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Studies on the utilization of low cost adsorbents for removal of heavy metals from wastewaters are gaining attention. Activated carbons were developed from Apricot stone, by chemically treating with sulfuric acid. Batch adsorption experiments were performed to find out the effective lead removal at different metal ion concentrations, adsorbent size and dosage. Operational parameters such as pH, agitation time and adsorbent concentration, initial ion concentration and particle size were also studied. Adsorption data fitted well with the Langmuir and Freundlich models. However, Freundlich isotherm displayed a better fitting model than Langmuir isotherm because of the higher correlation coefficient that the former exhibited. Apricot Adsorption kinetics data were modeled using the pseudo-first and pseudo-second-order models. The results indicate that the second-order model best describes adsorption kinetic data. The estimated maximum capacities of lead ions adsorbed by Apricot stone activated with sulphuric acid were 21.38 mg g-1. In addition, the thermodynamic parameters, standard free energy (ΔG0), standard enthalpy (ΔH0), standard entropy (ΔS0) of the adsorption process were calculated., Moroccan Journal of Chemistry, Vol 2, No 5 (2014): Proceedings of the fourth edition of the International Congress Water, Waste & Environment

Multivariate statistical techniques, cluster and principal component analysis were applied to the data on groundwater quality of Ain Azel plain (Algeria), to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the hydrochemistry at the plain scale. Q-mode hierarchical cluster analysis grouped 54 groundwater samples into three clusters, that is, relatively less saline water (group 1), mixed water (group 2) and blended water (group 3), based on the similarity of groundwater quality characteristics. Principal component analysis, applied to the data sets of the three different groups obtained from hierarchical cluster analysis, resulted in four, five and three latent factors explaining 83.21, 83.36 and 87.30% of the total variance in groundwater quality data sets of group 1, group 2 and group 3, respectively. The varifactors obtained from PCA indicate that the parameters responsible for groundwater quality variations are mainly related to presence and dissolution of some carbonate, dolomitic and evaporite minerals; natural processes and water-rock interaction in the three water types. The results of this study clearly demonstrate the usefulness of multivariate statistical analysis in hydro chemical.Key words: Cluster analysis, principal component analysis, hydrochemistry, Ain Azel plain, Algeria.