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23 pages, 27814 KiB  
Article
Investigation of the Origin of Elevated Amounts of Iron and Manganese in a Dam Reservoir
by Maria Michalopoulou, Nikolaos Depountis, Eleni Zagana and Pavlos Avramidis
Geosciences 2024, 14(12), 336; https://doi.org/10.3390/geosciences14120336 - 8 Dec 2024
Viewed by 459
Abstract
On the outskirts of the Pinios dam reservoir (Ilia Regional Unit, Greece), a water treatment plant serves the water supply needs of the surrounding municipalities, in which high concentrations of Fe and Mn, before treatment, have been observed. The main purpose of this [...] Read more.
On the outskirts of the Pinios dam reservoir (Ilia Regional Unit, Greece), a water treatment plant serves the water supply needs of the surrounding municipalities, in which high concentrations of Fe and Mn, before treatment, have been observed. The main purpose of this research was to investigate the mechanism of increased iron (Fe) and manganese (Mn) levels in the reservoir of the Pinios dam, which impacts its water treatment plant operation. A wide range of hydrochemical and sedimentological analyses were conducted over a hydrological year, focusing on the spatial and temporal distribution of Fe and Mn in both water and sediment samples across the established research monitoring stations. Sediment samples from the reservoir’s bottom revealed predominantly fine-grained material, rich in total organic carbon, with elevated Mn and Fe oxide levels. Significant seasonal variations in Fe and Mn levels were also discovered, with higher Mn levels observed in the anoxic bottom waters of the reservoir during the dry season, attributed to the reduced conditions favoring Mn oxide dissolution over Fe. Conversely, during the wet season, a homogenization of metal concentrations throughout the water column was observed due to increased oxygenation and freshwater inflow. These outcomes were confirmed by the hydrochemical analysis, indicating that the redox conditions, pH, and temperature, as well as the presence of organic matter, significantly influence the mobility and bioavailability of these metals in the reservoir. The findings of this study clarify that the high concentration of Fe and Mn can be linked to the mineral composition of the upstream Neogene and flysch formations in the study area. As these formations are affected by geological weathering, they tend to enrich the streams, through soil erosion and runoff processes, with metals like Fe and Mn, which are eventually transported into the dam reservoir. This study highlights the critical influence of lithological, sedimentological, and hydrological factors on the redox conditions and seasonal stratification that govern the behavior of Fe and Mn concentrations and mobility in dam reservoirs. These findings are critical for informing water resource management practices and dam infrastructure operators and developing effective environmental conservation strategies in similar cases. Full article
(This article belongs to the Section Geochemistry)
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Figure 1
<p>Study area with the prevailing geological formations.</p>
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<p>Temporal variation in manganese and iron concentration for the period 2016 to 2017.</p>
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<p>Sediment and water column sampling locations in the reservoir.</p>
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<p>Runoff water sampling locations.</p>
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<p>Triangular grain size classification diagram of the analyzed sediments. The asterisks represent analyzed sediments.</p>
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<p>Grain size percentage and statistical parameters of the reservoir sediments.</p>
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<p>Bar charts of TOC/TN/TP of the reservoir sediments.</p>
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<p>Bar charts of the major elements of the reservoir sediments.</p>
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<p>Variation in MnO and Fe<sub>2</sub>O<sub>3</sub> of the reservoir sediments.</p>
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<p>The spatial distribution of (<b>a</b>) MnO and (<b>b</b>) Fe<sub>2</sub>O<sub>3</sub> of the reservoir sediments.</p>
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<p>Distribution of physicochemical parameters in the water column (9 October 2018). Station P7: (<b>a</b>) DO (mg/L), (<b>b</b>) T (°C), and (<b>c</b>) pH. Station P3: (<b>d</b>) DO (mg/L), (<b>e</b>) T(°C), and (<b>f</b>) pH.</p>
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<p>Distribution of physicochemical parameters in the water column (22 February 2019). Station P7: (<b>a</b>) DO (mg/L), (<b>b</b>) T (°C), and (<b>c</b>) pH. Station P3: (<b>d</b>) DO (mg/L), (<b>e</b>) T (°C), and (<b>f</b>) pH.</p>
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<p>Chemical analyses of the runoff water samples from the Pinios River network (Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup>, HCO<sub>3</sub><sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, Cl<sup>−</sup>, and NO<sub>3</sub><sup>−</sup>).</p>
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<p>Chemical analyses of the runoff water samples from the Pinios River network (K<sup>+</sup>, NO<sub>2</sub><sup>−</sup>, NH<sub>4</sub><sup>+</sup>, PO<sub>4</sub><sup>3−</sup>, Fe<sub>tot</sub>, and Mn).</p>
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<p>Chemical analyses of the runoff water samples from the Pinios River network (pH, temperature (°C), and conductivity at 25 °C).</p>
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<p>Spatial distribution of Mn (<b>a</b>–<b>c</b>) and Fe<sub>tot</sub> (<b>d</b>–<b>f</b>) of the reservoir’s surface water samples. Sampling dates: (<b>a</b>,<b>d</b>) 4 June 2018, (<b>b</b>,<b>e</b>) 9 October 2018, and (<b>c</b>,<b>f</b>) 22 February 2019.</p>
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<p>Variation in manganese and iron concentration (μg/L) of the reservoir water samples on (<b>a</b>) 4 June 2018, (<b>b</b>) 9 October 2018, and (<b>c</b>) 22 February 2019 in contrast with the Fe and Mn influx from the surrounding tributaries on 30 January 2019 and (<b>d</b>) 22 February 2019 in contrast with the Fe and Mn influx from the surrounding tributaries on 22 February 2019.</p>
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<p>Variation in manganese and iron concentration (μg/L) of the reservoir water samples on 9 October 2018, (<b>a</b>) Fe<sub>tot</sub> and (<b>b</b>) Mn; and 22 February 2019, (<b>c</b>) Fe<sub>tot</sub> and (<b>d</b>) Mn.</p>
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16 pages, 5541 KiB  
Article
Resilience or Collapse? Reconstructing the Water Quality Time Series of a Tropical River Impacted by a Mine Tailings Dam Failure
by Anelise Rodrigues Machado Garcia, Diego Guimarães Florencio Pujoni and José Fernandes Bezerra-Neto
Limnol. Rev. 2024, 24(4), 637-652; https://doi.org/10.3390/limnolrev24040037 - 6 Dec 2024
Viewed by 519
Abstract
The 2015 Fundão tailings dam collapse in Mariana, Brazil, was a major environmental catastrophe. Assessing its long-term effects on water quality is critical for environmental restoration and policy development. In this study, we reconstructed a 15-year time series of five water quality parameters [...] Read more.
The 2015 Fundão tailings dam collapse in Mariana, Brazil, was a major environmental catastrophe. Assessing its long-term effects on water quality is critical for environmental restoration and policy development. In this study, we reconstructed a 15-year time series of five water quality parameters to assess whether the collapse caused permanent changes. Using public data from the Minas Gerais Water Institute (IGAM), we fitted generalized additive models for location, scale, and shape to model long-term trends in turbidity, total solids, conductivity, pH, and dissolved oxygen. Predictor variables included daily precipitation and smooth functions for time and longitudinal distance along the river. As expected, turbidity and total solids increased sharply after the collapse; however, the mean values returned to pre-collapse levels within four years. Conductivity, which was already elevated pre-collapse, remained high following the passage of the tailings plume. Although we observed a tendency toward pre-collapse values, the long-term conductivity mean did not fully stabilize to previous levels. No clear patterns were observed for pH or dissolved oxygen. This study highlights the acute impact of the dam collapse on five water quality parameters in the Doce River and illustrates the river’s subsequent stabilization process, although other important and chronic impacts are still persistent. Long-term studies such as this provide valuable insights into the dynamics of fluvial systems. Full article
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<p>Map of the Doce River Basin with IGAM and INMET sampling stations.</p>
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<p>Flowchart of the methodology.</p>
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<p>Scatter plots of the accumulated rainfall over different accumulated intervals with principal component 1 scores (PC1). Pearson correlation, <span class="html-italic">p</span>-values and the line representing the regression slope are indicated.</p>
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<p>(<b>A</b>)—Time series of the 30-day accumulated rainfall (average of the four INMET stations); the points represent the measured values. (<b>B</b>)—Long-term trend estimated by the GAMLSS model. The solid line represents the average, and the shaded area represents the standard error. The red vertical line represents the collapse date, and the horizontal line represents the average for the period. (<b>C</b>)—Seasonality across the four periods. The solid black line indicates the monthly median for that period.</p>
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<p>(<b>A</b>)—Time series of the flow from Governador Valadares station, the points represent the measured values. (<b>B</b>)—Long-term trend estimated by the GAMLSS model. The solid line represents the average and the shaded area represents the standard error. The red vertical line represents the date of the collapse, and the horizontal line represents the average for the period. (<b>C</b>)—Seasonality across the four periods, the solid black line indicates the monthly median for that period.</p>
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<p>(<b>A</b>)—Reconstructed time series of total solids. For clarity, the plot shows the data observed only from a single sampling station (RD035). The black dots represent the observed values, and the grey lines represent the fitted values from the 50 bootstrap samples. (<b>B</b>)—Long-term trend estimated by the GAMLSS model. The solid line and the shaded area represent the mean and the standard error, respectively. The red vertical line marks the collapse date, and the horizontal line represents the average for Period 1. (<b>C</b>)—Seasonality across the four periods. The solid black line indicates the monthly median for that period.</p>
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<p>(<b>A</b>)—Reconstructed time series of turbidity. For clarity, the plot shows the data observed only from a single sampling station (RD035). The black dots represent the observed values, and the grey lines represent the fitted values from the 50 bootstrap samples. (<b>B</b>)—Long-term trend estimated by the GAMLSS model. The solid line and the shaded area represent the mean and the standard error, respectively. The red vertical line marks the collapse date, and the horizontal line represents the average for Period 1. (<b>C</b>)—Seasonality across the four periods. The solid black line indicates the monthly median for that period.</p>
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<p>(<b>A</b>)—Reconstructed time series of conductivity. For clarity, the plot shows the data observed only from a single sampling station (RD035). The black dots represent the observed values, and the grey lines represent the fitted values from the 50 bootstrap samples. (<b>B</b>)—Long-term trend estimated by the GAMLSS model. The solid line and the shaded area represent the mean and the standard error, respectively. The red vertical line marks the collapse date, and the horizontal line represents the average for Period 1. (<b>C</b>)—Seasonality across the four periods. The solid black line indicates the monthly median for that period.</p>
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<p>(<b>A</b>)—Reconstructed time series of pH. For clarity, the plot shows the data observed only from a single sampling station (RD035). The black dots represent the observed values, and the grey lines represent the fitted values from the 50 bootstrap samples. (<b>B</b>)—Long-term trend estimated by the GAMLSS model. The solid line and the shaded area represent the mean and the standard error, respectively. The red vertical line marks the collapse date, and the horizontal line represents the average for Period 1. (<b>C</b>)—Seasonality across the four periods. The solid black line indicates the monthly median for that period.</p>
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<p>(<b>A</b>)—Reconstructed time series of dissolved oxygen. For clarity, the plot shows the data observed only from a single sampling station (RD035). The black dots represent the observed values, and the grey lines represent the fitted values from the 50 bootstrap samples. (<b>B</b>)—Long-term trend estimated by the GAMLSS model. The solid line and the shaded area represent the mean and the standard error, respectively. The red vertical line marks the collapse date, and the horizontal line represents the average for Period 1. (<b>C</b>)—Seasonality across the four periods. The solid black line indicates the monthly median for that period.</p>
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22 pages, 23991 KiB  
Article
Conceptual and Applied Aspects of Water Retention Tests on Tailings Using Columns
by Fernando A. M. Marinho, Yuri Corrêa, Rosiane Soares, Inácio Diniz Carvalho and João Paulo de Sousa Silva
Geosciences 2024, 14(10), 273; https://doi.org/10.3390/geosciences14100273 - 16 Oct 2024
Viewed by 751
Abstract
The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability [...] Read more.
The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities. Full article
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<p>(<b>a</b>) Ore-pile draining and (<b>b</b>) water content variation along the pile.</p>
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<p>Relationship between the water content and the amount of fines.</p>
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<p>(<b>a</b>) Physical model of a soil column with a water table (<b>b</b>) Relationships between free energy and water content in a soil column with a fixed water table (<b>c</b>) Variation of water content with the height of the column (modified from Edlefesen and Anderson [<a href="#B7-geosciences-14-00273" class="html-bibr">7</a>]).</p>
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<p>Suction (<b>a</b>) and water content (<b>b</b>) profile in the field.</p>
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<p>(<b>a</b>) PVC column; (<b>b</b>) schematic drawing of the column; (<b>c</b>) suction equilibrium profile, and (<b>d</b>) water content profiles for three hypothetical materials [<a href="#B15-geosciences-14-00273" class="html-bibr">15</a>].</p>
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<p>Soil water retention curve of the material (data from Jesus et al. [<a href="#B22-geosciences-14-00273" class="html-bibr">22</a>]).</p>
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<p>Segments for the column assembly.</p>
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<p>Drainage segment. Placement of (<b>a</b>) gravel and (<b>b</b>) medium sand.</p>
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<p>Column compaction process: (<b>a</b>) Details of the compaction; (<b>b</b>) column at its 6th segment.</p>
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<p>First completed column: (<b>a</b>) Image of the completed column; (<b>b</b>) sensor positions.</p>
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<p>Time lag graphical analysis between sensors WC6 and TE6 during (<b>a</b>) saturation, (<b>b</b>) drainage, (<b>c</b>) infiltration, and (<b>d</b>) evaporation.</p>
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<p>Stages imposed in the columns.</p>
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<p>Profiles at the end of construction and before saturation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during saturation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during drainage: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Responses of the TE6 (<b>a</b>) and WC6 (<b>b</b>) sensors to the first infiltration and evaporation.</p>
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<p>Responses of the TE6 (<b>a</b>) and WC6 (<b>b</b>) sensors to the second infiltration and evaporation.</p>
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<p>Profiles during the first infiltration: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during the first evaporation: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during the second infiltration: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during the second evaporation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Measured water flux at the base of the column.</p>
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<p>A closer look at the sensor readings plotted with the retention curve.</p>
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<p>Water retention curve with the sensor readings.</p>
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<p>SWRC versus infiltration and evaporation data.</p>
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13 pages, 1914 KiB  
Article
Pre- and Postnatal Vitamin A Deficiency Impairs Motor Skills without a Consistent Effect on Trace Mineral Status in Young Mice
by Joseph Arballo, Jennifer M. Rutkowsky, Marjorie J. Haskell, Kyla De Las Alas, Reina Engle-Stone, Xiaogu Du, Jon J. Ramsey and Peng Ji
Int. J. Mol. Sci. 2024, 25(19), 10806; https://doi.org/10.3390/ijms251910806 - 8 Oct 2024
Viewed by 1363
Abstract
Pregnant women and children are vulnerable to vitamin A deficiency (VAD), which is often compounded by concurrent deficiencies in other micronutrients, particularly iron and zinc, in developing countries. The study investigated the effects of early-life VAD on motor and cognitive development and trace [...] Read more.
Pregnant women and children are vulnerable to vitamin A deficiency (VAD), which is often compounded by concurrent deficiencies in other micronutrients, particularly iron and zinc, in developing countries. The study investigated the effects of early-life VAD on motor and cognitive development and trace mineral status in a mouse model. C57BL/6J dams were fed either a vitamin A-adequate (VR) or -deficient (VD) diet across two consecutive gestations and lactations. Offspring from both gestations (G1 and G2) continued the same diets until 6 or 9 weeks of age. Behavioral assays were conducted to evaluate motor coordination, grip strength, spatial cognition, and anxiety. Hepatic trace minerals were analyzed. A VD diet depleted hepatic retinoids and reduced plasma retinol across all ages and gestations. Retracted rear legs and abnormal gait were the most common clinical manifestations observed in VD offspring from both gestations at 9 weeks. Poor performance on the Rotarod test further confirmed their motor dysfunction. VAD didn’t affect hemoglobin levels and had no consistent effect on hepatic trace mineral concentrations. These findings highlight the critical role of vitamin A in motor development. There was no clear evidence that VAD alters the risk of iron deficiency anemia or trace minerals. Full article
(This article belongs to the Special Issue The Role of Trace Elements in Nutrition and Health)
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Figure 1
<p>Effect of early-life vitamin A deficiency on growth and development of mouse offspring. The postweaning body weight change (<b>A</b>), body weight (<b>B</b>), brain weight (<b>C</b>), brain-to-body weight ratio (<b>D</b>), intestinal length (<b>E</b>), and tail length (<b>F</b>) of 9-week-old offspring born from the first gestation (G1, n = 6–12 mice/(sex∙treatment)). Figures (<b>G</b>–<b>L</b>) show the corresponding growth parameters of 6-week-old offspring born from the second gestation (G2, n = 6–10 mice/(sex∙treatment)), respectively. Figures (<b>M</b>–<b>R</b>) show the corresponding growth parameters of 9-week-old offspring born from the G2 (n = 6–9 mice/treatment), respectively. VR, vitamin A-adequate diet; VD, vitamin A-free diet. Data present LS means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Effect of early-life vitamin A deficiency on blood hemoglobin concentration. Figures (<b>A</b>–<b>C</b>) show the hemoglobin concentrations in 9-week-old offspring born from the first gestation (G1, n = 7–12 mice/(sex∙treatment)) and 6-week (n = 6–10 mice/(sex∙treatment)) and 9-week-old offspring (n = 6–9 mice/treatment) born from the second gestation (G2), respectively. VR, vitamin A-adequate diet; VD, vitamin A-free diet. Data present LS means ± SEM.</p>
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<p>Effect of early-life vitamin A deficiency on hepatic trace mineral concentrations. Figures (<b>A</b>–<b>E</b>) show hepatic trace mineral concentrations in 9-week-old offspring born from the first gestation (G1, n = 7–12 mice/(sex∙treatment)). Figures (<b>F</b>–<b>J</b>) and figures (<b>K</b>–<b>O</b>) show hepatic trace mineral concentrations in 6-week-old (n = 6–10 mice/(sex∙treatment)) and 9-week-old (n = 6–9 mice/treatment) offspring born from the second gestation (G2), respectively. VR, vitamin A-adequate diet; VD, vitamin A-free diet. Data present LS means ± SEM. ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effect of early-life vitamin A deficiency on motor function assessed through Rotarod test. Rotarod test performance of 9-week-old offspring born from the first gestation (G1, n = 5 mice/treatment) was assessed based on the highest speed of rotation (<b>A</b>), travel distance (<b>B</b>), and travel duration (<b>C</b>). Figures (<b>D</b>–<b>F</b>) and figure (<b>G</b>–<b>I</b>) show corresponding test parameters for 6- (n = 5–6/treatment) and 9-week-old (n = 6–8 mice/treatment) offspring born from the second gestation (G2), respectively. VR, vitamin A-adequate diet; VD, vitamin A-free diet. Data present LS means ± SEM. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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14 pages, 2555 KiB  
Article
Application of SAP to Improve the Handling Properties of Iron Ore Tailings of High Cohesiveness: Could a Reagent Help the Decommissioning Process of a Dam?
by Hely Simões Gurgel and Ivo André Homrich Schneider
Mining 2024, 4(4), 733-746; https://doi.org/10.3390/mining4040041 - 2 Oct 2024
Viewed by 1105
Abstract
This work aims to evaluate the use of a superabsorbent polymer (SAP) to provide improvements in the handling properties of iron ore tailings (IOT). The material studied came from the magnetic separation reprocessing of the material discarded at the Gelado Dam, located in [...] Read more.
This work aims to evaluate the use of a superabsorbent polymer (SAP) to provide improvements in the handling properties of iron ore tailings (IOT). The material studied came from the magnetic separation reprocessing of the material discarded at the Gelado Dam, located in Serra dos Carajás in the state of Pará, Brazil. While the concentrate presents reasonable handling conditions, the tailings, with 61.5% iron, 15% moisture, and 39% of the mass, have high cohesiveness and adhesiveness due to their fine nature and the climatic conditions of the Amazon rainforest. However, the tailings can still be considered a product as long as the handling and transportation logistics are feasible. Thus, studies with an SAP and IOT were carried out in a bench rotating drum to promote mixing between them, and the main variables studied were the SAP dosage and the required contact time. The improvement in the physical properties of the IOT were evaluated considering the Hausner ratio, Carr index, Jenike’s flow function index, Atterberg limits, and chute angle. The superabsorbent polymer promoted a significant improvement in the state of consistency of the material, and the best performance was obtained with a dosage of 1000 g t−1. As long as a suitable contact condition was promoted, a contact time of 1 min was enough to achieve the expected benefits. After dosing with the superabsorbent polymer, the material’s handling classification changed from ‘cohesive’ to ‘easy flow’, and the chute angle was reduced from 90° to levels below 60°. It was concluded that the application of the superabsorbent polymer has the potential to improve the fluidity of the material discarded in the magnetic concentration operation, allowing it to be handled throughout the production and transportation chain. The SAP appears to be an important additive for the full use of the material present in the dam (100% recovery), with both economic and socio-environmental benefits. Full article
(This article belongs to the Special Issue Envisioning the Future of Mining, 2nd Edition)
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<p>Location of the Gelado Dam in Brazil.</p>
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<p>Flowchart of the operations involved in the reprocessing of iron ore waste deposited at the Gelado Dam and a simplified mass balance.</p>
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<p>(<b>a</b>) Rotating drum applied to SAP/IOT interaction; (<b>b</b>) images of IOT before (<b>left</b>) and after (<b>right</b>) the application of the SAP; (<b>c</b>) scanning electron microscope microphotographs of the SAP; (<b>d</b>) photograph of the SAP.</p>
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<p>Grain size distribution of the iron ore tailing resulting from the process of magnetic concentration from Gelado dam.</p>
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<p>Atterberg limits of the iron ore tailings of the Gelado DAM as a function of the SAP dosage.</p>
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<p>Chute angle as a function of the normal pressure of the iron ore tailings of the Gelado Dam with and without the SAP.</p>
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<p>Transportation logistics of IOT from the Gelado Dam to the consumer market.</p>
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20 pages, 1236 KiB  
Article
Photo-Fenton Treatment under UV and Vis Light Reduces Pollution and Toxicity in Water from Madín Dam, Mexico
by Deysi Amado-Piña, Rubi Romero, Emmanuel Salazar Carmona, Armando Ramírez-Serrano, Leobardo Manuel Gómez-Oliván, Gustavo Elizalde-Velázquez and Reyna Natividad
Catalysts 2024, 14(9), 620; https://doi.org/10.3390/catal14090620 - 14 Sep 2024
Viewed by 950
Abstract
Water from Madín Dam in Mexico has been shown to contain a wide variety of pollutants such as drugs, pesticides, personal care products and compounds that are released into the environment as waste from production processes. In this work, the effect of the [...] Read more.
Water from Madín Dam in Mexico has been shown to contain a wide variety of pollutants such as drugs, pesticides, personal care products and compounds that are released into the environment as waste from production processes. In this work, the effect of the main process variables on the percentage of total organic carbon (TOC) removal in water samples from the Madín reservoir was studied by applying a photo-Fenton treatment catalyzed with iron-pillared clays. The catalyst was characterized by XRD, N2 physisorption, DRS and XPS. The sampling and characterization of the water from the Madín reservoir was carried out according to Mexican standards. The system for treatment tests was 0.1 L of reaction volume and a controlled temperature of 23–25 °C, and the reaction system was kept under constant stirring. After 4 h of treatment time under UV light, the TOC removal was 90%, and it was 60% under Vis light. The main ROS involved in the photo-Fenton process driven by UVC light were hydroxyl radicals, while hydroperoxyl radicals predominate in the Vis-light-driven process. Evidence of superoxide anion participation was not found. The toxicity of untreated and treated water was assessed on Danio rerio specimens, and it was observed to be reduced after the photo-Fenton treatment. Full article
(This article belongs to the Section Photocatalysis)
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Figure 1
<p>Diffractograms of Fe-PILC and bentonite clay.</p>
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<p>Total organic carbon removal efficiency in water samples from Madín reservoir through different treatment processes. Reaction volume: 0.1 L, T: 25 C, pH<sub>o</sub>: 6.02, treatment time: 60 min, UV light: 254 nm, 166 W/m<sup>2</sup>, Vis light: (3 lamps, 100 W/m<sup>2</sup> each).</p>
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<p>Effect of initial oxygen presence (photo-Fenton-N<sub>2</sub>) and addition of radical scavengers (isopropanol, IPA; benzoquinone, BQ) to the photo-Fenton system under (<b>a</b>) UVC light (254 nm, 166 W/m<sup>2</sup>) and (<b>b</b>) Vis light (3 lamps, 100 W/m<sup>2</sup> each). Reaction conditions: volume: 0.1 L, T: 25 °C, pH<sub>o</sub>: 6.02, catalyst loading (W<sub>cat</sub>): 0.500 g/L, reaction time: 60 min for processes in (<b>a</b>) and 240 min for processes in (<b>b</b>).</p>
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<p>Location of the water sampling stations inside the Madín Dam: New Madín (1), Old Madín (2), entrance of the Tlalnepantla River (3), entrance of the San Juan River (4), dam curtain (5).</p>
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24 pages, 4316 KiB  
Article
Profile of Bacterial Communities in Copper Mine Tailings Revealed through High-Throughput Sequencing
by Joseline Jiménez-Venegas, Leonardo Zamora-Leiva, Luciano Univaso, Jorge Soto, Yasna Tapia and Manuel Paneque
Microorganisms 2024, 12(9), 1820; https://doi.org/10.3390/microorganisms12091820 - 3 Sep 2024
Viewed by 1855
Abstract
Mine-tailing dumps are one of the leading sources of environmental degradation, often with public health and ecological consequences. Due to the complex ecosystems generated, they are ideal sites for exploring the bacterial diversity of specially adapted microorganisms. We investigated the concentrations of trace [...] Read more.
Mine-tailing dumps are one of the leading sources of environmental degradation, often with public health and ecological consequences. Due to the complex ecosystems generated, they are ideal sites for exploring the bacterial diversity of specially adapted microorganisms. We investigated the concentrations of trace metals in solid copper (Cu) mine tailings from the Ovejería Tailings Dam of the National Copper Corporation of Chile and used high-throughput sequencing techniques to determine the microbial community diversity of the tailings using 16S rRNA gene-based amplicon sequence analysis. The concentrations of the detected metals were highest in the following order: iron (Fe) > Cu > manganese (Mn) > molybdenum (Mo) > lead (Pb) > chromium (Cr) > cadmium (Cd). Furthermore, 16S rRNA gene-based sequence analysis identified 12 phyla, 18 classes, 43 orders, 82 families, and 154 genera at the three sampling points. The phylum Proteobacteria was the most dominant, followed by Chlamydiota, Bacteroidetes, Actinobacteria, and Firmicutes. Genera, such as Bradyrhizobium, Aquabacterium, Paracoccus, Caulobacter, Azospira, and Neochlamydia, showed high relative abundance. These genera are known to possess adaptation mechanisms in high concentrations of metals, such as Cd, Cu, and Pb, along with nitrogen-fixation capacity. In addition to their tolerance to various metals, some of these genera may represent pathogens of amoeba or humans, which contributes to the complexity and resilience of bacterial communities in the studied Cu mining tailings. This study highlights the unique microbial diversity in the Ovejería Tailings Dam, including the discovery of the genus Neochlamydia, reported for the first time for heavy metal resistance. This underscores the importance of characterizing mining sites, particularly in Chile, to uncover novel bacterial mechanisms for potential biotechnological applications. Full article
(This article belongs to the Special Issue Advances in Soil Microbial Ecology)
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<p>Ovejería Tailings Dam, CODELCO Division. (<b>A</b>) Location of Chile in South America. (<b>B</b>) Location of Santiago, the capital city of Chile (Metropolitan Region). (<b>C</b>) Tiltil County. (<b>D</b>) Location of Ovejería Tailings Dam and sampling points (indicated by yellow stars and numbers). In areas where the surface of the tailing dam is hatched, the dam wall is shown in orange. For illustrative purposes, the red arrow indicates one of the operating points where fresh tailings enter into the Ovejería Tailings Dam.</p>
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<p>Alpha-diversity plots among the sample points in the Ovejería Tailings Dam. (<b>A</b>) Chao1, (<b>B</b>) Pielou, (<b>C</b>) Shannon, and (<b>D</b>) Simpson indices (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Relative abundances of bacteria per sampled point (<b>A</b>) at the phylum level, (<b>B</b>) at the family level, and (<b>C</b>) at the genus level.</p>
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<p>(<b>A</b>) Venn diagram showing the number of common bacterial genera between three different zones belonging to the Ovejería Tailings Dam. (<b>B</b>) List of common bacterial genera regardless of the variable. (<b>C</b>) List of common bacterial genera between points. (*) Bacterial genera marked with asterisks have the highest relative abundance on average at the three points (&gt;1.0%). (A-N-P-R) <span class="html-italic">Allorhizobium–Neorhizobium–Pararhizobium–Rhizobium</span>.</p>
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<p>Spearman correlation between the relative abundances of 27 common genera and the concentrations of seven metals (total and available concentrations) across the three sampling points (P1, P2, and P3). The X-axis represents metals, while the Y-axis represents genera. Color intensity indicates the strength of the correlation, with positive correlations in red and negative correlations in blue. Significant correlations are highlighted with (*).</p>
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15 pages, 5434 KiB  
Article
Evaluating the Behavior of Bauxite Tailings Dewatering in Decanter Centrifuges
by Camila Botarro Moura, Arthur Pinto Chaves, Rafael Alves de Souza Felipe and Homero Delboni Júnior
Minerals 2024, 14(8), 827; https://doi.org/10.3390/min14080827 - 15 Aug 2024
Cited by 1 | Viewed by 956
Abstract
Depending on the ore quality, a washing process can be conducted with the bauxite, which basically consists of scrubbing the ore and screening in order to increase the available alumina grade, i.e., the alumina extractable using the Bayer Process, and reduce the impurity [...] Read more.
Depending on the ore quality, a washing process can be conducted with the bauxite, which basically consists of scrubbing the ore and screening in order to increase the available alumina grade, i.e., the alumina extractable using the Bayer Process, and reduce the impurity content. Tailings are usually disposed of in a tailings dam in the form of a slurry, which is a mixture of solid particles and liquid, consisting mainly of ultra-fine kaolinite, making the dewatering operation challenging. To reduce the environmental impact, mining companies are studying alternative methods to dewater the tailings, and different dewatering methods are available worldwide. The use of new technologies to dewater the tailings has contributed to facing the challenges of achieving sustainable development with their disposal. The decanter centrifuges are already an option for operations for the Canadian oil sands, gold ore in Peru, and nickel in New Caledonia; they are also being tested for iron ore in Brazil. In the present work, bauxite dewatering using the decanter centrifuge was evaluated to understand more about the behavior of these materials and to investigate the effects of various process parameters on the solid recovery and solid content of the flows, using three different kinds of equipment. The results indicated that decanter centrifuges can be used to achieve a high concentration of solids in the cake, with values ranging from 60% to 80% solids per weight and a great clarification in the liquid phase (centrate) from 0 to 6% solids per weight, values which mean the solid phase is suitable for reutilization in the processing circuit. Additionally, the present work provides a better understanding of how different solid contents feed can affect the behavior of the equipment. Full article
(This article belongs to the Section Mineral Processing and Extractive Metallurgy)
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<p>Number of academic publications concerning different impact types in the aluminum industry, 2023 [<a href="#B5-minerals-14-00827" class="html-bibr">5</a>].</p>
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<p>Cut-away of solid bowl decanter centrifuge [<a href="#B7-minerals-14-00827" class="html-bibr">7</a>].</p>
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<p>Laboratory centrifuge.</p>
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<p>Process of pilot plant.</p>
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<p>Sampling points in the pilot plant.</p>
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<p>The Marcy scale.</p>
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<p>Particle size distribution.</p>
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<p>Quick determination of the settled solids using 1 L graduated cylinder.</p>
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<p>Results of spin test.</p>
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<p>Tests results of centrifuge 1.</p>
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<p>Tests results of centrifuge 2.</p>
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<p>Tests results of centrifuge 3.</p>
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<p>Comparison between feed solid content.</p>
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<p>Pile of dewatered solids.</p>
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<p>Comparison between cake solid content.</p>
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<p>Comparison between centrate solid contents.</p>
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<p>Recovery of solids.</p>
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17 pages, 4121 KiB  
Article
Susceptibility to Liquefaction of Iron Ore Tailings in Upstream Dams Considering Drainage Conditions Based on Seismic Piezocone Tests
by Giovani C. L. R. da Costa, Guilherme J. C. Gomes and Helena Paula Nierwinski
Appl. Sci. 2024, 14(14), 6129; https://doi.org/10.3390/app14146129 - 14 Jul 2024
Viewed by 1242
Abstract
One of the critical challenges facing the mining sector is related to the prevention and mitigation of catastrophic incidents associated with its tailing dams. As mining tailings are very heterogeneous and field characterization is expensive and complex, geotechnical properties of these materials are [...] Read more.
One of the critical challenges facing the mining sector is related to the prevention and mitigation of catastrophic incidents associated with its tailing dams. As mining tailings are very heterogeneous and field characterization is expensive and complex, geotechnical properties of these materials are largely unknown. The seismic cone penetration test (SCPTu) provides a field approach to estimate a large array of geotechnical information, including the liquefaction potential of tailing dams. Yet, the exploration of strain softening behaviors in geomaterials under undrained loading, utilizing the state parameter (ψ) inferred from SCPTu tests initially applied to soft soils, has been often used for mining tailings. This study is concerned with the implementation of a tailing classification system which uses the ratio between the small strain shear modulus and the cone tip resistance (G0/qt). A series of laboratory tests was executed, and three different methodologies were adopted to assess the effects of (partial) drainage conditions based on 531.26 m of SCPTu measurements conducted at three different upstream iron ore tailing dams in Brazil. Furthermore, the G0/qt ratio is integrated with ψ to assess the liquefaction tendencies of the investigated materials. The findings reveal the heterogeneous nature of the tailings, wherein indications of partial drainage are discernible across numerous records. Liquefaction analyses demonstrate that the tailings exhibit a contractive behavior in over 94% of the SCPTu data, confirming their susceptibility to flow liquefaction. Our findings are relevant for site characterization within iron ore tailing dams and other mining sites with similar geotechnical attributes. Full article
(This article belongs to the Special Issue Geotechnical Engineering and Infrastructure Construction)
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<p>Classification methodology adapted from Nierwinski, Schnaid and Odebrecht [<a href="#B28-applsci-14-06129" class="html-bibr">28</a>,<a href="#B29-applsci-14-06129" class="html-bibr">29</a>,<a href="#B30-applsci-14-06129" class="html-bibr">30</a>], clearly distinguishing between plastic and non-plastic soils while simultaneously evaluating drainage conditions.</p>
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<p>Correlation between the q<sub>tD</sub>/q<sub>tUD</sub> ratio and the friction angle (<math display="inline"><semantics> <mrow> <mo>ϕ</mo> </mrow> </semantics></math>) obtained from triaxial tests. The friction angle value can be used to approximate the q<sub>tD</sub>/q<sub>tUD</sub> ratio, consequently facilitating the correction of q<sub>tD</sub> through Equation (4). The shaded region in gray highlights a range of variations documented in the literature. The listed references can be found in Senneset, Sandven and Janbu [<a href="#B35-applsci-14-06129" class="html-bibr">35</a>].</p>
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<p>Study site: Simplified geological map of the Iron Quadrangle (Minas Gerais, Brazil), highlighting mining sites and historical cities. This map was produced based on research by Cavalcanti et al. [<a href="#B38-applsci-14-06129" class="html-bibr">38</a>].</p>
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<p>Typical SPCTu results from Structure 3 showing q<sub>t</sub>, f<sub>s</sub>, u<sub>2</sub>, B<sub>q</sub> and I<sub>c</sub> plotted against depth. The layers from the I<sub>c</sub> graphic stand for: 1—Gravelly sand, 2—Sands: clean to silty, 3—Silty sands to sandy silts, 4—Clayey silt to silty clay, 5—Clays, and 6—Organic soils.</p>
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<p>Particle size distribution curves for the three structures: (<b>a</b>) Structure 1, (<b>b</b>) Structure 2, and (<b>c</b>) Structure 3 under study, revealing that the predominant material composition consists of a combination of sands and silts.</p>
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<p>Mohr circles and the failure envelope derived from a representative triaxial test result. The plot includes the Mohr–Coulomb derived parameters: cohesion (0) and friction angle (37°).</p>
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<p>Results of the classification method proposed by Nierwinski, Schnaid and Odebrecht [<a href="#B28-applsci-14-06129" class="html-bibr">28</a>,<a href="#B29-applsci-14-06129" class="html-bibr">29</a>,<a href="#B30-applsci-14-06129" class="html-bibr">30</a>] applied to the investigated tailings (structures). Ninety-seven percent of the data are situated within the non-plastic zone, implying a prevailing granular characteristic within the tailings.</p>
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<p>Results of the drainage conditions analysis using (<b>a</b>) B<sub>q</sub>, (<b>b</b>) Q<sub>tn</sub> and (<b>c</b>) V parameters. The histogram highlights, within the shaded grey region, the count of records obtained from SCPTu tests indicating partial drainage conditions according to three distinct methodologies.</p>
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<p>SCPTu results are displayed with q<sub>t</sub> plotted against standard velocity, followed by the application of drainage corrections based on (<b>a</b>) B<sub>q</sub>, (<b>b</b>) Q<sub>tn</sub> and (<b>c</b>) V.</p>
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<p>Comparison of I<sub>c</sub> with q<sub>t</sub> on standard velocity and subsequent application of drainage correction based on (<b>a</b>) B<sub>q</sub>, (<b>b</b>) Q<sub>tn</sub> and (<b>c</b>) V.</p>
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<p>Estimation of state parameters for the iron ore tailings before and after q<sub>t</sub> correction according to B<sub>q</sub>, Q<sub>tn</sub> and V. P limit values are boundaries defined by in situ stresses generally experienced by soils [<a href="#B37-applsci-14-06129" class="html-bibr">37</a>].</p>
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18 pages, 4446 KiB  
Article
Major, Trace and Rare Earth Elements Geochemistry of Bottom Sediments in the Retiro Baixo Reservoir after the B1 Tailings Dam Rupture, Paraopeba River (Brazil)
by Diego S Sardinha, Mateus Sala Pinto, Paulo Henrique Bretanha Junker Menezes, Gunther Brucha, Jéssica Teixeira Silveira, Letícia Hirata Godoy, Deivid Arimatea Saldanha de Melo and Fernando Verassani Laureano
Minerals 2024, 14(6), 621; https://doi.org/10.3390/min14060621 - 18 Jun 2024
Viewed by 1012
Abstract
The rupture of an iron mining tailing dam in Brumadinho, Brazil, released around 10 million cubic meters of tailings, of which 1.6 Mm3 reached the Paraopeba River. In this work, a total of 30 samples from three bottom sediment cores were collected [...] Read more.
The rupture of an iron mining tailing dam in Brumadinho, Brazil, released around 10 million cubic meters of tailings, of which 1.6 Mm3 reached the Paraopeba River. In this work, a total of 30 samples from three bottom sediment cores were collected in the lower course of the Paraopeba River basin and analyzed for major, trace and rare earth elements by ICP-OES and ICP-MS. The sediments presented a range of compositions with different weathering histories, overall marked by depleted Ca2+, Na+ and K+ compared with the average UCC, PAAS and NASC and some advanced weathering trends. The samples presented a fractionation pattern characterized by a continuous depletion of light REEs from La to Sm and a regular decreased distribution of heavy REEs from Gd to Yb, and the Co/Th vs. La/Sc diagram indicates a predominant intermediate source. The upper samples presented the highest contents of REEs, probably due to the higher presence of iron and aluminum oxides and hydroxides, which can be related to more advanced weathering. The Al, Cu, Ni, V, Zn, Co, Mn, Ti, Fe and Si concentrations and the CF, EF and Igeo index values varied across the sediment core samples, demonstrating that there were long periods of geogenic or anthropogenic contributions. Full article
(This article belongs to the Special Issue Chemical Weathering Studies)
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<p>Paraopeba River basin with emphasis on the B1 tailings dam and Retiro Baixo reservoir sampling point locations.</p>
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<p>Paraopeba River basin. (<b>a</b>) Lithological map. (<b>b</b>) Soil map.</p>
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<p>(<b>A</b>) The ternary diagram of the (Al<sub>2</sub>O<sub>3</sub>)/(CaO+Na<sub>2</sub>O)/(K<sub>2</sub>O) oxides, in molecular proportions, for the bottom sediment samples [<a href="#B56-minerals-14-00621" class="html-bibr">56</a>,<a href="#B57-minerals-14-00621" class="html-bibr">57</a>,<a href="#B58-minerals-14-00621" class="html-bibr">58</a>]. (<b>B</b>) The classification of terrigenous sandstones and shales using Log (Fe<sub>2</sub>O<sub>3</sub>/K<sub>2</sub>O) vs. Log (SiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub>) [<a href="#B59-minerals-14-00621" class="html-bibr">59</a>].</p>
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<p>Trace element distribution for each sample normalized to Average Post-Archaean Australian Shale (PAAS) and Average North American Shale Composite (NASC) [<a href="#B46-minerals-14-00621" class="html-bibr">46</a>,<a href="#B53-minerals-14-00621" class="html-bibr">53</a>,<a href="#B55-minerals-14-00621" class="html-bibr">55</a>].</p>
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<p>REE patterns for each sample normalized by the CC1 chondrite [<a href="#B47-minerals-14-00621" class="html-bibr">47</a>,<a href="#B48-minerals-14-00621" class="html-bibr">48</a>].</p>
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<p>(<b>A</b>) REE sum (ΣREE) vs. sample depth. (<b>B</b>) Provenance discrimination diagrams of Co/Th vs. La/Sc.</p>
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<p>Major and trace element concentrations vs. depth of the sediment core.</p>
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28 pages, 7482 KiB  
Article
Coupled Microstructural EBSD and LA-ICP-MS Trace Element Mapping of Pyrite Constrains the Deformation History of Breccia-Hosted IOCG Ore Systems
by Samuel Anthony King, Nigel John Cook, Cristiana Liana Ciobanu, Kathy Ehrig, Yuri Tatiana Campo Rodriguez, Animesh Basak and Sarah Gilbert
Minerals 2024, 14(2), 198; https://doi.org/10.3390/min14020198 - 15 Feb 2024
Cited by 1 | Viewed by 2077
Abstract
Electron backscatter diffraction (EBSD) methods are used to investigate the presence of microstructures in pyrite from the giant breccia-hosted Olympic Dam iron–oxide copper gold (IOCG) deposit, South Australia. Results include the first evidence for ductile deformation in pyrite from a brecciated deposit. Two [...] Read more.
Electron backscatter diffraction (EBSD) methods are used to investigate the presence of microstructures in pyrite from the giant breccia-hosted Olympic Dam iron–oxide copper gold (IOCG) deposit, South Australia. Results include the first evidence for ductile deformation in pyrite from a brecciated deposit. Two stages of ductile behavior are observed, although extensive replacement and recrystallization driven by coupled dissolution–reprecipitation reaction have prevented widespread preservation of the earlier event. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) element maps of pyrite confirm that many pyrite grains display compositional zoning with respect to As, Co, and Ni, but that the zoning is often irregular, patchy, or otherwise disrupted and are readily correlated with observed microstructures. The formation of ductile microstructures in pyrite requires temperatures above ~260 °C, which could potentially be related to heat from radioactive decay and fault displacements during tectonothermal events. Coupling EBSD methods with LA-ICP-MS element mapping allows a comprehensive characterization of pyrite textures and microstructures that are otherwise invisible to conventional reflected light or BSE imaging. Beyond providing new insights into ore genesis and superimposed events, the two techniques enable a detailed understanding of the grain-scale distribution of minor elements. Such information is pivotal for efforts intended to develop new ways to recover value components (precious and critical metals), as well as remove deleterious components of the ore using low-energy, low-waste ore processing methods. Full article
(This article belongs to the Special Issue Microanalysis Applied to Mineral Deposits)
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<p>(<b>A</b>) Generalized stratigraphic column that depicts relationships between basement and cover rocks in the Olympic Dam district and major tectonothermal events, BIF–banded iron formation. (<b>B</b>) Geological map of the district showing major faults and IOCG systems superimposed onto basement geology. Modified after Courtney-Davies et al. [<a href="#B30-minerals-14-00198" class="html-bibr">30</a>].</p>
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<p>Geological sketch map of the Olympic Dam deposit modified after Ehrig et al. [<a href="#B28-minerals-14-00198" class="html-bibr">28</a>] showing zoning with respect to Cu-Fe-sulfide mineralogy: pyrite–chalcopyrite (Py-Ccp), chalcopyrite–bornite (Ccp-Bn) and bornite–chalcocite (Bn-Cc) within the Olympic Dam Breccia Complex (ODBC). Annotations: RDG—Roxby Downs Granite, HEMQ—hematite quartz ± barite breccia.</p>
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<p>Overview reflected light maps (<b>A</b>,<b>B</b>) and scanned images (<b>C</b>,<b>D</b>) of analyzed polished blocks with assemblages and pyrite textures described in <a href="#minerals-14-00198-t001" class="html-table">Table 1</a>. The assemblage in CLC23 is closely analogous to that of CLC6. White boxes shown on the insets mark EBSD maps of pyrite. Abbreviations: Ab—albite, Chl—chlorite, Ccp—chalcopyrite, Hm—hematite, Kfs—K-feldspar, Py—pyrite, Qz—quartz, Sd—siderite, Ser—sericite.</p>
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<p>Reflected light and BSE images of pyrite grains selected for EBSD analysis. (<b>A</b>) Grain impingement in pyrite from sample MV65 with fracturing and pulverization to rock flour with (<b>B</b>,<b>C</b>) BSE images displaying rounded multiphase inclusions within pyrite. (<b>D</b>) Grain rich in healed fractures and inclusions of surrounding gangue from sample MV18B with white box depicting E. (<b>E</b>) Detail of grain from D, showing compositional zoning with respect to Co (white arrows) and presence of fine trails of gangue minerals (black arrows) that seem to offset pyrite zonation patterns. (<b>F</b>) Intact pyrite grain adjacent to a pulverized grain along a rupture in quartz from sample MV37. (<b>G</b>) Sub-idiomorphic highly pulverized pyrite grain from sample CLC6. Abbreviations: Au—native-gold, Cat—cattierite, Ccp—chalcopyrite, Chl—chlorite, Hm—hematite, Py—pyrite, Pyh—pyrrhotite, Qz—quartz, Sd—siderite, Ser—sericite.</p>
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<p>Orientation contrast EBSD maps and data from pyrite in sample MV65 (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>A) (<b>A</b>) BC map with a BSE image inset displaying galena trails of sub-micron thickness at the grain margin. (<b>B</b>) Y-IPF map and corresponding (<b>C</b>,<b>D</b>) {100} pole figures with matching colors displaying pyrite orientations from (<b>C</b>) the outlined pyrite in B and (<b>D</b>) pyrite external to ‘outlined pyrite’ larger than 1000 pixels. Pole figures display two recognizable deformations, rotation about a &lt;100&gt; axis labelled D1, and a shift of all {100} axes labelled D2. Abbreviations: Gn—galena, Py—pyrite.</p>
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<p>Orientation contrast maps and data from pyrite in sample MV18B (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>D). (<b>A</b>) BC map and (<b>B</b>) GROD angle map that displays low- (yellow) and medium-angle (cyan) boundaries and progressive misorientation in pyrite. (<b>C</b>) A closeup of the medium-angle boundary domain that displays a lenticular morphology with corresponding {100} and {110} pole figures from the area. (<b>D</b>) Reflected light micrograph depicting the relationship between Co-As-Ni zonation (white arrows), the lenticular domain and microstructure 1 (yellow dotted line). (<b>E</b>) {100} pole figures taken across microstructures labelled 1, 2, and 3 shown on (<b>B</b>). (<b>F</b>) Transects (shown in (<b>B</b>)) taken across subtle misorientation (orange) and low-angle boundaries (red) show cumulative misorientation across the grain.</p>
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<p>Orientation contrast maps and data from pyrite in sample MV37 (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>F). (<b>A</b>) BC map displaying microstructures, (<b>B</b>) shown at higher magnification in BSE. (<b>C</b>) Y-IPF map displaying microfractures across the right grain with low- (yellow) and medium-angle (cyan) boundaries in the left grain. (<b>D</b>) {100} pole figure displaying rotation about a &lt;100&gt; axis with colors derived from (<b>E</b>) the TC map that shows two unique orientations. The red pyrite apparently impinges upon the blue, with a (<b>F</b>) closeup BSE image displaying quartz and galena trails at this margin. Ccp—chalcopyrite, Gn—galena, Qz—quartz.</p>
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<p>Orientation contrast EBSD maps and data from pyrite in sample CLC6 (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>G). (<b>A</b>) BC, (<b>B</b>) Z-IPF and (<b>C</b>) GROD angle maps depicting spatial misorientation. (<b>D</b>) {100}/{110}/{111} pole figures with colors derived from (<b>B</b>). Two orientations of rotation are displayed in ‘Py1′ (yellow and red on (<b>B</b>)) and shown (<b>E</b>) along transects. The pink axis rotation annotations on (<b>D</b>) correspond to the pink transect on (<b>B</b>,<b>E</b>), whereas the cyan annotations depicting a shift of all axes on (<b>D</b>) reflects the cyan transect on (<b>B</b>,<b>E</b>). ‘Py2′, shown on (<b>B</b>), exhibits rotation about two axis marked a and b with black annotations on (<b>D</b>).</p>
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<p>(<b>A</b>) BC and (<b>B</b>) GROD angle maps with associated (<b>C</b>) {100}, {110} and {111} pole figures of pyrite from sample CLC23. (<b>D</b>) A transect, with its trajectory shown in (<b>B</b>) by a white line, depicts subtle and progressive cumulative misorientation across the grain.</p>
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<p>LA-ICP-MS maps and corresponding reflected light micrograph of pyrite grain from sample MV18B annotated with yellow dashed lines corresponding to microstructures 1, 2, 3, and the lenticular {110} domain shown by EBSD (<a href="#minerals-14-00198-f006" class="html-fig">Figure 6</a>). Pyrite contains patchy zonation with respect to As, and antithetic Ni-Co zonation enclosed by a reaction rim relatively enriched in Co and As marked by a white dashed line. Crosscutting microstructures display elevated Co, As, Te, Au, Bi, Pb, and Ag. Scales in counts-per-second.</p>
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<p>LA-ICP-MS maps and corresponding reflected light micrograph of pyrite grains in sample MV37 annotated with sub angle boundaries shown by EBSD (<a href="#minerals-14-00198-f007" class="html-fig">Figure 7</a>). Pyrite shows patchy zoning with respect to Ni, Co, As, and Au annotated by white dashed lines, with yellow or black dashed lines over microstructures with relative enrichment in Bi and Pb. Scales in counts-per-second; <sup>197</sup>Au in linear scale (n × 10<sup>0</sup>) and <sup>125</sup>Te in linear scale (n × 10<sup>1</sup>).</p>
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<p>LA-ICP-MS maps and corresponding reflected light micrograph of pyrite grain from sample CLC6. Chemical data depicts a relict Ni-rich core, surrounding an oscillatory zonation with respect to Ni, Co, As, Te, and Se and an As-Se-enriched reaction rim. Scales in counts-per-second; <sup>59</sup>Co in linear scale (n × 10<sup>4</sup>) and <sup>77</sup>Se in linear scale (n × 10<sup>2</sup>).</p>
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16 pages, 2512 KiB  
Article
Characterization and Analysis of Iron Ore Tailings Sediments and Their Possible Applications in Earthen Construction
by Sofia Bessa, Marlo Duarte, Gabriela Lage, Isabela Mendonça, Roberto Galery, Rochel Lago, Ana Paula Texeira, Fernando Lameiras and Maria Teresa Aguilar
Buildings 2024, 14(2), 362; https://doi.org/10.3390/buildings14020362 - 29 Jan 2024
Cited by 4 | Viewed by 1693
Abstract
Mineral extraction is of ultimate importance for the economies of different countries, and Brazil is one of the world’s leading producers of iron ores. Unfortunately, dams are still the main problem, mainly in Minas Gerais, especially after the Fundão Dam rupture in 2015. [...] Read more.
Mineral extraction is of ultimate importance for the economies of different countries, and Brazil is one of the world’s leading producers of iron ores. Unfortunately, dams are still the main problem, mainly in Minas Gerais, especially after the Fundão Dam rupture in 2015. Additionally, there is still a massive presence of buildings built on earth throughout the Minas Gerais mining region, built from the 18th century to today. Investigating the potential of iron ore tailings (IOT) to be incorporated into traditional earthen construction techniques in regions affected by dam ruptures presents a relevant and innovative research approach. In addition, the local reuse of these sediments should be the priority. Thus, the main objective of this work was to collect, characterize, and analyze the possibilities of the application of these tailings to produce rammed earth (RE). A complete characterization analysis was performed on the samples collected at three points. To analyze the soil-IOT compatibility, representative mixtures of RE were produced, and the specific mass, compaction, and compressive strength were performed. It was observed that the IOT samples have a high silica content and that the mixtures of IOT–soil, even without cement, reached the compressive strength values of the international standards, or even above them. Full article
(This article belongs to the Special Issue Eco-Friendly Building Materials)
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<p>Location of collection points along the Rio Doce. Source: Google Maps, 2023 (modified).</p>
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<p>Granulometric curves of tailing samples.</p>
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<p>N<sub>2</sub> sorption isotherms for the tailing’s samples.</p>
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<p>X-ray diffractograms of the tailing’s samples.</p>
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<p>Scanning electron microscopy images of tailing’s samples.</p>
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32 pages, 3379 KiB  
Article
The Modeling of a River Impacted with Tailings Mudflows Based on the Differentiation of Spatiotemporal Domains and Assessment of Water–Sediment Interactions Using Machine Learning Approaches
by João Paulo Moura, Fernando António Leal Pacheco, Renato Farias do Valle Junior, Maytê Maria Abreu Pires de Melo Silva, Teresa Cristina Tarlé Pissarra, Marília Carvalho de Melo, Carlos Alberto Valera, Luís Filipe Sanches Fernandes and Glauco de Souza Rolim
Water 2024, 16(3), 379; https://doi.org/10.3390/w16030379 - 23 Jan 2024
Cited by 2 | Viewed by 2087
Abstract
The modeling of metal concentrations in large rivers is complex because the contributing factors are numerous, namely, the variation in metal sources across spatiotemporal domains. By considering both domains, this study modeled metal concentrations derived from the interaction of river water and sediments [...] Read more.
The modeling of metal concentrations in large rivers is complex because the contributing factors are numerous, namely, the variation in metal sources across spatiotemporal domains. By considering both domains, this study modeled metal concentrations derived from the interaction of river water and sediments of contrasting grain size and chemical composition, in regions of contrasting seasonal precipitation. Statistical methods assessed the processes of metal partitioning and transport, while artificial intelligence methods structured the dataset to predict the evolution of metal concentrations as a function of environmental changes. The methodology was applied to the Paraopeba River (Brazil), divided into sectors of coarse aluminum-rich natural sediments and sectors enriched in fine iron- and manganese-rich mine tailings, after the collapse of the B1 dam in Brumadinho, with 85–90% rainfall occurring from October to March. The prediction capacity of the random forest regressor was large for aluminum, iron and manganese concentrations, with average precision > 90% and accuracy < 0.2. Full article
(This article belongs to the Section Water Quality and Contamination)
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Graphical abstract

Graphical abstract
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<p>Map of Paraopeba River basin, with indication of major tributaries, municipality centers, position in the Brazilian state of Minas Gerais, and location of four water and sediment monitoring stations: PT-52 (“upstream”), PT-13, PT-14 (“anomalous”) and PT19 (“natural”), as well as one streamflow station BCF-RL-08, all used in the statistical assessment and artificial intelligence modeling of water–sediment interactions. The shaded areas describe the general precipitation increase from the mouth to the spring areas of the basin. The datasets of streamflow and water and sediment parameters are provided as <a href="#app1-water-16-00379" class="html-app">Supplementary Materials</a>.</p>
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<p>River model proposed to assess water–sediment interactions in large rivers, based on the initial definition of spatial and temporal domains succeeded by the application of an ensemble of statistical and artificial intelligence algorithms and integrated interpretation of their results.</p>
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<p>Boxplot diagrams of: (<b>A</b>) Fe(dis), (<b>B</b>) Al(dis) and (<b>C</b>) Mn(dis) concentrations in the dry and rainy periods of 2019 to 2021, measured at the “upstream” (PT-52), “anomalous” (PT-13 and PT-14) and “natural” (PT-19) monitoring stations.</p>
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<p>Boxplot diagrams of: (<b>A</b>) Fe(tot), (<b>B</b>) Al(tot) and (<b>C</b>) Mn(tot) concentrations in the dry and rainy periods of 2019 to 2021, measured at the “upstream” (PT-52), “anomalous” (PT-13 and PT-14) and “natural” (PT-19) monitoring stations.</p>
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<p>Boxplot diagrams of ambient conditions and contaminants other than Fe, Al and Mn dissolved and total concentrations in the water of Paraopeba River: (<b>A</b>) pH, (<b>B</b>) temperature, (<b>C</b>) turbidity, (<b>D</b>) total arsenic, (<b>E</b>) dissolved lead, (<b>F</b>) total lead, (<b>G</b>) dissolved phosphorus, (<b>H</b>) total phosphorus, in the dry and rainy periods of 2019 to 2021, measured at the “upstream” (PT-52), “anomalous” (PT-13 and PT-14) and “natural” (PT-19) monitoring stations.</p>
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<p>Boxplot diagrams of ambient conditions and contaminants other than Fe, Al and Mn dissolved and total concentrations in the water of Paraopeba River: (<b>A</b>) pH, (<b>B</b>) temperature, (<b>C</b>) turbidity, (<b>D</b>) total arsenic, (<b>E</b>) dissolved lead, (<b>F</b>) total lead, (<b>G</b>) dissolved phosphorus, (<b>H</b>) total phosphorus, in the dry and rainy periods of 2019 to 2021, measured at the “upstream” (PT-52), “anomalous” (PT-13 and PT-14) and “natural” (PT-19) monitoring stations.</p>
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<p>Boxplot diagrams of compartment (E) variables, which are related to the chemical composition of sediments + tailings mixtures: (<b>A</b>) aluminum, (<b>B</b>) arsenic, (<b>C</b>) lead, (<b>D</b>) iron, (<b>E</b>) phosphorus and (<b>F</b>) manganese, in the dry and rainy periods of 2019 to 2021, measured at the “upstream” (PT-52), “anomalous” (PT-13 and PT-14) and “natural” (PT-19) monitoring stations.</p>
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<p>Boxplot diagrams of (F) compartment variables (<a href="#water-16-00379-t001" class="html-table">Table 1</a>), which are related to the granulometric fractions of sediments and tailings: (<b>A</b>) clay, (<b>B</b>) silt, (<b>C</b>) very fine-grained sand, (<b>D</b>) fine-grained sand, (<b>E</b>) sand, (<b>F</b>) coarse-grained sand, (<b>G</b>) very coarse-grained sand. Boxplot diagram of (<b>D</b>) compartment variable: (<b>H</b>) river flow. The dry and rainy periods between 2019 and 2021 were considered, as well as the “upstream” (PT-52), “anomalous” (PT-13 and PT-14) and “natural” (PT-19) monitoring stations.</p>
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<p>Boxplot diagrams of (F) compartment variables (<a href="#water-16-00379-t001" class="html-table">Table 1</a>), which are related to the granulometric fractions of sediments and tailings: (<b>A</b>) clay, (<b>B</b>) silt, (<b>C</b>) very fine-grained sand, (<b>D</b>) fine-grained sand, (<b>E</b>) sand, (<b>F</b>) coarse-grained sand, (<b>G</b>) very coarse-grained sand. Boxplot diagram of (<b>D</b>) compartment variable: (<b>H</b>) river flow. The dry and rainy periods between 2019 and 2021 were considered, as well as the “upstream” (PT-52), “anomalous” (PT-13 and PT-14) and “natural” (PT-19) monitoring stations.</p>
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<p>Spearman’s rank-order correlations between all variables listed in <a href="#water-16-00379-t001" class="html-table">Table 1</a>, computed at the “upstream” (panels (<b>A</b>,<b>B</b>)) and “anomalous” PT-13 (<b>C</b>,<b>D</b>) monitoring stations, in the dry and rainy periods.</p>
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<p>Principal component analysis (biplots) of all parameters listed in <a href="#water-16-00379-t001" class="html-table">Table 1</a>: (<b>A</b>) “upstream” station in the dry period; (<b>B</b>) “upstream” station in the rainy period; (<b>C</b>) “anomalous” PT-13 station in the dry period; (<b>D</b>) “anomalous” PT-13 station in the rainy period.</p>
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<p>Principal component analysis (biplots) of all parameters listed in <a href="#water-16-00379-t001" class="html-table">Table 1</a>: (<b>A</b>) “upstream” station in the dry period; (<b>B</b>) “upstream” station in the rainy period; (<b>C</b>) “anomalous” PT-13 station in the dry period; (<b>D</b>) “anomalous” PT-13 station in the rainy period.</p>
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<p>Mean values of R<sup>2</sup><sub>Adjust</sub> (Equation (1)) relative to the estimates of Al(dis), Fe(dis), Mn(dis), Al(tot), Fe(tot) and Mn(tot) concentrations, at the “upstream” and “anomalous” PT-13 stations and in dry and rainy periods. The terms MLR, MLP and RF designate the multiple linear regression models with stepwise forward selection of variables, multilayer perceptron neural networks and random forest regressor, respectively.</p>
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<p>Time-series of Fe(dis) and Fe(tot) concentrations, as measured (obs in the legend) or estimated (est in the legend) by the random forest regressor model in the “upstream” (panels (<b>A</b>,<b>B</b>)) and “anomalous” PT-13 (panels (<b>C</b>,<b>D</b>)) stations.</p>
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19 pages, 6927 KiB  
Article
Mechanical and Microstructural Response of Iron Ore Tailings under Low and High Pressures Considering a Wide Range of Molding Characteristics
by Giovani Jordi Bruschi, Carolina Pereira Dos Santos, Hugo Carlos Scheuermann Filho, Camila da Silva Martinatto, Luana Rutz Schulz, João Paulo de Sousa Silva and Nilo Cesar Consoli
Mining 2023, 3(4), 712-730; https://doi.org/10.3390/mining3040039 - 18 Nov 2023
Cited by 2 | Viewed by 1415
Abstract
The dry stacking of filtered tailings is an option to deal with safety-related issues involving traditional slurry disposition in impoundments. Filtered tailings can be compacted to pre-define design specifications, which minimizes structural instability problems, such as those related to liquefaction. Yet, comprehending the [...] Read more.
The dry stacking of filtered tailings is an option to deal with safety-related issues involving traditional slurry disposition in impoundments. Filtered tailings can be compacted to pre-define design specifications, which minimizes structural instability problems, such as those related to liquefaction. Yet, comprehending the tailing’s response under various stress states is essential to designing any dry stacking facility properly. Thus, the present research evaluated the mechanical response of cemented and uncemented compacted filtered iron ore tailings, considering different molding characteristics related to compaction degree and molding moisture content. Therefore, a series of one-dimensional compression tests and consolidated isotropically drained triaxial tests (CID), using 300 kPa and 3000 kPa effective confining pressures, were carried out for different specimens compacted at various molding characteristics. In addition, changes in gradation owing to both compression and shearing were evaluated using sedimentation with scanning electron microscope tests. The overall results have indicated that the 3% Portland cement addition enhanced the strength and stiffness of the compacted iron ore tailings, considering the lower confining pressure. Nevertheless, the same was not evidenced for the higher confining stress. Moreover, the dry-side molded specimens were initially stiffer, and significant particle breakage did not occur owing to one-dimensional compression but only due to shearing (triaxial condition). Full article
(This article belongs to the Topic Mining Innovation)
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<p>Compaction characteristics of the iron ore tailings and molding points.</p>
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<p>SEM micrographs of the iron ore tailings magnified by (<b>a</b>) 100× and (<b>b</b>) 500×; (<b>c</b>) 5000×.</p>
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<p>One-dimensional compression tests (<b>a</b>) uncemented and (<b>b</b>) cemented specimens.</p>
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<p>Triaxial stress–strain data and volume change response: (<b>a</b>) <span class="html-italic">p</span>’<sub>0</sub> = 300 kPa, uncemented; (<b>b</b>) <span class="html-italic">p</span>’<sub>0</sub> = 300 kPa, cemented; (<b>c</b>) <span class="html-italic">p</span>’<sub>0</sub> = 3000 kPa, uncemented; (<b>d</b>) <span class="html-italic">p</span>’<sub>0</sub> = 3000 kPa, cemented.</p>
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<p>Triaxial stress–strain data and volume change response: (<b>a</b>) <span class="html-italic">p</span>’<sub>0</sub> = 300 kPa, uncemented; (<b>b</b>) <span class="html-italic">p</span>’<sub>0</sub> = 300 kPa, cemented; (<b>c</b>) <span class="html-italic">p</span>’<sub>0</sub> = 3000 kPa, uncemented; (<b>d</b>) <span class="html-italic">p</span>’<sub>0</sub> = 3000 kPa, cemented.</p>
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<p>Triaxial stress–strain data and volume change response: (<b>a</b>) <span class="html-italic">p</span>’<sub>0</sub> = 300 kPa, uncemented; (<b>b</b>) <span class="html-italic">p</span>’<sub>0</sub> = 300 kPa, cemented; (<b>c</b>) <span class="html-italic">p</span>’<sub>0</sub> = 3000 kPa, uncemented; (<b>d</b>) <span class="html-italic">p</span>’<sub>0</sub> = 3000 kPa, cemented.</p>
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<p>Grain size distribution test conducted after the one-dimensional compression test: (<b>a</b>) uncemented and (<b>b</b>) cemented.</p>
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<p>Grain size distribution test conducted after the triaxial test: (<b>a</b>) uncemented and (<b>b</b>) cemented.</p>
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<p>SEM micrographs after one-dimensional compression tests at 500× magnification rate: (<b>a</b>) M_D_0C and (<b>b</b>) M_D_3C.</p>
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<p>SEM micrographs after triaxial testing at 500× magnification rate: (<b>a</b>) M_D_0C and (<b>b</b>) M_D_3C. Red circles and arrows represent the grain breakage of the samples.</p>
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9 pages, 1531 KiB  
Communication
Methodological Approach for an Online Water Quality Monitoring System in an Iron Ore Tailing Dam
by Renato Oliveira da Silva Júnior, Helena Pereira Almeida, Marcio Sousa da Silva, Adriano Cuenya França, Eduardo Balleroni, Nailson dos Santos, Paulo Henrique Vilela, Adayana Maria Queiroz de Melo and José Tasso Felix Guimarães
Water 2023, 15(20), 3663; https://doi.org/10.3390/w15203663 - 19 Oct 2023
Cited by 1 | Viewed by 1532
Abstract
Monitoring the concentration of potentially toxic elements (PTEs) in the aquatic ecosystems of the Amazon is critical to guarantee the maintenance of the ecological balance and the life quality of human populations that reside in or use these environments for survival. In this [...] Read more.
Monitoring the concentration of potentially toxic elements (PTEs) in the aquatic ecosystems of the Amazon is critical to guarantee the maintenance of the ecological balance and the life quality of human populations that reside in or use these environments for survival. In this sense, many rivers in the region are dammed to form lakes for depositing mining tailings. Among these, the Gelado Project has the largest iron ore dam in the Amazon that occupies about 13.5 km2 of surface area with 142 million m3 of water and tailings volume, which are currently being mined for exploration, and its upstream waters and downstream are historically used by traditional populations. Based on this, to monitor the impacts of this activity, an online system for sampling and automatic analysis of water quality, composed of three process analyzers monitoring more than 20 parameters, including the PTEs (Cd, Pb, Fe, Mn, Ni, and Cu), was installed downstream of this dam. Therefore, this short communication describes this system’s development, installation, operation, and main advantage over conventional methods. Full article
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<p>Igarapé Gelado Dam (IGD). (<b>A</b>) Iron ore mining and Amazon rain forest. (<b>B</b>) Hypsometry and bathymetry map. (<b>C</b>) Area, surroundings and lake of the Gelado dam in Carajás, Eastern Amazon.</p>
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<p>Schematic diagram of the IGD-WQ project: water is collected every two hours, the sample is analyzed by the system, data is collected by ECU (Unit Control Electronic) and transmitted to a control and monitoring room.</p>
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