Heavy metals are present in nature and industrial wastewater. Due to their mobility in natural water ecosystems and toxicity, the presence of heavy metals in surface water and ground water has become a major inorganic contamination problem. Discharge and treatment of industrial wastewater containing heavy metals are important issues in environmental protection. If unrecognized or inappropriately treated, heavy-metal toxicity can result in serious hazards. Conventionally these heavy metals are removed from wastewater with physical and chemical methods such as chemical precipitation, chemical oxidation and reduction, ion exchange, filtration, electrochemical treatment... However, these methods may result in the high capital investment and operating cost, incomplete removal, low selectivity, high-energy consumption. Recent research efforts have been made on the development of efficient and low cost metal organic adsorbents in treatment of industrial wastewater. The present work describes the study on the biosorption Lead (II), Copper (II) and Cadmium (II) ions onto pretreated aerobic granular sludge from single component and multi systems in equilibrium systems and in batch sorption experiment. Results showed that Ca-biomass was an efficient biosorbent for the removal of Pb2+, Cu2+ and Cd2+ ions from an aqueous solution. The single-metal sorption uptake capacity of the aerobic granules for Pb2+ was inhibited by the presence of Cd2+ and Cu2+ ion. The decreasing affinity of aerobic granular sludge for the three metal ions was established as: Pb2+ Cu2+ Cd2+. Factors such as hydration effects and electronegative charge may contribute to the competitive sorption results. The metal removal capacities of aerobic biomass were six times higher than those of activated Carbon
especially in the Cd2+ removal capacity was very high. The Langmuir model was better than the Freundlich model in regard of describing the Pb2+ Cu2+ and Cd2+ ion biosorption on raw biomass and Ca-biomass. The maximum capacity Qmax determined by the Langmuir model fit to experimental data.