Integrated Geochemical Assessment of Soils and Stream Sediments to Evaluate Source-Sink Relationships and Background Variations in the Parauapebas River Basin, Eastern Amazon

Role of the EM clustering method in determining the geochemical background of As and Cr in soils: a case study in the north of Changchun, China

Determining the geochemical background for heavy metals is vital in soil management activities. Although many statistical methods for geochemical background determination have been proposed, the multi-population problem of geochemical data, primarily regional ones, derived mainly from mixing multiple populations belonging to various geological sources or processes, needs to be better addressed. In this study, the Expectation-Maximization (EM) algorithm was employed to separate multiple populations in a 1:250,000 scale regional geochemical data set of soils in a lithologically complex region in the north of Changchun, China. The data set included 3746 surface soil samples analyzed for SiO2, K2O, Al2O3, CaO, La, Rb, Y, Ti, Ce, V, Cr, and As. The potential high-risk areas of As and Cr were determined before and after the separation of multiple populations. The comparison results show that the EM clustering method can efficiently separate multiple populations and determine soil geochemical background more reasonably, thus eliminating false contamination that is easily misidentified and better revealing concealed contamination that is challenging to detect.

Magnetic susceptibility as a proxy for detection of total petroleum hydrocarbons in contaminated wetlands

Soil petroleum hydrocarbon contamination in the wetlands could cause ecological risk, especially through leakage into water reservoirs. So, the detection of the spatial variability of total petroleum hydrocarbons (TPH) in these soils is very crucial. The variability of TPH and its associations with magnetic susceptibility (χ_lf) in contaminated soils around the Shadegan pond in southern Iran was investigated. TPH varied from 2.1 to 18.1% (w/w), by the variation of χ_lf from 14.08 to 713.93 × 10^-8 m³ kg^-1. The highest variability (coefficient of variation, CV = 107.12%) was obtained for χ_lf indicating significant impacts of magnetic minerals induced by crude oil contamination. High positive correlations were detected among TPH, χ_lf, and different forms of iron (Fe_d: extracted by CBD, Fe_o: extracted by oxalate, and Fe_t: total iron). The results of mineralogy by powdery XRD and scanning electron microscopy (SEM), also revealed the formation of ferrimagnetic minerals (magnetite, maghemite) during the biodegradation of petroleum hydrocarbons. The stepwise multiple regression analysis showed that χ_lf and Fe_d made a great contribution and could explain about 74% of TPH variability in the studied sites. For the extension of this cost-effective and rapid technique, further work is needed to assay saturation isothermal remnant magnetization and isothermal remanet magnetization in contaminated sites.

Assessment and Remediation of Soils Contaminated by Potentially Toxic Elements (PTE)

Potentially toxic elements (PTE) can cause significant damage to the environment and human health in the functions of mobility and bioavailability [...]