The aqueous concentrations of heavy metals in soils, sediments, and aquatic environments frequently are controlled by the dissolution and precipitation of discrete mineral phases. Contaminant uptake by organisms as well as contaminant transport in natural systems typically occurs through the solution phase. Thus, the thermodynamic solubility of contaminant-containing minerals in these environments can directly influence the chemical reactivity, transport, and ecotoxicity of their constituent ions. In many cases, Pb-contaminated soils and sediments contain the minerals anglesite (PbSO ₄ ), cerussite (PbCO ₃ ), and various lead oxides (e.g., litharge, PbO) as well as Pb ²⁺ adsorbed to Fe and Mn (hydr)oxides. Whereas adsorbed Pb can be comparatively inert, the lead oxides, sulfates, and carbonates are all highly soluble in acidic to circumneutral environments, and soil Pb in these forms can pose a significant environmental risk. In contrast, the lead phosphates [e.g., pyromorphite, Pb ₅ (PO ₄ ) ₃ Cl] are much less soluble and geochemically stable over a wide pH range. Application of soluble or solid-phase phosphates (i.e., apatites) to contaminated soils and sediments induces the dissolution of the “native” Pb minerals, the desorption of Pb adsorbed by hydrous metal oxides, and the subsequent formation of pyromorphites in situ . This process results in decreases in the chemical lability and bioavailability of the Pb without its removal from the contaminated media. This and analogous approaches may be useful strategies for remediating contaminated soils and sediments.

International audienceAbstract Nowadays, due to environmental concerns, fast on-site quantitative analyses of soils are required. Laser induced breakdown spectroscopy is a serious candidate to address this challenge and is especially well suited for multi-elemental analysis of heavy metals. However, saturation and matrix effects prevent from a simple treatment of the LIBS data, namely through a regular calibration curve. This paper details the limits of this approach and consequently emphasizes the advantage of using artificial neural networks well suited for non-linear and multi-variate calibration. This advanced method of data analysis is evaluated in the case of real soil samples and on-site LIBS measurements. The selection of the LIBS data as input data of the network is particularly detailed and finally, resulting errors of prediction lower than 20% for aluminum, calcium, copper and iron demonstrate the good efficiency of the artificial neural networks for on-site quantitative LIBS of soils