Diffuse reflectance spectroscopy for estimation of soil organic matter and texture in burned and abandoned cropland areas

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Examining the Performance of PARACUDA-II Data-Mining Engine versus Selected Techniques to Model Soil Carbon from Reflectance Spectra

The monitoring and quantification of soil carbon provide a better understanding of soil and atmosphere dynamics. Visible-near-infrared-short-wave infrared (VIS-NIR-SWIR) reflectance spectroscopy can quantitatively estimate soil carbon content more rapidly and cost-effectively compared to traditional laboratory analysis. However, effective estimation of soil carbon using reflectance spectroscopy to a great extent depends on the selection of a suitable preprocessing sequence and data-mining algorithm. Many efforts have been dedicated to the comparison of conventional chemometric techniques and their optimization for soil properties prediction. Instead, the current study focuses on the potential of the new data-mining engine PARACUDA-II&reg;, recently developed at Tel-Aviv University (TAU), by comparing its performance in predicting soil oxidizable carbon (Cox) against common data-mining algorithms including partial least squares regression (PLSR), random forests (RF), boosted regression trees (BRT), support vector machine regression (SVMR), and memory based learning (MBL). To this end, 103 soil samples from the Pokrok dumpsite in the Czech Republic were scanned with an ASD FieldSpec III Pro FR spectroradiometer in the laboratory under a strict protocol. Spectra preprocessing for conventional data-mining techniques was conducted using Savitzky-Golay smoothing and the first derivative method. PARACUDA-II&reg;, on the other hand, operates based on the all possibilities approach (APA) concept, a conditional Latin hypercube sampling (cLHs) algorithm and parallel programming, to evaluate all of the potential combinations of eight different spectral preprocessing techniques against the original reflectance and chemical data prior to the model development. The comparison of results was made in terms of the coefficient of determination (R2) and root-mean-square error of prediction (RMSEp). Results showed that the PARACUDA-II&reg; engine performed better than the other selected regular schemes with R2 value of 0.80 and RMSEp of 0.12; the PLSR was less predictive compared to other techniques with R2 = 0.63 and RMSEp = 0.29. This can be attributed to its capability to assess all the available options in an automatic way, which enables the hidden models to rise up and yield the best available model

A multivariate statistical and GIS approach to estimate heavy metal(loid)s in contaminated surface soils

In recent decades, there has been a growing concern over the escalating pollution of soil with heavy metal(loid)s, which poses an immediate threat to human health, food safety, and the overall soil environment. This research aimed to assess the extent of contamination, spatial distribution, sources of contamination, potential ecological risks, and health hazards associated with heavy metal(loid)s (specifically As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sr, and Zn) by collecting soil samples from the surface soils in the mining region of Cerrito Blanco and Matehuala, San Luis Potosi in central Mexico. In addition to this, another study was conducted on rare trace metal(loid)s (B, Ba, Sb, Sn, and V) and other trace metals (Ca, Mg, Na, and K) in this selected region, which shows a level of contamination for those metals. The contamination levels of these heavy metal(loid)s were determined using various indices such as Igeo (geo-accumulation index), Cf (contamination factor), PLI (pollution load index), Cd (degree of contamination), mCd (modified degree of contamination), PIN (nemerow pollution index), EF (enrichment factor), and PERI (potential ecological risk index). Multivariate statistical techniques, such as principal component analysis (PCA), cluster analysis, or factor analysis, were used to identification of patterns and correlations among different heavy metal(loid)s and soil parameters. The findings indicated a significant degree of contamination in the surface soil due to heavy metal(loid)s. The integrated contamination indices and the potential ecological risk index revealed high levels of contamination and substantial ecological risks in the study areas, with particular emphasis on the need to control As in the surface soils surrounding Matehuala. Source identification of heavy metal(loid)s were performed using the APCS-MLR, PMF, and UNMIX receptor models, which detected three potential sources: mining and smelting activities, natural sources, and anthropogenic activities. The APCS-MLR model appeared to be more suitable for identifying complex contamination sources, demonstrating a better R2 coefficient and P/M (predicted/measured) ratio than the other models. Mining and smelting activities were identified as the primary factors influencing the distribution of heavy metal(loid)s in the surface soils. The most effective GIS interpolation technique was selected to analyse the spatial distribution patterns of heavy metal(loid) content, comparing five different GIS interpolation approaches such as Inverse Distance Weighting (IDW), Local Polynomial (LP), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), and Radial Basis Functions (RBF). The results indicated regions of significant concentrations for all heavy metal(loid)s, with the northern, western, and central parts of the study area exhibiting particularly elevated levels. Ecological risk assessment based on PERI revealed considerable risk for As and moderate risk for the remaining metals. Moreover, a probabilistic evaluation of health risks indicated minimal non-carcinogenic risks (HI) for humans but significant carcinogenic risks (CR) for both adults and children. Notably, children were found to be more vulnerable to the health risks associated with exposure to these heavy metals compared to adults. Consequently, enhanced monitoring efforts are necessary to address the issue of heavy metal(loid)s contamination in the rapidly developing Matehuala regions.James Watt Scholarshi