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.

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.

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.

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.

Hepatectomías repetidas por enfermedad maligna / Liver repeated resections by malignant disease

Antecedentes: La resección quirurgica radical es el mejor tratamiento en pacientes seleccionados para las lesiones hépaticas malignas. Con los adelantos técnicos de los últimos quince años las re-resecciones pudieron brindar resultados similares a la resección primaria. Objetivo: Analizar nuestra experiencia en este tipo de re-resecciones. Lugar: Sanatorio Parque, Rosario. Departamento de Cirugía Gastrointestinal. Diseño: Trabajo retrospectivo. Población: De 147 pacientes resecados con patología hépatica maligna, 15 con recidiva y que cumplían con los requisitos para ser nuevamente resecados, constituyen la población a estudiar: A 3 de ellos se le practicó posteriormente una tercera resección. Intervenciones: Resecciones hepáticas con intento potencialmente curativo con márgenes de parénquima sano no menor de un cm. Resultados: No hubo mortalidad a 30 días. La morbilidad fue del 26,6 por ciento (4/15 pacientes). La supervivencia media desde la segunda resección fue de 31,0 meses y la mediana de 24,1 meses. Conclusiones: Las resecciones hepáticas, repetidas en pacientes seleccionados pueden prolongar la supervivencia en 24 meses aproximadamente y pueden ser efectuadas con baja morbi-mortalidad

Hepatectomías repetidas por enfermedad maligna / Liver repeated resections by malignant disease

Antecedentes: La resección quirurgica radical es el mejor tratamiento en pacientes seleccionados para las lesiones hépaticas malignas. Con los adelantos técnicos de los últimos quince años las re-resecciones pudieron brindar resultados similares a la resección primaria. Objetivo: Analizar nuestra experiencia en este tipo de re-resecciones. Lugar: Sanatorio Parque, Rosario. Departamento de Cirugía Gastrointestinal. Diseño: Trabajo retrospectivo. Población: De 147 pacientes resecados con patología hépatica maligna, 15 con recidiva y que cumplían con los requisitos para ser nuevamente resecados, constituyen la población a estudiar: A 3 de ellos se le practicó posteriormente una tercera resección. Intervenciones: Resecciones hepáticas con intento potencialmente curativo con márgenes de parénquima sano no menor de un cm. Resultados: No hubo mortalidad a 30 días. La morbilidad fue del 26,6 por ciento (4/15 pacientes). La supervivencia media desde la segunda resección fue de 31,0 meses y la mediana de 24,1 meses. Conclusiones: Las resecciones hepáticas, repetidas en pacientes seleccionados pueden prolongar la supervivencia en 24 meses aproximadamente y pueden ser efectuadas con baja morbi-mortalidad (AU)