Carbon footprints of tailings dams' disasters: A study in the Brumadinho region (Brazil).

Tailings dams' breaks are environmental disasters with direct and intense degradation of soil. This study analyzed the impacts of B1 tailings dam rupture occurred in the Ribeirão Ferro-Carvão watershed (Brumadinho, Brazil) in January 25, 2019. Soil organic carbon (SOC) approached environmental degradation. The analysis encompassed wetlands (high-SOC pools) located in the so-called Zones of Decreasing Destructive Capacity (DCZ5 to DCZ1) defined along the Ferro-Carvão's stream bed and banks after the disaster. Remote sensed water indices were extracted from Landsat 8 and Sentinel-2 satellite images spanning the 2017-2021 period and used to distinguish the wetlands from other land covers. The annual SOC was extracted from the MapBiomas repository inside and outside the DCZs in the same period, and assessed in the field in 2023. Before the dam collapse, the DCZs maintained stable levels of SOC, while afterwards they decreased substantially reaching minimum values in 2023. The reductions were abrupt: for example, in the DCZ3 the decrease was from 51.28 ton/ha in 2017 to 4.19 ton/ha in 2023. Besides, the SOC increased from DCZs located near to DCZs located farther from the dam site, a result attributed to differences in the percentages of clay and silt in the tailings, which also increased in the same direction. The Ferro-Carvão stream watershed as whole also experienced a slight reduction in the average SOC levels after the dam collapse, from nearly 43 ton/ha in 2017 to 38 ton/ha in 2021. This result was attributed to land use changes related with the management of tailings, namely opening of accesses to remove them from the stream valley, creation of spaces for temporary deposits, among others. Overall, the study highlighted the footprints of tailings dams' accidents on SOC, which affect not only the areas impacted with the mudflow but systemically the surrounding watersheds. This is noteworthy.

Scenarios of environmental deterioration in the Paraopeba River, in the three years after the breach of B1 tailings dam in Brumadinho (Minas Gerais, Brazil).

The collapse of B1 dam at the Córrego do Feijão mine of Vale, S.A., located in the Ferro-Carvão stream watershed (Brazil), released 11.7 Mm3 of tailings rich in iron and manganese, and 2.8 Mm3 entered the Paraopeba River 10 km downstream. Seeking to predict the evolution of environmental deterioration in the river since the dam break on January 25, 2019, the present study generated exploratory and normative scenarios based on predictive statistical models, and proposed mitigating measures and subsides to ongoing monitoring plans. The scenarios segmented the Paraopeba into three sectors: "anomalous" for distances ≤63.3 km from the B1 dam site, "transition" (63.3-155.3 km), and "natural" (meaning unimpacted by the mine tailings in 2019; >155.3 km). The exploratory scenarios predicted a spread of the tailings until reaching the "natural" sector in the rainy season of 2021, and their containment behind the weir of Igarapé thermoelectric plant located in the "anomalous" sector, in the dry season. Besides, they predicted the deterioration of water quality and changes to the vigor of riparian forests (NDVI index) along the Paraopeba River, in the rainy season, and a restriction of these impacts to the "anomalous" sector in the dry season. The normative scenarios indicated exceedances of chlorophyll-a in the period January 2019-January 2022, but not exclusively caused by the rupture of B1 dam as they also occurred in areas not affected by the accident. Conversely, the manganese exceedances clearly flagged the dam failure, and persist. The most effective mitigating measure is likely the dredging of the tailings in the "anomalous" sector, but currently it represents solely 4.6 % of what has entered the river. Monitoring is paramount to update the scenarios until the system enters a route towards rewilding, and must include water and sediments, the vigor of riparian vegetation, and the dredging.

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.

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.

A partial least squares-path model of causality among environmental deterioration indicators in the dry period of Paraopeba River after the rupture of B1 tailings dam in Brumadinho (Minas Gerais, Brazil).

This study investigated the collapse of B1 mine-tailings dam that occurred in 25 January 2019 and severely affected the Brumadinho region (Minas Gerais state, Brazil) socially, economically and environmentally. As regards water resources, the event impacted the Paraopeba River in the first 155.3 km counted from the dam site, meaning nearly half the main water course downstream of B1. In the impacted sector, high concentrations of tailings-related Al, Fe, Mn, P in river sediment-tailings mixtures and water were detected, as well as changes to the reflectance of riparian forests. In the river water, the metal concentrations raised significantly above safe levels. For caution, the water management authorities declared immediate suspension of Paraopeba River as drinking water source to the Metropolitan Region of Belo Horizonte (6 million people), irrespective of representing nearly 30% of all supply. In this study, the main purpose was to assess potential links between tailings distribution, river water composition and reflectance of forest vegetation, which worked out as latent variables in regression models. The latent variables were represented by numerous physical and chemical parameters, measured 4 times in 22 sites during the dry period of 2019. The modeling results suggested the release of aluminum and phosphorus from sand fractions in the mine tailings as major cause of water contamination. The NDVI changes were interpreted as environmental deterioration. Changes in redox potential may have raised manganese concentrations in surface water further affecting the forest NDVI. Distance from the B1 dam and dissolved calcium appear to attenuate deterioration. Overall, the regressions allowed robust prognoses of environmental deterioration in the Paraopeba River under low flow conditions. More importantly, they can be transposed to similar dam ruptures helping environmental authorities to decide upon measures that can bring the affected rivers to pre-rupture conditions.

Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures.

The inadequate management of soils and the absence of conservation practices favor the degradation of pastures and can trigger adverse environmental alterations and damage under the terms of Brazilian Federal Law no. 6.938/1981. Based on this premise, this study aimed to estimate soil losses caused by water erosion in pasture areas using the brightness index (BI) from the annual series of Landsat 8 images in different geological formations. A specifically prepared Google Earth Engine (GEE) script automatically extracted the BI from the images. The study occurred in the Environmental Protection Area (EPA) of Uberaba River basin (Minas Gerais, Brazil). To accomplish the goal, 180 digital 500-wide random buffers were selected from 3 geologic types (60 points per type), and then analyzed for zonal statistics of USLE (Universal Soil Loss Equation) soil loss and BI in a Geographic Information System. The regression models BI versus USLE soil loss allowed estimating BI soil losses over the pastures of EPA. The model fittings were remarkable. The validation of soil loss maps in the EPA occurred in pasture phytophysiognomies through the probing of penetration resistance in 37 randomly selected locations. The results were satisfactory, mostly those based on the BI. The BI losses increased for greater resistances. Amplified losses also occurred in regions exposed to environmental land use conflicts (actual uses that deviate from land capability or natural use). Overall, the BI approach proved efficient to accurately track soil losses and pasture degradation over large areas, with the advantage of standing on a single parameter easily accessed through remote sensed data. From an environmental standpoint, this is an important result, because the accurate diagnosis and prognosis of degraded pastures is paramount to implement mitigation measures following the "polluter pays principle", even more in Brazil where the areas occupied by degraded pastures are enormous.