Chemical, physical, and biological attributes in soils affected by deposition of iron ore tailings from the Fundão Dam failure

Assessing the reclamation of a contaminated site affected by the Fundão dam tailings trough phytoremediation and bioremediation

The rupture of the Fundão dam (Brazil) spread tailings contaminated with sodium and ether-amine into the Doce River Basin. Aiming at rehabilitating a contaminated riparian site, phytoremediation with native species of the Atlantic Forest was performed under four treatments: ES-1: physical remediation (sediment scraping) + chemical remediation (organic matter) + bioremediation (double inoculation with the arbuscular mycorrhizal fungus (AMF) Rhizophagus irregularis and the plant growth-promoting bacteria Bacillus subtilis); ES-2: chemical remediation + bioremediation; ES-3: physical remediation + chemical remediation; ES-4: chemical remediation. Ether-amine and sodium contents, plant growth and, soil quality parameters were compared among treatments and relative to preserved and degraded sites. Two years after planting, the outstanding plant growth was attributed to the phytoremediation of ether-amine and ammonium, followed by a significant increase in soil microbial biomass (Phospholipid fatty acids-PLFAs), particularly the Gram+ bacteria and total fungi but not AMF, whose response was independent of the inoculation. While sodium and ether-amine declined, soil K, P, NO3- contents, dehydrogenase and acid phosphatase activities, cation exchange capacity (CEC) and soil aggregation increased, especially in ES-1. Thus, such remediation procedures are recommended for the restoration of riparian areas affected by the Fundão tailings, ultimately improving sediment fertility, aggregation and stabilization.

Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems

In November 2015, a catastrophic rupture of the Fundão dam in Mariana (Brazil), resulted in extensive socio-economic and environmental repercussions that persist to this day. In response, several reforestation programs were initiated to remediate the impacted regions. However, accurately assessing soil health in these areas is a complex endeavor. This study employs machine learning techniques to predict soil quality indicators that effectively differentiate between the stages of recovery in these areas. For this, a comprehensive set of soil parameters, encompassing 3 biological, 16 chemical, and 3 physical parameters, were evaluated for samples exposed to mining tailings and those unaffected, totaling 81 and 6 samples, respectively, which were evaluated over 2 years. The most robust model was the decision tree with a restriction of fewer levels to simplify the tree structure. In this model, Cation Exchange Capacity (CEC), Microbial Biomass Carbon (MBC), Base Saturation (BS), and Effective Cation Exchange Capacity (eCEC) emerged as the most pivotal factors influencing model fitting. This model achieved an accuracy score of 92% during training and 93% during testing for determining stages of recovery. The model developed in this study has the potential to revolutionize the monitoring efforts conducted by regulatory agencies in these regions. By reducing the number of parameters that necessitate evaluation, this enhanced efficiency promises to expedite recovery monitoring, simultaneously enhancing cost-effectiveness while upholding the analytical rigor of assessments.

Mineralogical fingerprint and human health risk from potentially toxic elements of Fe mining tailings from the Fundão dam

In 2015, >50 million cubic meters of Fe mining tailings were released into the Doce River basin from the Fundão dam, raising the question of its consequences on the affected ecosystems. This study aimed to establish a mineralogical-(geo)chemical association of potentially toxic elements (PTEs) from Fe mining tailings from the Fundão dam, collected seven days after the failure, through a multidisciplinary approach combining assessment of the risk to human health, environmental geochemistry, and mineralogy. Thus, eleven tailings samples were collected with the support of the Brazilian Military Police Fire Department. Granulometry, magnetic measurements, optical microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and sequential chemical extraction of PTEs analyses were performed. Contamination indexes, assessment of risk to human health, and Pearson correlation were calculated using the results of sequential chemical extraction of PTEs. The predominance of goethite in Fe oxyhydroxide concentrates from the mud indicates that the major source of hematite may not be from tailings, but from pre-existing soils and sediments, and/or preferential dissolution of hematite in deep flooded zones of the tailings column of the Fundão dam. Moreover, the high correlation of most carcinogenic PTEs with their crystallographic variables indicates that goethite is the primary source of contaminants. Goethites from Fe mining tailings showed high specific surface area and Al-substitution, and due to their greater stability and reactivity, the impacts on PTE sorption phenomena and bioavailability may be maintained for long periods. However, their lower dissolution rate, and the consequent release of heavy metals would promote greater resilience for affected ecosystems, preventing significant PTE inputs under periodic reduction conditions. More specific studies, involving the crystallographic characteristics of Fe oxyhydroxides should be developed since they may provide another critical component of this set of complex and dynamic variables that interfere with the bioavailability of metals in ecosystems.

Soil arthropod community responses to restoration in areas impacted by iron mining tailings deposition after Fundão dam failure

In 2015, the failure of the Fundão dam in Mariana, Brazil released ~43 million m3 of iron mining tailings into the environment. Despite restoration initiatives in the following years, few studies-and most focused on revegetation-have evaluated the effectiveness of the restoration process in areas impacted by the disaster. We aimed to evaluate the responses of the arthropod community in areas impacted by iron mining tailings deposition from the Fundão dam that is in the restoration process. We defined sampling units in the riparian zone of the Gualaxo do Norte River, which is under restoration, and in a native not impacted riparian zone. We collected soil arthropods using pitfall traps and sampled environmental variables in the same sites. We used generalize least squares models (GLS) to test if the restored areas already presented values of arthropod diversity and functional group abundance similar to the reference area and to test which environmental variables are influencing arthropod diversity. We also tested how large the differences of arthropod community composition between the study areas and used the index of indicator species (IndVal) to verify which species could be used as an indicator of reference or restoration areas. The diversity of arthropods and the functional groups of detritivores and omnivores were higher in the native riparian zone. Understory density, soil density, organic matter content, and microbial biomass carbon were the environmental variables that significantly explained the diversity and species composition of arthropods. We show that restoration areas still have different soil arthropod diversity values and community composition when compared to reference areas. Evaluating the response of the arthropod community to the restoration process and long-term monitoring are essential to achieve a satisfactory result in this process and achieve a self-sustaining ecosystem.

Detailed characterization of iron-rich tailings after the Fundão dam failure, Brazil, with inclusion of proximal sensors data, as a secure basis for environmental and agricultural restoration

Following the Fundão dam failure in Brazil, 60 million m³ of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments. This work hypothesized that portable X-ray fluorescence (pXRF) spectrometry combined with a pocket-sized (Nix™ Pro) color sensor and benchtop magnetic susceptibilimeter can produce substantial data for fast and clean characterization of iron-rich tailings. The objectives were to differentiate impacted and non-impacted areas (soils and sediments) based on proximal sensors data, and to predict attributes of agronomic and environmental importance. A total of 148 composite samples were collected on totally impacted, partially impacted, and non-impacted areas (natural soils). The samples were analyzed via pXRF to obtain the total elemental composition; via Nix™ Pro color sensor to obtain the red (R), green (G), and blue (B) parameters; and assessed for magnetic susceptibility (MS). The same samples used for analyses via the aforementioned sensors were wet-digested (USEPA 3051a method) followed by ICP-OES quantification of potentially toxic elements. Principal component analysis was performed to differentiate impacted and non-impacted areas. The pXRF data alone or combined with other sensors were used to predict soil agronomic properties and semi-total concentration of potentially toxic elements via random forest regression. For that, samples were randomly separated into modeling (70%) and validation (30%) datasets. The pXRF proved to be an efficient method for rapid and eco-friendly characterization of iron-rich tailings, allowing a clear differentiation of impacted and non-impacted areas. Also, important soil agronomic properties (clay, cation exchange capacity, soil organic carbon, pH and macronutrients availability) and semi-total concentrations of Ba, Pb, Cr, V, Cu, Co, Ni, Mn, Ti, and Li were accurately predicted (based upon the lowest RMSE and highest R² and RPD values). Sensor data fusion (pXRF + Nix Pro + MS) slightly improved the accuracy of predictions. This work highlights iron-rich tailings from the Fundão dam failure can be in detail characterized via pXRF ex situ, providing a secure basis for complementary studies in situ aiming at identify contaminated hot spots, digital mapping of soil and properties variability, and embasing pedological, agricultural and environmental purposes.

Potentially toxic elements in iron mine tailings: Effects of reducing soil pH on available concentrations of toxic elements

Tailings from iron mining are characterized by high concentrations of iron and manganese oxides, as well as high pH values. With these characteristics, most of the potentially toxic elements (PTE) contained in the tailings are somewhat unavailable. The aim of the present study was to evaluate how a reduction in the pH of iron mine tailings may affect PTE availabilities. The tailings were collected on the banks of the Gualaxo do Norte River (Mariana, MG, Brazil), one of the main areas impacted by the rupture of the Fundão Dam (Barragem de Fundão). A completely randomized experimental design was used, including five pH values (6.4, 5.4, 4.3, 3.7, and 3.4) and five replications. The concentrations of the PTE (Ba, Cr, Cd, Co, Cu, Fe, Mn, Pb, Ni, and Zn) were determined after extraction following different methodologies: USEPA 3051A, DTPA, Mehlich-1, Mehlich-3, and distilled water. A comparison of the available concentrations of the elements in the tailings with those in a soil not impacted by tailings shows that Cr, Cd, Cu, Fe, Mn, Ni, Ba, and Co were higher in the soil impacted by the tailings. The different methods used for evaluating the availability of PTE in the tailings at various pH exhibited the following decreasing order in relation to the quantity extracted: Mehlich-3 > Mehlich-1 > DTPA > distilled water. However, regarding sensitivity to change in pH, the order was DTPA > water > Mehlich-1 > Mehlich-3. The increases in the concentrations of PTE due to the reduction in the pH of the tailings did not lead to concentrations that exceed the limits of Brazilian regulations. The DTPA extractant exhibited higher coefficients of correlation between the PTE concentrations and the pH of the tailings, proving to be suitable for use in areas affected by the deposition of iron mine tailings.

Effects of CaO2 based Fenton - like reaction on heavy metals and microbial community during co-composting of straw and sediment

In this study, a Fenton-like system was constructed by CaO₂ and nano-Fe₃O₄ in the co-composting system of straw and sediment. Its effect on the passivation of heavy metals and the evolution of microbial community were investigated. The results showed that the establishment of CaO₂-Fenton-like system increased the residual Cu and residual Zn by 27.62% and 16.80%, respectively. In addition, the CaO₂-Fenton-like system facilitated the formation of humic acid (HA) up to 20.84 g·kg^-1. Redundancy analysis (RDA) showed that the CaO₂-Fenton-like system accelerated bacterial community succession and promoted the passivation of Cu and Zn. Structural equation models (SEMs) indicated that Fenton reaction affected Cu and Zn passivation by affecting pH, bacterial communities, and HA. This study shows that the CaO₂-Fenton-like system could promote the application of composting in the remediation of heavy metals contamination in sediment.