Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone.

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0-0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.

A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics.

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0-20 cm) in a 2574 km2 agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (-0.26 > r > -0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.

Association between extracted copper and dissolved organic matter in dairy-manure amended soils.

Dairy manure often has elevated concentrations of copper (Cu) that when applied to soil may create toxicity risks to seedlings and soil microbes. Manure application also increases dissolved organic matter (DOM) in soil solution. We hypothesize that high rates of dairy manure amendment over several years will cause increased DOM in the soil that complexes Cu, increasing its mobility. To test this hypothesis, this study investigated water soluble Cu concentrations and dissolved organic carbon (DOC) in soil samples from 3 years of manure-amended soils. Samples were collected at two depths over the first 3 years of a long-term manure-amendment field trial. DOC, Cu, Fe, and P concentrations were measured in water extracts from the samples. Ultraviolet/visible (UV/Vis) spectra were used to assess the DOC characteristics. After 3 years of manure application, extractable Cu concentration was approximately four times greater in the surface and two times greater in subsurface samples of manure-amended soils as compared to non-amended control soils and traditional mineral fertilizer-amended soils. The extractable Cu concentration was greatest in plots that had the highest manure amendment rates (35 t ha-1 and 52 t ha-1, dry weight). The UV/Vis parameters SUVA254 and E2/E3 correlated with Cu concentration in the extracts (p < 0.05), suggesting that DOC characteristics are important in Cu-binding. The molecular characteristics of the DOC in the subsurface after 3 years of manure amendment were distinct from the DOC in the control plot, suggesting that manure amendment creates mobile DOC that may facilitate Cu mobilization through soil. The 10-fold increase in extractable Cu concentration after only 3 years of manure application indicates that repeated applications of the dairy manure sources used in this study at rates of 35 t/ha or greater may create risks for Cu toxicity and leaching of Cu into ground and surface waters.