Rare earth elements (REEs): geochemical patterns and contamination aspects in Brazilian benchmark soils.

Rare earth elements have been increasingly used in modern societies and soils are likely to be the final destination of several REE-containing (by)products. This study reports REE contents for topsoils (0-20 cm) of 175 locations in reference (n = 68) and cultivated (n = 107) areas in Brazil. Benchmark soil samples were selected accomplishing a variety of environmental conditions, aiming to: i) establishing natural background and anthropogenic concentrations for REE in soils; ii) assessing potential contamination of soils - via application of phosphate fertilizers - with REE; and, iii) predicting soil-REE contents using biomes, soil type, parent material, land use, sand content, and biomes-land use interaction as forecaster variables through generalized least squares multiple regression. Our hypotheses were that the variability of soil-REE contents is influenced by parent material, pedogenic processes, land use, and biomes, as well as that cultivated soils may have been potentially contaminated with REE via input of phosphate fertilizers. The semi-total concentrations of REE were assessed by inductively coupled plasma mass spectrometry (ICP-MS) succeeding a microwave-assisted aqua regia digestion. Analytical procedures followed a rigorous QA/QC protocol. Soil physicochemical composition and total oxides were also determined. Natural background and anthropogenic concentrations for REE were established statistically from the dataset by the median plus two median absolute deviations method. Contamination aspects were assessed by REE-normalized patterns, REE fractionation indices, and Ce and Eu anomalies ratios, as well as enrichment factors. The results indicate that differences in the amounts of REE in cultivated soils can be attributed to land use and agricultural sources (e.g., phosphate-fertilizer inputs), while those in reference soils can be attributed to parent materials, biomes, and pedogenic processes. The biomes, land use, and sand content helped to predict concentrations of light REE in Brazilian soils, with parent material being also of special relevance to predict heavy REE contents in particular.

Modeling arsenic content in Brazilian soils: What is relevant?

Arsenic accumulation in the environment poses ecological and human health risks. A greater knowledge about soil total As content variability and its main drivers is strategic for maintaining soil security, helping public policies and environmental surveys. Considering the poor history of As studies in Brazil at the country's geographical scale, this work aimed to generate predictive models of topsoil As content using machine learning (ML) algorithms based on several environmental covariables representing soil forming factors, ranking their importance as explanatory covariables and for feeding group analysis. An unprecedented databank based on laboratory analyses (including rare earth elements), proximal and remote sensing, geographical information system operations, and pedological information were surveyed. The median soil As content ranged from 0.14 to 41.1 mg kg-1 in reference soils, and 0.28 to 58.3 mg kg-1 in agricultural soils. Recursive Feature Elimination Random Forest outperformed other ML algorithms, ranking as most important environmental covariables: temperature, soil organic carbon (SOC), clay, sand, and TiO2. Four natural groups were statistically suggested (As content ± standard error in mg kg-1): G1) with coarser texture, lower SOC, higher temperatures, and the lowest TiO2 contents, has the lowest As content (2.24 ± 0.50), accomplishing different environmental conditions; G2) organic soils located in floodplains, medium TiO2 and temperature, whose As content (3.78 ± 2.05) is slightly higher than G1, but lower than G3 and G4; G3) medium contents of As (7.14 ± 1.30), texture, SOC, TiO2, and temperature, representing the largest number of points widespread throughout Brazil; G4) the largest contents of As (11.97 ± 1.62), SOC, and TiO2, and the lowest sand content, with points located mainly across Southeastern Brazil with milder temperature. In the absence of soil As content, a common scenario in Brazil and in many Latin American countries, such natural groups could work as environmental indicators.

Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil.

This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF). In order to reduce the effects of an imbalanced dataset on the accuracy of the tested models, additional sampling retrieved by photointerpretation was necessary. Accuracy assessment was based on aggregated data from a proportional 5-fold cross-validation procedure. Extrapolation assessment was based on the multivariate environmental similarity surface (MESS). The RF model including additional sampling (RF*) showed the best performance among the tested models (overall accuracy = 49%, kappa index = 0.33). The RF* allowed to link soil mapping units (SMU) and, in the case of less-common soil classes in the watershed, to set specific conditions of occurrence on the space of terrain-attributes. MESS analysis showed reliable outputs for 82.5% of the watershed. SMU distribution across the watershed was: Typic Rhodudult (56%), Typic Hapludult* (13%), Typic Dystrudept (10%), Typic Endoaquent + Fluventic Dystrudept (10%), Typic Hapludult (9.5%) and Rhodic Hapludox + Typic Hapludox (2%).

Natural variation of arsenic fractions in soils of the Brazilian Amazon.

Arsenic (As) in native soils of the Amazon rainforest is a concern due to its likely origin from the Andean rivers, which transport loads of sediments containing substantial amounts of trace elements coming from the cordilleras. Yet, unveiling soil As baseline concentrations in the Amazon basin is still a need because most studies in Brazil have been performed in areas with predominantly high concentrations and cannot express a real baseline value for the region. In this study, 414 soil samples (0-20, 20-40 and 40-60 cm layers) were collected from different sites throughout the Amazon basin - including native Amazon rainforest and minimally disturbed areas - and used to determine total and extractable (soluble + available) As concentrations along with relevant soil physicochemical properties. Descriptive statistics of the data was performed and Pearson correlation supported by a Principal Component Analysis (PCA) provided an improved understanding of where and how As concentrations are influenced by soil attributes. Total As concentration ranged from 0.98 to 41.71 mg kg-1 with values usually increasing from the topsoil (0-20 cm) to the deepest layer (40-60 cm) in all sites studied. Considering the proportional contribution given by each fraction (soluble and available) on extractable As concentration, it is noticeable that KH2PO4-extractable As represents the most important fraction, with >70% of the As extracted on average in all the sites studied. Still, the extractable fractions (soluble + available) correspond to ~0.24% of the total As, on average. Total, available, and soluble As fractions were strongly and positively correlated with soil Al3+. The PCA indicated that soil pH in combination with CEC might be the key factors controlling soil As concentrations and the occurrence of each arsenic fraction in the soil layers.