Removal of pesticides from aqueous solution: Quantitative relationship between activated carbon characteristics and...

The adsorption of pesticides (atrazin, atrazin-desethyl and triflusulfuron-methyl) from aqueous solution is performed by activated carbon fibers (ACF) and granular activated carbons (GAC) in static and dynamic reactors, in order to study the co-influence of adsorbent and adsorbate characteristics on the adsorption mechanisms. First, mono-component adsorption equilibrium is carried out in a batch reactor for a wide range of concentrations (from 5 microg 1(-1) to 21.4 mg 1(-1)). Classic models, like Freundlich and Langmuir equations, are applied: the maximum adsorption capacities are high, ranging between 63 and 509 mg g(-1). The comparison of single-solute isotherms tends to confirm the decisive role of the adsorbent properties in the adsorption capacity of pesticides by the activated carbons: the performance of ACF is significantly higher than that of GAC due to a narrower pore size distribution of fibers in the area of micropores. Furthermore, their small diameter (10 microm compared with 1 mm for grains) enables faster adsorption kinetics because of the larger surface area exposed to the fluid. The influence of adsorbate size is also demonstrated. A multiple linear regression enables the co-influence of adsorbent and adsorbate properties to be quantified, a relationship being assessed between Langmuir maximum adsorption capacity and pesticide molecular weight and adsorbent diameter (R2 = 0.90). Secondly, the adsorption of the three pesticides is studied in a dynamic reactor: in this case, the influence of operating conditions (inlet concentration C0, flow velocity U0) is also taken into account. As the initial concentration or flow velocity decreases, the column performance significantly improves. Both operating factors are included in a multiple linear regression (R2 = 0.91) used to predict saturation adsorption capacity, with molecular weight and particle diameter being again designed as influent explicative variables.