Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors.

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 (R2), concordance correlation coefficient (ρc), 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, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (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.

Interactions between land use and soil type drive soil functions, highlighting water recharge potential, in the Cantareira System, Southeast of Brazil.

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.

Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers.

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: R2 = 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.

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.

A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones.

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 (R2). 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.

Clean quality control of agricultural and non-agricultural lime by rapid and accurate assessment of calcium and magnesium contents via proximal sensors.

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 (R2 > 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.

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.

Deep Tillage Strategies in Perennial Crop Installation: Structural Changes in Contrasting Soil Classes.

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: MT­plant hole; CT­furrow; SB­subsoiler; DT­rotary 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.

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 Hgtotal 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. Hgtotal 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. Hgtotal 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 Hgtotal 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.

Pocket-sized sensor for controlled, quantitative and instantaneous color acquisition of plant leaves.

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, R2, 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.

X-ray fluorescence spectrometry applied to digital mapping of soil fertility attributes in tropical region with elevated spatial variability.

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.

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.

Occasional tillage in no-tillage systems: A global meta-analysis.

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.

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.

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.

Assessing soil contamination in automobile scrap yards by portable X-ray fluorescence spectrometry and magnetic susceptibility.

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 m3 kg-1) at low frequency (χlf) compared to reference soil (3.6 to 7.5 × 10-7 m3 kg-1). The highest values of χlf 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 χlf as non-destructive techniques displays potential for identifying soil contamination in automobile scrap yards.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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%).

Effects of phosphate, carbonate, and silicate anions on CO2 emission in a typical oxisol from cerrado region / Efeitos dos ânions fosfato, carbonato e silicato na emissão de CO 2 em um latossolo típico da região do cerrado

The effects of agricultural practices on greenhouse gases emissions (e.g. CO2) at the soil-atmosphere interface have been highlighted worldwide. The use of ground limestone has been considered as the main responsible for CO2 emission from soils. However, liming is need as conditioner of acidic soils and the CO2 emission can be compensated due to carbon sequestration by plants. This study simulated under laboratory conditions the effects of two common agricultural practices in Brazil (P-fertilization and liming) on soil CO2 emission. Columns made of PVC tubes containing 1 kg of a typical Dystrophic Red Latosol from Cerrado region were incubated with CaCO3 (simulating liming), CaSiO3 (simulating slag), and different doses of KH2PO4 (simulating P-fertilization). The soil columns were moistened to reach the field capacity (0.30 cm3cm-3) and, during 36 days, CO2 emissions at the soil surface were measured using a portable Licor LI-8100 analyzer coupled to a dynamic chamber. The results showed that CO2 emission was influenced by phosphate, carbonate, and silicate anions. When using CaSiO3, accumulated CO2 emission (36-day period) was 20% lower if compared to the use of CaCO3. The same amount of phosphate and liming (Ca-carbonate or Ca-silicate) added to the soil provided the same amount of CO2 emission. At the same P dose, as Si increased the CO2emission increased. The highest CO2 emission was observed when the soil was amended with the highest phosphate and silicate doses. Based on this experiment, we could oppose the claim that the use of limestone is a major villain for CO2 emission. Also, we have shown that other practices, such as fertilization using P + CaSiO3, contributed to a higher CO2 emission. Indeed, it is important to emphasize that the best practices of soil fertility management will undoubtedly contribute to the growth of crops and carbon sequestration.

Os efeitos das práticas agrícolas nas emissões de gases de efeito estufa (e.g., CO2) na interface solo-atmosfera têm sido destacados em todo o mundo. O uso de calcário tem sido considerado oprincipal responsável pela emissão de CO2 em solos. Entretanto, a calagem é necessária como condicionador de solos ácidos e a emissão de CO2 pode ser compensada devido ao sequestro de carbono pelas plantas. Este estudo simulou, em condições de laboratório, os efeitos de duas práticas agrícolas comuns no Brasil (adubação fosfatada e calagem) na emissão de CO2 do solo. Colunas de tubos de PVC, contendo 1 kg de amostra de um Latossolo Vermelho Distrófico típico da região de Cerrado, foram incubadas com CaCO3 (simulando calagem), CaSiO3 (simulando escória) e diferentes doses de KH2PO4 (simulando fertilização com P). As colunas de soloforam umedecidas para atingir a capacidade de campo (0,30 cm3 cm-3) e, durante 36 dias, as emissões de CO2na superfície do solo foram medidas usando um analisador portátil Licor LI-8100 acoplado a uma câmara dinâmica. Os resultados mostraram que a emissão de CO2 foi influenciada pelos ânions fosfato, carbonato esilicato. Ao usar CaSiO3, a emissão de CO2 acumulada (período de 36 dias) foi 20% menor se comparado ao uso de CaCO3. A mesma quantidade de fosfato e calcário (Ca-carbonato ou Ca-silicato) adicionado ao solo proporcionou a mesma quantidade de emissão de CO2. Na mesma dose de P, o Si aumentou a emissão de CO2. A maior emissão de CO2 foi observada quando o solo foi alterado com as maiores doses de fosfato e silicato. Com base neste experimento, nega-se que o uso de calcário em solos é um grande vilão para a emissão de CO2. Além disso, foi mostrado que outras práticas, como a fertilização usando P + CaSiO3, contribuíram para uma maior emissão de CO2. Assim, é importante enfatizar que práticas adequadas de manejo da fertilidade do solo, sem dúvida, contribuirão para o crescimento das culturas e o sequestro de carbono.

Tropical soils characterization at low cost and time using portable X-ray fluorescence spectrometer (pXRF): Effects of different sample preparation methods / Caracterização de solos tropicais a baixo custo e tempo usando espectrômetro portátil de fluorescência de raios-X (pXRF): Efeitos de diferentes métodos de preparo de amostras

ABSTRACT Portable X-ray fluorescence spectrometer (pXRF) has been recently adopted by the Soil Science community for uses in both field and laboratory, obtaining the total content of several chemical elements in a few seconds. Sulfuric acid digestion is an expensive and time-consuming laboratory analysis that provides contents of Fe2O3, Al2O3, SiO2, TiO2 and P2O5, important for soil studies. Due to few pXRF studies in tropical soils, this work aimed to compare contents of Fe2O3, Al2O3, SiO2, TiO2 and P2O5 obtained by pXRF with sulfuric acid digestion results, and to evaluate the effects of varying forms of preparing soil samples and scanning with pXRF on the resulting values in Brazilian soils. Soils were scanned in five conditions in-field (in situ) and in laboratory, evaluating varying sample preparation methods, particle sizes and soil moisture. Four pXRF scanning operational modes were tested. Linear regressions were adjusted between results of pXRF and sulfuric acid digestion. Equations were validated with an independent set of samples. Statistical analyses compared the methods of preparing the samples. Adequate linear models reached R2 of 0.99 and 0.89 for Fe2O3 and TiO2, respectively. Validation promoted R2 greater than 0.97 and RMSE and ME close to zero for both oxides. Statistical differences of pXRF results were found among the methods of preparing samples. pXRF spectrometer has great potential to obtain Fe2O3 and TiO2 content rapidly and economically with high correspondence with laboratory results of sulfuric acid digestion analysis. Varying methods of preparing the samples promote differences in the results of pXRF.

RESUMO O espectrômetro portátil de fluorescência de raios-X (pXRF) foi recentemente adotado pela Ciência do Solo, para uso em campo e laboratório, para obtenção do conteúdo total de vários elementos químicos em poucos segundos. A digestão com ácido sulfúrico é uma análise laboratorial cara e demorada que fornece teores de Fe2O3, Al2O3, SiO2, TiO2 e P2O5, importantes para estudos sobre solos. Devido aos poucos estudos sobre o pXRF em solos tropicais, este trabalho objetivou comparar os teores de Fe2O3, Al2O3, SiO2, TiO2 e P2O5 obtidos pelo pXRF com os resultados de digestão com ácido sulfúrico e avaliar os efeitos de diferentes formas de preparo de amostras de solo e leitura com o pXRF sobre seus resultados para solos brasileiros. Os solos foram submetidos a leituras com o pXRF em cinco condições, em campo (in situ) e em laboratório, avaliando variados métodos de preparo de amostras, tamanhos de partículas e umidade do solo. Quatro modos de operação do pXRF foram testados. Regressões lineares foram ajustadas entre os resultados do pXRF e digestão com ácido sulfúrico. As equações foram validadas com um conjunto independente de amostras. Análises estatísticas compararam os métodos de leitura de amostras. Modelos lineares adequados atingiram R2 de 0,99 e 0,89 para Fe2O3 e TiO2, respectivamente. A validação promoveu R2 maior que 0.97 e RMSE e ME próximos a zero para ambos os óxidos. Foram encontradas diferenças estatísticas dos resultados do pXRF entre os métodos de preparo de amostras. O pXRF possui um grande potencial para obter rápida e economicamente os teores de Fe2O3 e TiO2 com elevada correspondência com os resultados laboratoriais da análise da digestão com ácido sulfúrico. Métodos variáveis ​​de preparo das amostras promovem diferenças nos resultados de pXRF.