An in-depth human health risk assessment of potentially toxic elements in highly polluted riverine soils, Príbram (Czech Republic).

Environmental pollution by potentially toxic element (PTE) and the associated health risks in humans are increasingly becoming a global challenge. The current study is an in-depth assessment of PTEs including the often studied lead (Pb), manganese (Mn), zinc (Zn), arsenic (As) and the less-studied titanium (Ti), rubidium (Rb), strontium (Sr), zirconium (Zr), barium (Ba) and thorium (Th) in highly polluted floodplain topsoil samples from the Litavka River, Czech Republic. Soil chemical properties including carbon (Cox) and reaction (pH_H2O) together with iron (Fe) were assessed in the same soils. A portable X-ray fluorescence spectrometer (p-XRFS) (Delta Premium) was used to measure the PTEs and Fe contents of the soils. Soil organic carbon and reaction pH were determined following routine laboratory procedures. The concentration level of each PTE was compared against world average and crustal values, with the majority of elements exceeding the aforementioned geochemical background levels. Distributions of the PTEs were mapped. Two pollution assessment indices including enrichment factor (EF) and pollution index (PI) levels were calculated and their means for Zn (43.36, 55.54), As (33.23, 43.59) and Pb (81.08, 103.21) show that these elements were enriched. Zn, As and Pb accounted for the high pollution load index (PLI) levels observed in the study. The EF and PI distribution maps corresponded with the concentration distribution maps for each PTE. On health risk assessment, hazard quotients (HQ) in different human groups varied. Children had the highest HQs for all PTEs than adults (women and men). PTEs with high HQ levels in distinct human groups were As, Zr and Pb. Zirconium is a less likely element to pose a health risk in humans. Nonetheless, it should be kept in check despite its low pollution occurrence.

Comparison of Cubist models for soil organic carbon prediction via portable XRF measured data.

Soil organic carbon (SOC) tends to form complexes with most metallic ions within the soil system. Relatively few studies compare SOC predictions via portable X-ray fluorescence (pXRF) measured data coupled with the Cubist algorithm. The current study applied three different Cubist models to estimate SOC while using several pXRF measured data. Soil samples (n = 158) were collected from the Litavka floodplain area during two separate sampling campaigns in 2018. Thirteen pXRF data or predictors (K, Ca, Rb, Mn, Fe, As, Ba, Th, Pb, Sr, Ti, Zr, and Zn) were selected to develop the proposed models. Validation and comparison of the models applied the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results revealed that Cubist 1, utilizing all the predictors yielded the best model outcome (MAE = 0.51%, RMSE = 0.68%, R2 = 0.78) followed by Cubist 2, using predictors with relatively high importance (VarImp. predictors) (MAE = 0.64%, RMSE = 0.82%, R2 = 0.68), and lastly Cubist 3 with predictors showing a significantly positive correlation (MAE = 0.69%, RMSE = 0.90%, R2 = 0.62). The Cubist 1 model was considered more promising for explaining the complex relationships between SOC and the pXRF data used. Moreover, for the estimation of SOC in temperate floodplain soils all the Cubist models gave an acceptable model. However, future research should focus on using other auxiliary data [e.g., soil properties, data from other sensors (e.g., FieldSpec)] as well as extend the study area to cover more soil types hence improve model robustness as well as parsimoniousness.

Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review.

The rising and continuous pollution of the soil from anthropogenic activities is of great concern. Owing to this concern, the advent of digital soil mapping (DSM) has been a tool that soil scientists use in this era to predict the potentially toxic element (PTE) content in the soil. The purpose of this paper was to conduct a review of articles, summarize and analyse the spatial prediction of potentially toxic elements, determine and compare the models' usage as well as their performance over time. Through Scopus, the Web of Science and Google Scholar, we collected papers between the year 2001 and the first quarter of 2019, which were tailored towards the spatial PTE prediction using DSM approaches. The results indicated that soil pollution emanates from diverse sources. However, it provided reasons why the authors investigate a piece of land or area, highlighting the uncertainties in mapping, number of publications per journal and continental efforts to research as well as published on trending issues regarding DSM. This paper reveals the complementary role machine learning algorithms and the geostatistical models play in DSM. Nevertheless, geostatistical approaches remain the most preferred model compared to machine learning algorithms.

Zirconium as a suitable reference element for estimating potentially toxic element enrichment in treated wastewater discharge vicinity.

The suitability of a reference element or normalizer used in assessing soil contamination levels using enrichment factor (EF) is important for soil quality assessment and monitoring. This study evaluated the results of using three reference elements Ti, Fe, and Zr for EF determination of Rb and Sr in soils within treated wastewater discharge vicinity, Central Botswana. The upper continental crust (UCC), world average values (WAV), and the local background values (LBV) were used in EF assessment of eight pedons. The elemental concentrations of the soils were determined with portable X-ray fluorescence (pXRF) analyzer. Relationships between the elements were strongly significant between Rb and Ti (r = 0.600, p < 0.01), Rb and Fe (r = 0.735, p < 0.01), Sr and Ti (r = 0.545, p < 0.01), and Sr and Fe (r = 0.841, p < 0.01). Second-level correlation analysis between contamination factor (CF) and EF levels showed Zr as the best reference element for Rb and Sr in the soils. Results from this study provide baseline knowledge necessary for contamination assessment and monitoring of soils with similar environmental conditions.