Heavy metals in soil on spoil heap of an abandoned lead ore treatment plant, SE Congo-Brazzaville

Total concentrations of 4 heavy metals (Pb, Zn, Cu, and Mn), non-metal As and three reference elements (Ti, Fe and Al) of a soil profile on spoil heap were examined. 54 soil samples were collected in the soil profile at different depths: 15 to 45, 45 to 75, 75 to 105, 105 to 135 and 135 to 150 cm. The first 15 cm of top layer contained an accumulation of ores and was not sampled. Lead, zinc, copper, manganese and arsenic levels in the soil profile decreased from 11800 to 2000, 2400 to 1200, 270 to 90, 6700 to 750 and 1300 to 600 mg/kg, respectively. The levels of these trace elements were higher than the permissible limit level in soil according to European norms. The concentration levels of heavy metals and arsenic in the soil profile are listed in the following order: Pb > Mn > Zn > As > Cu. High positive correlations were observed between As, Cu, Zn and Al, Fe, Mn, Ti. Among the heavy metals, lead was not significantly correlated with Al, Fe and Ti. Evaluation of enrichment factor, geo-accumulation index and pollution load index values from the measured concentrations of heavy metals and continental crust values shows a very high contamination of the soil profile by the heavy metals studied and arsenic.Key words: Heavy metals, spoil heap, contamination, enrichment factor, geo-accumulation index, pollution load index

Analysis of the mechanical system transforming convertible kinetic energy into electrical energy in the Inga 2 hydroelectric power plant

In this article, we present the study of a mechanical system that can transform the kinetic energy of the river at the surface of the dam into mechanical rotational energy, which can be transformed into electrical energy. The mathematical model is based on the principle of conservation of energy. The power taken from the waves must be at least equal to the power needed to operate the turbine. Interdependence laws between the characteristic quantities of the turbine are obtained, which allows to better exploit the results of this work

WRF Sensitivity Analysis in Wind and Temperature Fields Simulation for the Northern Sahara and the Mediterranean Basin

Different configurations for the Weather Research and Forecasting (WRF) model were evaluated to improve wind and temperature fields predictions in the Northern Sahara and the Mediterranean basin. Eight setups, associated with different combinations of the surface layer physical parameters, the land surface model, and the grid nudging parameters, were considered. Numerical simulations covered the entire month of November 2017. Model results were compared with surface data from meteorological stations. The introduction of the grid nudging parameters leads to a general improvement of the modeled 10 m wind speed and 2 m temperature. In particular, nudging of wind speed parameter inside the planetary boundary layer (PBL) provides the most remarkable differences. In contrast, the nudging of temperature and relative humidity parameters inside the PBL may be switched off to reduce computational time and data storage. Furthermore, it was shown that the prediction of the 10 m wind speed and 2 m temperature is quite sensitive to the choice of the surface layer scheme and the land surface model. This paper provides useful suggestions to improve the setup of the WRF model in the Northern Sahara and the Mediterranean basin. These results are also relevant for topics related with the emission of mineral dust and sea spray within the Mediterranean region

CARI'96 : actes du 3ème colloque africain sur la recherche en informatique = CARI'96 : proceedings of the 3rd African conference on research in computer science

Les méthodes d'adjoint pour l'assimilation de données ont été appliquées récemment aux modèles numériques de prévision à grande échelle. Ceci conduit à la minimisation d'une fonction coût, définie comme une somme discrète en espace et en temps des différences entre la prévision du modèle et les observations connues. Notre but est ici d'utiliser de telles méthodes pour l'assimilation de données au moyen d'un modèle à mésoéchelle. (Résumé d'auteur