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The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.

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The objective of this work was to investigate the interaction of arsenic species (As(III) and As(V)) with tropical peat. Peat samples collected in Brazil were characterized using elemental analysis and 13C NMR. Adsorption experiments were performed using different concentrations of As with peat in natura and enriched with Fe or Al, at three different pH levels. Peat samples, in natura or enriched with metals, were analysed before and after adsorption processes using Fourier transform infrared spectroscopy (FTIR) spectroscopy. The adsorption kinetics was evaluated, and the data were fitted using the Langmuir and Freundlich models. The results showed that interaction between As and peat was dependent on the levels of organic matter (OM) and the metals (Fe and Al). As(III) was not adsorbed by in natura peat or Al-enriched peat, although small amounts of As(III) were adsorbed by Fe-enriched peat. Adsorption of As(V) by the different peat samples ranged from 21.3 to 52.7 µg g(-1). The best fit to the results was obtained using the pseudo-second-order kinetic model, and the adsorption of As(V) could be described by the Freundlich isotherm model. The results showed that Fe-enriched peat was most effective in immobilizing As(V). FTIR analysis revealed the formation of ternary complexes involving As(V) and peat enriched with metals, suggesting that As(V) was associated with Al or Fe-OM complexes by metal bridging.

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This paper deals with the development and optimization of an analytical procedure using ultrafiltration and a flow-injection system, and its application in in-situ experiments to characterize the lability and availability of metal species in humic-rich hydrocolloids. The on-line system consists of a tangential flow ultrafiltration device equipped with a 3-kDa filtration membrane. The concentration of free ions in the filtrate was determined by atomic-absorption spectrometry, assuming that metals not complexed by aquatic humic substances (AHS) were separated from the complexed species (M-AHS) retained by the membrane. For optimization, exchange experiments using Cu(II) solutions and AHS solutions doped with the metal ions Ni(II), Mn(II), Fe(III), Cd(II), and Zn(II) were carried out to characterize the stability of the metal-AHS complexes. The new procedure was then applied in-situ at a tributary of the Ribeira do Iguape river (Iguape, São Paulo State, Brazil) and evaluated using the ions Fe(III) and Mn(II), which are considered to be essential constituents of aquatic systems. From the exchange between metal-natural organic matter (M-NOM) and the Cu(II) ions it was concluded that Cu(II) concentrations >485 microg L(-1) were necessary to obtain maximum exchange of the complexes Mn-NOM and Fe-NOM, corresponding to 100% Mn and 8% Fe. Moreover, the new analytical procedure is simple and opens up new perspectives for understanding the complexation, transport, stability, and lability of metal species in humic-rich aquatic environments.

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This work involved the development and application of a new analytical procedure for in-situ characterization of the lability of metal species in aquatic systems by using a system equipped with a diffusion membrane and cellulose organomodified with p-aminobenzoic acid groups (DM-Cell-PAB). To this end, the DM-Cell-PAB system was prepared by adding cellulose organomodified with p-aminobenzoic acid groups (Cell-PAB) to pre-purified cellulose bags. After the DM-Cell-PAB system was sealed, it was examined in the laboratory. The in-situ application involved immersing the DM-Cell-PAB system in two different rivers, enabling us to study the relative lability of metal species (Cu, Cd, Fe, Mn, and Ni) as a function of time and quantity of exchanger. The procedure is simple and opens up a new perspective for understanding environmental phenomena relating to the complexation, transport, stability, and lability of metal species in aquatic systems rich in organic matter.

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