Evaluation of pollution by heavy metals of an abandoned Pb-Zn mine in northern Tunisia using sequential fractionation and geostatistical mapping

This study investigates the contents of lead, zinc, and cadmium in 109 near-surface soil samples collected around the abandoned mine of Fedj Lahdoum, northern Tunisia, to assess the risk of pollution they generate. The study involved some analytical procedures such as pH measurements, X-ray diffraction techniques, sequential fractionation, and geostatistical mapping using the ordinary Kriging techniques. The sequential fractionation revealed that the bioavailability of Pb, Zn, and Cd follows the orders F5?>?F3 » F4?>?F2 » F1, F5?>?F3 » F4 » F2?>?F1 and F5?>?F2 » F4?>?F1, respectively; their associations with organic matter and residual sulfides (F4) are relatively low. However, their high cumulated contents are dominantly associated with the residual (F5) and reducible (F3) fractions. The geostatistical mapping was endeavored to predict the spatial distribution of the studied heavy metals at unsampled sites and to produce a cumulated risk map of soil pollution. The latter is discussed with emphasis of the main factors responsible for the scattering of the pollution as much as the landscape conditions, the chemical composition of the mine tailings, the surface drainage of meteoric water and the wind. This study provides insight into the delineation of the spatial spreading of Pb, Zn, and Cd around the abandoned mine Fedj Lahdoum and their surrounding urban areas. It reveals that the mine infrastructure areas encompassing both extraction and processing and tailing deposition areas are the main sources of contamination. And the landscape conditions together with the surface drainage of meteoric water and the wind are the main factors responsible for the scattering of the pollution.

     

Dynamic extreme values modeling and monitoring by means of sea shores water quality biomarkers and valvometry

Water quality can be evaluated using biomarkers such as tissular enzymatic activities of endemic species. Measurement of molluscs bivalves activity at high frequency (e.g., valvometry) during a long time period is another way to record the animal behavior and to evaluate perturbations of the water quality in real time. As the pollution affects the activity of oysters, we consider the valves opening and closing velocities to monitor the water quality assessment. We propose to model the huge volume of velocity data collected in the framework of valvometry using a new nonparametric extreme values statistical model. The objective is to estimate the tail probabilities and the extreme quantiles of the distribution of valve closing velocity. The tail of the distribution function of valve closing velocity is modeled by a Pareto distribution with parameter ??_t,τ, beyond a threshold τ according to the time t of the experiment. Our modeling approach reveals the dependence between the specific activity of two enzymatic biomarkers (Glutathione-S-transferase and acetylcholinesterase) and the continuous recording of oyster valve velocity, proving the suitability of this tool for water quality assessment. Thus, valvometry allows in real-time in situ analysis of the bivalves behavior and appears as an effective early warning tool in ecological risk assessment and marine environment monitoring.