was highlighted by many authors [21,22,33].…”

Section: Arsenic Distribution and Other Chemical Parameters Of Watermentioning

confidence: 99%

Multivariate Statistical Analyses on Arsenic Occurrence in Rybnik Reservoir

Widziewicz¹,

Loska²

2012

Archives of Environmental Protection

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Popular statistical techniques, such as Spearman's rank correlation matrix, principal component analysis (PCA) and multiple linear regression analysis were applied to analyze a large set of water quality data of the Rybnik Reservoir generated during semiannual monitoring. Water samples collected at 9 sampling sites located along the main axis of the reservoir were tested for 14 selected parameters: concentrations of co-occurring elements, ions and physicochemical parameters. The aim of this study was to estimate the impact of those parameters on inorganic arsenic occurrence in Rybnik Reservoir water by means of multivariate statistical methods. The spatial distribution of arsenic in Rybnik Power Station reservoir was also included. Inorganic arsenic As(III), As(V) concentrations were determined by hydride generation method (HG-AAS) using SpectrAA 880 spectrophotometer (Varian) coupled with a VGA-77 system for hydride generation and ECT-60 electrothermal furnace. Spearman's rank correlation matrix was used in order to fi nd existing correlations between total inorganic arsenic (As Tot ) and other parameters. The results of this analysis suggest that As was positively correlated with PO 4 3-; Fe and TDS. PCA confi rmed these observations. Principal component analysis resulted in three PC's explaining 57% of the total variance. Loading values for each component indicate that the processes responsible for As release and distribution in Rybnik Reservoir water were: leaching from bottom sediments together with other elements like Cu, Cd, Cr, Pb, Zn, Ni, Ca (PC1) and co-precipitation with PO 4 3-, Fe and Mn (PC3) regulated by physicochemical properties like T and pH (PC2). Finally, multiple linear regression model has been developed. This model incorporates only 8 (T, pH, PO 4 3-,Fe, Mn, Cr, Cu, TDS) out of initial 14 variables, as the independent predictors of total As contamination level. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex environmental data sets.

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Section: Arsenic Distribution and Other Chemical Parameters Of Watermentioning

confidence: 99%

Multivariate Statistical Analyses on Arsenic Occurrence in Rybnik Reservoir

Widziewicz¹,

Loska²

2012

Archives of Environmental Protection

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Selenium, Se

Pilarczyk¹,

Tomza–Marciniak²,

Pilarczyk³

et al. 2019

Mammals and Birds as Bioindicators of Trace Element Contaminations in Terrestrial Environments

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The relative sensitivity of freshwater species to antimony(III): Implications for water quality guidelines and ecological risk assessments

Obiakor

Tighe

Wang

et al. 2017

Environ Sci Pollut Res

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Antimony (Sb) is a pollutant in many jurisdictions, yet its threat to aquatic biota is unclear. Water quality guidelines (WQGs) for Sb are not well established and large uncertainty factors are commonly applied in derivation. We constructed freshwater species sensitivity distributions (SSDs) for Sb(III) using available acute toxicity data sourced from temperate and tropical regional studies. A tiered ecological risk assessment (ERA) approach using risk quotients (RQs) was applied for characterisation of risks presented by Sb(III) concentrations measured in the freshwater environment. Multiple parametric models were fitted for each SSD, with the optimal model used to derive the 5% hazardous concentration (HC5), defined as protective of 95% of species, and the corresponding predicted no effect concentration (PNEC). The HC5 values for whole and temperate SSDs were estimated at 781 and 976 μg L Sb(III), respectively, while the PNECs for both datasets were 156 and 195 μg L Sb(III), respectively. Due to limited tropical data, a temperate-to-tropic extrapolation factor of 10 was used to estimate an interim PNEC for tropical regions of 20 μg L Sb(III). Based on published freshwater Sb(III) concentration values across a range of locations, potential ecological risks posed by Sb(III) in some freshwater systems studied would be classified as medium to high risk, but the majority of locations sampled would fall into the low ecological risk category. Our results facilitate the understanding of toxic effects of Sb(III) to freshwater species but also demonstrate that data for Sb ERA are extremely limited.

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Microtrace Metalloids Speciation in Lakes Water Samples (Poland)

Cited by 10 publications

References 11 publications

Multivariate Statistical Analyses on Arsenic Occurrence in Rybnik Reservoir

Multivariate Statistical Analyses on Arsenic Occurrence in Rybnik Reservoir

Selenium, Se

The relative sensitivity of freshwater species to antimony(III): Implications for water quality guidelines and ecological risk assessments

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