Speciation of metals by sequential extractions of agricultural soils located near a dumpsite for prediction of element availability to vegetables.

In this paper heavy metal pollution has been investigated by comparing total concentrations and speciation of heavy metals (Cr, Cu, Mn, Zn, Pb and Sr) in soils from four agricultural fields (S1, S2, S3, S4) located in the direct vicinity of the largest landfill in Senegal. The sequential test allowed discriminating between various fractions of heavy metals, namely the acid-extractable fraction, the fraction bound to Fe oxides, the fraction bound to organic matter and the residual fraction. It was proven that the most important fractions of metals (Cr, Cu, Pb, Mn, Sr and Zn) are bound to the residual fraction, more than 50% for most sites, and thus they may be relatively hardly liberated into the environment. The results also showed that the metal pollution in S3 and S4 were more severe than in other sampling sites, especially for Mn and Zn. In addition, the exchangeable fraction, which is the most available, represents from 10 to 47% of the total concentration for Sr, Mn and Zn, indicating that a non-negligible part of these elements may be easily released. Matrix correlation between soil characteristics and the elemental concentrations was tested to study and to detect a possible trend of metal mobilization from organic matter or oxides to agricultural soils. Vegetable grown from the four sampling sites were analyzed, Cr, Zn and Pb concentrations were high in many studied foodstuffs, (up to 54 mg/kg; 45.8 mg/kg and 3.4 mg/kg for Mn, Zn and Pb respectively) and higher than the threshold values of FAO/WHO. Calculation of hazard indexes suggested no potential health risks associated with consuming the vegetables with the exception of cassava and cassava leaves.

Investigating the metal contamination status from recycling e-waste sites from Dakar, Senegal.

In Senegal, processes employed for recycling e-wastes result in release of so-called heavy metals in the environment. In this study, the metal distribution and concentrations of soils collected from four e-waste recycling areas located in the Dakar area are presented. Measurements are performed using portable X-ray fluorescence and laser-induced breakdown spectroscopy (LIBS) to obtain a complete analysis of the soil, including major and trace elements (i.e., mg/kg). Levels of zinc, copper, manganese, and iron in soils are high but contrasted ranging from 66 mg/kg for Cu in Mbeubeuss landfill to more than 1000 mg/kg for Mn and 10,000 mg/kg for Zn. Lower values are obtained for chromium and lead (Cr = 207 mg/kg and Pb = 181 mg/kg). The most elevated average lead values (i.e., 2935 mg/kg and 1427 mg/kg) are obtained from the e-waste recycling sites located in the urban area of Dakar: Reubeuss and Pikine, respectively. Specific element fingerprints are obtained from each of the studied areas. To evaluate human exposure to the main metals, vegetables grown outside the large e-waste landfill were analyzed. The values obtained for Cr, Cu, Fe, Mn, Sr, and Zn are low, ranging from 0.11 to 9.66 mg/kg for chromium in turnip and iron in beetroots, respectively. The calculated health risk index remains below unit for all vegetables, which means no potential risk of vegetable consumption for human health. The data provided useful information to estimate contaminations and their origin. Therefore, in order to ensure food safety, continuous monitoring of the vegetables grown in the vicinity of recycling areas is needed.

Tea geographical origin explained by LIBS elemental profile combined to isotopic information.

The combined LIBS and ICP HRMS analysis of 13 tea samples are studied in view of identification of tea geographical origin. The elemental signature provided by LIBS spectra is treated by principal component analysis followed by partial least square discriminant analysis and factorial discriminant analysis. Selected element lines are found efficient to discriminate most sample groups. Data analysis model is improved by variable selection and the isotopic ratio 11B/10B was employed to improve the prediction capacity of the model. The alkaline earth: Ba, Ca, Mg, Sr and alkaline Rb, Na are easily detected by the LIBS system and these elements are important to classify sample according to their geographical origin. Minor elements like P, S, Fe, B … also bring discriminant information. A five clusters model gave best correct identification in a cross validation test (94.2%). This method also allowed to identify the origin of four unknown teas. In this study the use of FDA or PLS DA after the PCA examination of the LIBS/ICP MS data led to similar conclusions for fast classification of the tea samples and identification of the geographical origin of the four unknown teas.