Distribution and bioavailability of arsenic in natural waters of a mining area studied by ultrafiltration and diffusive gradients in thin films - Gontijo, Erik S.J. [UNESP]; Watanabe, Cláudia H. [UNESP]; Monteiro, Adnívia S.C. [UNESP]; Tonello, Paulo S. [UNESP]; da Silva, Gilmare A.; Friese, Kurt; Roeser, Hubert M.P.; Rosa, Andre H. [UNESP] (Elsevier, 2016)
The distribution of metals and metalloids among particulate, dissolved, colloidal, free, and labile forms in natural waters is of great environmental concern since it determines their transportation behaviour and bioavailability. Organic matter can have an important role for this distribution process, since it is an important complexing agent and ubiquitous in the aquatic environment. We studied the distribution, mobility and bioavailability of Al, As and Fe in natural waters of a mining area (Quadrilátero Ferrífero, Brazil) and the influence of organic matter in these processes. Water samples were taken from 12 points during the dry and rainy seasons, filtrated at 0.45 μm and ultrafiltrated (<1 kDa) to separate the particulate, colloidal and free fractions. Diffusive gradients in thin films (DGT) were deployed at 5 sampling points to study the labile part of the elements. Total and dissolved organic carbon and the physicochemical parameters were measured along with the sampling. The results of ultrafiltration (UF) and DGT were compared. The relationship among the variables was studied through multivariate analysis (Kohonen neural network), which showed that the seasonality did not impact most of the samples. Fe and Al occurred mainly in the particulate fraction whereas As appeared more in the free fraction. Most of the dissolved Fe and Al were inert (colloidal form) while As was more labile and bioavailable. The results showed that sampling points with a higher quantity of complexed Fe (colloidal fraction) showed less labile As, which may indicate formation of ternary complexes among organic matter, As and Fe.
Keywords: Dissolved organic matter; Humic substances; Kohonen neural network; Metal speciation; Multivariate analysis