A partial least squares-path model of environmental degradation in the Paraopeba River, for rainy seasons after the rupture of B1 tailings dam, Brumadinho, Brazil

• Autor(a) principal: Mendes, Rafaella Gouveia
• Data de Publicação: 2022
• Outros Autores: do Valle Junior, Renato Farias, de Melo Silva, Maytê Maria Abreu Pires, de Morais Fernandes, Gabriel Henrique, Fernandes, Luís Filipe Sanches, Fernandes, António Carlos Pinheiro, Pissarra, Teresa Cristina Tarlé [UNESP], de Melo, Marília Carvalho, Valera, Carlos Alberto, Pacheco, Fernando António Leal
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.scitotenv.2022.158248; http://hdl.handle.net/11449/242192
• topic: Dam break; iron-ore waste; Metal(oid)s; NDVI changes; River contamination; Structural equation modeling

The present study aimed to investigate the rupture of B1 tailings dam of Córrego do Feijão mine, which drastically affected the region of Brumadinho (Minas Gerais, Brazil). The contamination of water resources reached 155.3 km from the dam site. In the river channel, high concentrations of Mn, Al, As and Fe were detected and correlated to the spillage of the tailings in the river. The presence of the tailings also affected the chlorophyll-a content in the water, as well as the reflectance of riparian forests. With the increase of metal(oid) concentrations above permitted levels, water management authorities suspended the use of Paraopeba River as resource in the impacted areas, namely the drinking water supply to the Metropolitan region of Belo Horizonte. This study aimed to evaluate possible links between tailings distribution, river water quality, and environmental degradation, which worked as latent variables in partial least squares regression models. The latent variables were represented by numerous physical and chemical parameters of water and sediment, measured four times in 22 locations during the rainy season of 2019, in addition to stream flow and to NDVI evaluated in satellite images processed daily. The modeling results suggested a relationship between river flow turbulence and increased arsenic release from sand fractions, as well as desorption of Mn from metal oxides, both representing causes of water quality reduction. They also revealed increasing iron concentrations affecting the forest NDVI (greening), which was interpreted as environmental degradation. The increase of chlorophyll-a concentrations (related with turbidity decreases), as well as the increase of river flows (responsible for dilution effects), seemed to work out as attenuators of degradation. Although applied to a specific site, our modeling approach can be transposed to equivalent dam failures and climate contexts, helping water resource management authorities to decide upon appropriate recovery solutions.