Geochemistry signatures of mercury in soils of the Amazon rainforest biome

Mercury (Hg) toxicity in soils depends on Hg species and other physical and chemical attributes, as selenium (Se) hotspots in soils, particularly relevant in Amazonian soils. The study of Hg species and their relations in representative locations of the Amazon rainforest biome is critical for assessing the potential risks of Hg in this environment. This work aimed to determine the concentration of total Hg and its species (Hg⁰, Hg₂²⁺ and Hg²⁺), and to correlate Hg_total concentration with total elemental composition, magnetic susceptibility, and physicochemical attributes of Amazon soils. Nine sites in the Amazon rainforest biome, Brazil, were selected and analyzed for their chemical, physical, and mineralogical attributes. The clay fraction of the studied Amazon soils is dominated by kaolinite, goethite, hematite, gibbsite, and quartz. Mica was also found in soils from the States of Acre and Amazonas. Hg_total ranged from 21.5 to 208 μg kg^-1 (median = 104 μg kg^-1), and the concentrations did not exceed the threshold value established for Brazilian soils (500 μg kg^-1). The Hg²⁺ was notably the predominant species. Its occurrence and concentration were correlated with the landscape position and soil attributes. Hg_total was moderately and positively correlated with TiO₂, clay, and Se. The findings showed that geographic location, geological formation, and pedological differences influence the heterogeneity and distribution of Hg_total in the studied soil classes. Thus, a detailed characterization and knowledgment of the soil classes is very important to clarify the complex behavior of this metal in the Amazon rainforest biome.

Assessment of iron-rich tailings via portable X-ray fluorescence spectrometry: the Mariana dam disaster, southeast Brazil

On November 5, 2015, the Fundão dam collapsed and released > 60 million m³ of iron-rich mining sediments into the Doce river basin, covering >1000 ha of floodplain soils across ~80 km from the rupture. The characterization of alluvial mud covering and/or mixed with native soil is a priority for successful environmental rehabilitation. Portable X-ray fluorescence (pXRF) spectrometry was used to (1) assess the elemental composition of native soils and alluvial mud across impacted riparian areas; and 2) predict fertility properties of the mud and soils that are crucial for environmental rehabilitation and vegetation establishment (e.g., pH, available macro and micronutrients, cation exchange capacity, organic matter). Native soils and alluvial mud were sampled across impacted areas and analyzed via pXRF and conventional laboratory methods. Random forest (RF) regression was used to predict fertility properties using pXRF data for pooled soil and alluvial mud samples. Mud and native surrounding soils were clearly differentiated based on chemical properties determined via pXRF (mainly SiO₂, Al₂O₃, Fe₂O₃, TiO₂, and MnO). The pXRF data and RF models successfully predicted pH for pooled samples (R² = 0.80). Moderate predictions were obtained for soil organic matter (R² = 0.53) and cation exchange capacity (R² = 0.54). Considering the extent of impacted area and efforts required for successful environmental rehabilitation, the pXRF spectrometer showed great potential for screening impacted areas. It can assess total elemental composition, differentiate alluvial mud from native soils, and reasonably predict related fertility properties in pooled heterogeneous substrates (native soil + mud + river sediments).

Foliar Elemental Analysis of Brazilian Crops via Portable X-ray Fluorescence Spectrometry

Foliar analysis is very important for the nutritional management of crops and as a supplemental parameter for soil fertilizer recommendation. The elemental composition of plants is traditionally obtained by laboratory-based methods after acid digestion of ground and sieved leaf samples. This analysis is time-consuming and generates toxic waste. By comparison, portable X-ray fluorescence (pXRF) spectrometry is a promising technology for rapid characterization of plants, eliminating such constraints. This worked aimed to assess the pXRF performance for elemental quantification of leaf samples from important Brazilian crops. For that, 614 samples from 28 plant species were collected across different regions of Brazil. Ground and sieved samples were analyzed after acid digestion (AD), followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES) to determine the concentration of macronutrients (P, K, Ca, Mg, and S) and micronutrients (Fe, Zn, Mn, and Cu). The same plant nutrients were directly analyzed on ground leaf samples via pXRF. Four certified reference materials (CRMs) for plants were used for quality assurance control. Except for Mg, a very strong correlation was observed between pXRF and AD for all plant-nutrients and crops. The relationship between methods was nutrient- and crop-dependent. In particular, eucalyptus displayed optimal correlations for all elements, except for Mg. Opposite to eucalyptus, sugarcane showed the worst correlations for all the evaluated elements, except for S, which had a very strong correlation coefficient. Results demonstrate that for many crops, pXRF can reasonably quantify the concentration of macro- and micronutrients on ground and sieved leaf samples. Undoubtedly, this will contribute to enhance crop management strategies concomitant with increasing food quality and food security.