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Due to the anthropogenic pressures of expansion areas for livestock and agricultural production in the Brazilian Cerrado, it is of paramount importance to understand the dynamics of land use/land cover (LULC) changes in this region. Thus, we investigated LULC changes in two sub-basins of the Tocantins-Araguaia River basin from 1997 to 2015 and consequently projected future changes for the timespan between 2030 and 2050. The Formoso sub-basin experienced significant expansion of agricultural and pasture areas, whereas the Sono sub-basin limited farmland expansion (more stable native vegetation) due to substantial protected areas, trends that were also observed for future projections (2030 and 2050). Pastureland in the Formoso sub-basin increased by 5.8%, while the Sono sub-basin saw significant gains in cultivated land, according to change detection analyses during the 1997-2015 period. High stability probabilities of no change (> 70%) for grassland areas in the Sono River sub-basin and pasturelands in the Formoso River sub-basin were computed. The CA-Markov model demonstrated a high consistency level with actual LULC classes for both sub-basins, as indicated by an overall Kappa coefficient above 0.8. Future projections for 2030 and 2050 show a substantial expansion of agriculture and pasture in both sub-basins, driven by specific factors such as soil organic carbon stocks, distance from rural settlements, and proximity to rivers. Short- and mid-term simulations indicate substantial expansion of agriculture and pasture in both basins, with potential adverse impacts on water erosion. Consequently, developing policies for soil management and sustainable land use planning is essential for agroecosystem sustainability, promoting a balanced approach to economic development while addressing climate change and anthropogenic challenges., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R 2 ), concordance correlation coefficient (ρ c ), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg -1 ) for Cu and 23.4 (mg kg -1 ) for Zn, respectively. Likewise, ρ c ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R 2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Most of the soil quality assessment protocols are focused on crop production and conservation management, while studies on vital soil functions, such as water recharge potential, should be incorporated into the monitoring of impacts on environmental quality. Our objective was to evaluate, through the Nexus approach, how dynamic (land use and management) and inherent (soil type) factors impact soil physical properties and processes that drive water recharge potential, biomass production, and water erosion in the Cantareira System, Brazil. The assessment considered three soils (Typic Hapludult, Typic Dystrudept, and Typic Usthortent) and four land uses (native forest, rotational grazing, extensive grazing, and eucalyptus), which constitute the main soils and land uses in the Cantareira System region. Representative soil samples were collected at 0-5 and 30-35 cm depth and analyzed for several soil physical quality indicators, which were used to calculate a Soil Physical Quality Index based on soil functions. Converting the native forest to eucalyptus and pasture reduced the overall soil physical quality and water recharge potential. The groundwater recharge potential function in the topsoil has the highest score of 0.72 for Typic Dystrudept in native forest contrasting with 0.16 for extensive pasture. Typic Dystrudept obtained the highest value of the SPQI value (0-5 cm: 0.85; 30-35 cm: 0.90) for native forests when compared to Typic Hapludult (0-5 cm: 0.76; 30-35 cm: 0.57) and Typic Usthortent (0-5 cm: 0.75; 30-35 cm: 0.72). Our findings sustain that land use effects on soil functions depends on soil type. Inclusion of soil type into the Nexus approach increases the understanding of natural resources and derived benefits of water, energy and food in the Cantareira System., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R 2  = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Following the Fundão dam failure in Brazil, 60 million m 3 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 R 2 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Ca and Mg are the most important chemical elements in lime. Properly measuring Ca and Mg contents is essential to assess the quality of lime products. Quality control guarantees the adequate use of lime in industrial processes, in soils, and helps avoiding adulteration. Proximal sensors can aid in this process by determining Ca and Mg contents easily, rapidly and without producing chemical waste. The objective of this study was to evaluate the use an environmentally-friendly method of analyzing the quality of lime. We studied 1) the use of portable X-ray fluorescence (pXRF) to predict concentrations of Ca and Mg in lime, 2) tested if NixPro™ sensor can improve prediction accuracy and 3) tested if sample preparation methods (grinding) affect analyses. 74 samples of lime were analyzed by two different laboratories (lab. 1 = 38, lab. 2 = 36). All samples submitted to pXRF and NixPro™ analyses. Sensor analyses were done in whole (CP) and ground (AQ) samples to test the effect of sample preparation in prediction performance. High correlation was found between Ca and Mg contents measured via pXRF and laboratory analyses. Mg-CP presented the highest correlation coefficient (r = 0.81); Mg-AQ, the lowest (0.57). Predictions presented good performance (R 2  > 0.68); Mg had the best results (0.86). Separating models per laboratory showed that some datasets are harder to model, probably due to variability in the source material (limestone). The addition of NixPro™ data contributed to improve prediction accuracy, although slightly. Predictions using CP samples presented the best results, especially for Mg, indicating that grinding is not necessary. This pioneer study demonstrated that fused proximal sensors can be used to rapidly and easily determine contents of Ca and Mg in soil amendments without producing chemical waste., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023 Elsevier Inc. All rights reserved.)

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R 2 ). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

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 (Hg 0 , Hg 2 2+ and Hg 2+ ), 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 Hg 2+ 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 TiO 2 , 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Tillage modifies soil structure, which can be demonstrated by changes in the soil’s physical properties, such as penetration resistance (PR) and soil electrical resistivity (ρ). The aim of this study was to evaluate the effect of deep tillage strategies on three morphogenetically contrasting soil classes in the establishment of perennial crops regarding geophysical and physical-hydric properties. The experiment was conducted in the state of Minas Gerais, southeastern Brazil. The tillage practices were evaluated in Typic Dystrustept, Rhodic Hapludult, and Rhodic Hapludox soil classes, and are described as follows: MT—plant hole; CT—furrow; SB—subsoiler; DT—rotary hoe tiller; and DT + calcium (Ca) (additional liming). Analyses of PR and electrical resistivity tomography (ERT) were performed during the growing season and measurements were measured in plant rows of each experimental plot. Undisturbed soil samples were collected for analysis of soil bulk density (Bd) at three soil depths (0−0.20, 0.20−0.40, and 0.40−0.60 m) with morphological evaluation of soil structure (VESS). Tukey’s test (p < 0.05) for Bd and VESS and Pearson linear correlation analysis between Bd, ρ, and PR were performed. Soil class and its intrinsic attributes have an influence on the effect of tillage. The greatest effect on soil structure occurred in the treatments DT and DT + Ca that mixed the soil to a depth of 0.60 m. The ρ showed a positive correlation with Bd and with PR, highlighting that ERT may detect changes caused by cultivation practices, although ERT lacks the accuracy of PR. The soil response to different tillage systems and their effects on soil structure were found to be dependent on the soil class.

The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R 2 , root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing., (Copyright © 2022 Elsevier GmbH. All rights reserved.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Portable X-ray fluorescence (pXRF) spectrometry offers valuable information for prediction models of soil fertility attributes spatial variation, although this approach is yet scarce in tropical regions. This study aims to predict and build spatial variability maps of soil pH, remaining phosphorus (P-Rem), soil organic matter (SOM) and sum of bases (SB) using pXRF results through stepwise multiple linear regression (SMLR) and Random Forest (RF) in a highly variable tropical area. Composite samples from soil A horizon were collected at 90 points throughout the campus of the Federal University of Lavras, Minas Gerais, Brazil, for pH, P-Rem, SOM, SB and pXRF analyses. RF predictions showed the highest accuracies, especially for P-Rem and SB (R² values of 0.66 and 0.55, respectively). Attributes that showed higher R² in punctual predictions also exhibited higher R² in spatial predictions. Data obtained from pXRF in tandem with RF can be used to assist prediction models for soil fertility attributes, consequently enabling the digital mapping of such attributes and helping to improve the knowledge about the spatial variability of such attributes in soils of tropical climate. This technique can therefore assist in the identification and orientation of adequate management practices in tropical agricultural practices.

No-tillage (NT) is a major component of conservation agricultural systems. Challenges that have arisen with the adoption of NT include soil compaction, weed management, and stratification of organic matter and nutrients. As an attempt to overcome these challenges, occasional tillage (OT) has been used as a soil management practice in NT systems. However, little is known about the impacts of OT on agronomic and environmental factors. For this reason, the objectives of this meta-analysis were: 1) to summarize the effects of OT on crop productivity, soil physical, chemical and biological properties, soil erosion and weed control; 2) to discuss the main aspects of NT management to optimize the use of OT; 3) to point out shortcomings in the diagnosis of soil compaction in NT systems, which may lead to erroneous decision-making processes regarding the use of OT. Overall, OT did not affect crops yields, although increased crop yields were observed in regions under water restriction and in soils with low retention capacity and water availability; OT improved soil physical properties (penetration resistance, soil bulk density, macroporosity, and total porosity), with persistence, generally, greater than 24 months, and decreased the soil aggregates stability; total organic carbon was reduced, particularly when plow/harrow was used and NT was already consolidated, and there was no effect on pH and available P; OT increased microbial biomass carbon, but had no effect on total microbial activity; soil erosion was reduced due to increased soil-water infiltration and reduced runoff, and finally, weed management was also improved by OT. It is suggested that suitable NT implementation and management, with the correct application of NT principles, will overcome problems associated with NT. As soil compaction is the main justification for the use of OT, methods of diagnosis and monitoring of soil compaction should be improved to assist in decision-making., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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.

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 TiO 2 . 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 TiO 2 contents, has the lowest As content (2.24 ± 0.50), accomplishing different environmental conditions; G2) organic soils located in floodplains, medium TiO 2 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, TiO 2 , and temperature, representing the largest number of points widespread throughout Brazil; G4) the largest contents of As (11.97 ± 1.62), SOC, and TiO 2 , 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

A by-product of industrialization and population growth, automobile scrap yards are a potential source of metal contamination in soil. This study evaluated the use of portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (χ) analysis in assessing metal soil contamination in scrap yards located in Brazil. Five automobile scrap yards were selected in Curitiba, Paraná State (CB1, CB2, and CB3) and Lavras, Minas Gerais State (LV1 and LV2). By evaluating metal concentrations and geoaccumulation index values, we verified moderate Cu, Pb, and Zr contamination and moderate to high Zn contamination, primarily in the topsoil (0-10 cm). Soil Zn concentrations in automobile scrap yards were on average four times higher than in reference soils, suggesting that galvanized automobile parts may be the primary source of this soil contaminant. Although other elements (i.e., As, Cr, Fe, Nb, Ni, and Y) were slightly increased compared to reference values in one or more soils, concentrations did not constitute contamination. Automobile scrap yard topsoil had higher χ values (5.8 to 52.9 × 10 -7  m 3  kg -1 ) at low frequency (χ lf ) compared to reference soil (3.6 to 7.5 × 10 -7  m 3  kg -1 ). The highest values of χ lf occurred in LV soils, which also represented the highest Zn contamination. Magnetic multidomain characteristics (percent frequency-dependent susceptibility between 2 and 10) indicated magnetic particle contributions of anthropogenic origin. The use of pXRF and χ lf as non-destructive techniques displays potential for identifying soil contamination in automobile scrap yards.

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%).

The ameliorating effects of different inorganic materials were investigated on a soil originating from a zinc smelter dumping site contaminated by toxic metals. Wild mustard (Sinapis arvensis L.) was used as a test plant. The soil was amended with different doses of mining sludge, Perferric Red Latosol (LVj), steel shots, cyclonic ash, silifertil, and superphosphate. The most effective amendments improved plant growth with 45% and reduced metal uptake by over 70% in comparison to untreated soil. Reductions in availability as estimated by BaCl2-extractable metals reached up to 90% for Zn and 65% for Cd as compared to unamended soil. These reductions were associated with lower shoot and root metal contents. Shoot Zn content was reduced from 1,369 microg g(-1) in plants grown on untreated soil to 377 microg g(-1) when grown on cyclonic ash amended soil while Cd decreased from 267 to 44 microg g(-1) in steel shots amended soil. Superphosphate addition had no ameliorating effect. On the contrary, it increased BaCl2-extractable amounts of Zn. Considering all parameters we determined, steel shots, cyclonic ash and silifertil are the most promising for remediating metal contaminated soil in the tropics. Further studies evaluating impacts, cost-effectiveness and durability of effects will be conducted.