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Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation. [ABSTRACT FROM AUTHOR]

Thallium (Tl), vanadium (V) and arsenic (As) are considered as typical toxic elements of increased interest. Their accumulation in soils can pose a substantial health threat to human beings. In this study, Fe–Mn modified biochar (FMBC) was chemically constructed to immobilize Tl, V and As in contaminated soils. The results showed that compared with pristine biochar (BC), FMBC can achieve significantly higher passivation effects for the studied contaminated soils, which reduced the bioavailable Tl, V and As contents by 83.9%, 71.09% and 71.92%, respectively. The passivation of Tl, As, and V via FMBC application was partially attributed to a notable increase in pH, which enhances the availability of adsorptive sites. Further, the newly formed minerals, including cancrinite, gibbsite and Fe–Mn (hydr)oxides, serve as additional adsorbents, substantially reducing the mobility of Tl, V and As. Additionally, the oxidation of Tl(I) to Tl(III) by the Fe–Mn (hydr)oxide of FMBC significantly enhanced Tl immobilization, consequently diminishing its bioavailability. The findings suggest that significant environmental threats could be alleviated through the potential application of FMBC in treating Tl-As-V dominated contamination in soils, providing a new perspective for the sustainable utilization of industrially polluted soils. Highlights: FMBC effectively reduced the bioavailability of Tl, As and V in contaminated soils. Formation of new minerals during passivation can enhance adsorption of the studied elements. Effective immobilization of Tl may be mainly ascribed to oxidation of Tl(I) to Tl(III) by FMBC. [ABSTRACT FROM AUTHOR]

The research of environmental geochemistry entered the big data era. Environmental big data is a kind of new method and thought, which brings both opportunities and challenges to GIS-based spatial analysis in geochemical studies. However, big data research in environmental geochemistry is still in its preliminary stage, and what practical problems can be solved still remain unclear. This short review paper briefly discusses the main problems and solutions of spatial analysis related to the big data in environmental geochemistry, with a focus on the development and applications of conventional GIS-based approaches as well as advanced spatial machine learning techniques. The topics discussed include probability distribution and data transformation, spatial structures and patterns, correlation and spatial relationships, data visualisation, spatial prediction, background and threshold, hot spots and spatial outliers as well as distinction of natural and anthropogenic factors. It is highlighted that the integration of spatial analysis on the GIS platform provides effective solutions to revealing the hidden spatial patterns and spatially varying relationships in environmental geochemistry, demonstrated by an example of cadmium concentrations in the topsoil of Northern Ireland through hot spot analysis. In the big data era, further studies should be more inclined to the integration and application of spatial machine learning techniques, as well as investigation on the temporal trends of environmental geochemical features. [ABSTRACT FROM AUTHOR]