Assessment of Gualaxo do Norte River water quality (Minas Gerais, Brazil) affected by the dam breach of Fundão utilizing exploratory multivariate techniques.

At the end of 2015, the Fundão dam belonging to the Samarco S.A. mining company was ruptured, releasing a flood of mud into the Gualaxo do Norte River, which advanced into the Doce River. The aim of the present study was to apply exploratory multivariate approaches to water quality data obtained during sampling campaigns at the Gualaxo do Norte River during the dry and rainy seasons, between July 2016 and June 2017. A total of 27 locations along the river were sampled, covering unaffected areas and regions influenced by the tailings waste from the dam. Determinations of chemical, physical, and microbiological water quality parameters were performed. Application of principal component analysis (PCA) resulted in the first two components together explaining 39.49% and 37.91% of the total variance for the dry and rainy season data, respectively. In both cases, the PCA groups were related to variables such as turbidity and total solids, which both presented higher values in regions affected by the mud flow. These results are in agreement with those obtained by the Kohonen neural network method, where two-dimensional maps confirmed the samples according to the affected and unaffected area by the disaster.

Speciation and quantification of Hg in sediments contaminated by artisanal gold mining in the Gualaxo do Norte River, Minas Gerais, SE, Brazil.

The Iron Quadrangle in SE Brazil was, in the eighteenth century, one of the most important Au producing regions of Brazil. In this region, gold is produced, even today, by artisanal methods that use Hg to increase the extraction efficiency with no control of Hg release to water systems and the atmosphere. In this context, the Gualaxo do Norte River is of particular interest; its springs are located in the Doce River basin, an important Brazilian basin that supplies water for 3.5 million people. The main goal of this work was to quantify and speciate the Hg in the sediments of the Gualaxo do Norte River using a direct mercury analyzer and gas chromatography-pyrolysis-atomic fluorescence detection system. Statistical analyses consisted of principal component analysis, aiming to assess interactions among elements and species and to group the variables in factors affecting the properties of sediment. The results show that total Hg (THg) and methylmercury (CH3Hg+) concentrations in samples ranged from 209 to 1207 µg kg-1 and from 0.07 to 1.00 µg kg-1, respectively (methylation percentages from 0.01 to 0.27%). Thermal desorption analysis showed that mercury is mainly present in the oxidized form, and correlation analyses pointed to a relationship between THg and MnO, indicating that manganese can oxidize and/or adsorb Hg. Together, MO and CH3Hg+ are important parameters in the third principal component, indicating the influence of OM on the methylation process. This first investigation on Hg methylation in this small-scale gold mining area points to the possibility of Hg bioaccumulation and to the need of better understanding the biogeochemical cycle of Hg in this area. Samples were collected in 2012, prior to the 2015 Fundão Dam disaster. The results are also a record of the characteristics of the sediment prior to that event.

Short-term hydromorphological and ecological responses of using woody structures for river restoration in a tailing-impacted tropical river.

Gualaxo do Norte River (GNR), in southeastern Brazil, was impacted by iron ore tailings from the Fundão Dam rupture (November 2015). The deposition of tailings on the riverbed has changed the hydrogeomorphological characteristics of the GNR, resulting in a decrease in the diversity of physical habitats and ecological biodiversity. As part of the process of restoration and management of this damaged ecosystem, the river restoration project ReNaturalize was implemented to restructure the geomorphological characteristics and the physical habitat and to enhance the reestablishment of biota, mainly for macroinvertebrates and fishes. For this goal, 203 wooden structures, such as tree trunks, branches, and grass were installed in two sections of GNR (T6R and T7R), totaling 1.8 km long. The effectiveness of the project was evaluated by an assessment that followed a before and after and control and impacted (BACI) design. Upstream of each Restored reach there is a Control and a Reference reach. Four campaigns were carried out, two before and two after the restoration process. After 14 months of the woody installation, an increase in hydraulic retention in the restored reaches was observed (T6R-20.2%; T7R-63.5%), when compared with the Control reaches, which favored the accumulation of sediments (T6R-388 metric tons; T7R-396 metric tons). This enhanced the formation of natural tailings barriers and promoted the enrichment of substrate types (T6R-39.2%; T7R-43%). The benthic macroinvertebrate community showed an increase in the total abundance (T6-110%), including the most sensitive groups (T6R-124%; T7R-124%). For fish, the increase was up to 81.38% with hand nets capture, indicating the recruitment of juveniles, and the abundance and the biomass of some species were also higher (up to 100%) than the Control reaches. The results indicated that the Restored reach is already qualitatively and quantitatively better than the Control reach and similar to the Reference reach, indicating the success of the study. Integr Environ Assess Manag 2023;19:648-662. © 2022 SETAC.

A model based on a multivariate classification for assessing impacts on water quality in a DOCE river watershed after the Fundão tailings dam failure.

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.