Prediction of heavy metals in polluted mangrove soils in Brazil with the highest reported levels of mercury using near-infrared spectroscopy.

Infrared reflectance spectroscopy has demonstrated potential as a tool for monitoring and preventing contamination in different environments. The objective of this study was to evaluate the usage of near-infrared spectroscopy for predicting heavy-metal contamination in mangrove soils from the Botafogo River estuary located in Pernambuco State, Northeastern Brazil. These soils exhibit the highest mercury (Hg) levels ever reported for Brazilian mangrove soils. Sixty-one samples (obtained at depths ranging from 0 to 5 cm) were collected and measured using near-infrared (1000-2500 nm) reflectance spectroscopy. Preprocessing methods were applied, and partial least squares regression was used to build prediction models for attributes such as clay content, soil organic matter (SOM), pH, Eh, and concentrations of Cr, Cu, Hg, Ni, Pb, and Zn. The models were evaluated using root mean squared error (RMSE), the adjusted coefficient of determination (R2adj), bias, the ratio of performance to interquartile distance (RPIQ), and Lin's concordance correlation coefficient (CCC). The best outcomes were noted for concentrations of Cr, Cu, Hg, Ni, and Pb (RPIQ > 2.5 and R2adj > 0.80); second-best outcomes were found for Zn and SOM (RPIQ > 1.5 and R2adj > 0.70). Clay content, pH and Eh exhibited the poorest outcomes (RPIQ < 1.5). The importance of spectral preprocessing is highlighted, notably with Savitzky-Golay derivatives and Multiplicative Scatter Corrections, which boosted performance for most of the variables. Near-infrared spectroscopy can be efficiently used to predict Cr, Cu, Hg, Ni, Pb and SOM and represents a technique complementary to traditional analyses.

Distribution of rare earth elements in soils of contrasting geological and pedological settings to support human health assessment and environmental policies.

Establishing quality reference values (QRVs) for rare earth elements (REEs) in soils is essential for the screening of these emergent contaminants. Currently, Brazil has the second-largest reserve of REEs, but data regarding background concentrations and distributions in soils remain scarce. The aim of this study was to establish the QRVs and assess the spatial distribution of REEs in soils, including REE fractionations and anomalies in (Piauí) state (251,529.186 km2), northeastern Brazil. This study reports the most detailed data on REE geochemistry in Brazilian soils. A total of 243 composite soil samples was collected at 0-20 cm depth. The mean background concentrations in soils followed the abundance of the earth's upper crust: Ce > La > Nd > Pr > Sm > Dy > Gd > Er > Yb > Eu > Tb > Lu. The ∑REEs (mg kg-1) showed the following order based on the individual mesoregions of Piauí state: Southeast (262.75) > North and Central-North (89.68) > Southwest (40.33). The highest QRVs were observed in the Southeast mesoregion. The establishment of QRVs based on the mesoregion scale improves data representativeness and the monitoring of natural REE values by identifying hot spots. Geostatistical modeling indicated significant local variability, especially in the Southeast mesoregion. The levels of these elements in this spatial zone are naturally higher than the other values across Piauí state and the mesoregion itself and indicate a high potential to exceed the QRVs. Our approach provides much needed data to help strengthen policies for both human health and environmental protection.

Combining geospatial analyses to optimize quality reference values of rare earth elements in soils.

High pedological and geological variability can trigger the formation of REE hotspots, causing a need to optimize the establishment of quality reference values (QRVs). Thus, we determined the background concentrations of REEs in the soils of an emerging Brazilian state and used a combination of Moran's I and indicator kriging to identify REE hotspots and determine QRVs. A total of 100 composite soil samples was collected at a 0.20 m depth to establish background concentrations, QRVs, and spatial distribution and to elaborate probability maps for REEs. The QRVs established for soils were the following (mg kg-1): La (27.21), Ce (57.26), Pr (10.49), Nd (24.29), Sm (4.75), Eu (0.90), Gd (4.22), Tb (0.82), Dy (1.54), Ho (0.38), Er (1.23), Yb (1.07), Lu (0.24), Y (10.65), and Sc (3.70). It was possible to draw attention to the Northwest and Southwest regions of the Rio Grande do Norte (RN) state, due to the formation of REE hotspots, indicated by Moran's I, and a high tendency to exceed the QRVs, confirmed by the indicator kriging. The high background concentrations and geochemical patterns for REEs showed that a single QRV for each REE and the entire state can neglect specific environmental characteristics and misrepresent the natural geochemistry of the soil. Thus, specific QRVs were established to optimize the monitoring of natural REE values by identifying hotspot areas. The criteria established here may be useful for other groups of potentially toxic elements, provided that observations meet the requirements of the spatial autocorrelation and kriging analyses. Graphical abstract.