Modeling desorption *kinetics in soils columns
The influence of desorption resistance on desorption kinetics exhibited by sorbed organic contaminants was investigated due to its importance in remediation. Experimental and mathematical tools were used to evaluate the effect of partial reversibility of the sorption process. Kinetic parameters in batch and column experiments were compared to assess the relative importance of differential sorption and desorption. Three natural sandy soils, which included two surface soils and one of an aquifer origin, were selected as natural sorbents. Naphthalene was used as a representative hydrophobic organic compound (HOC) due to its higher solubility and lower hydrophobicity compared to other 16 polycyclic aromatic hydrocarbons (PAHs) included in EPA's list of priority pollutants. A series of batch and column experiments using different techniques were conducted with equilibration time as a primary variable.
This study provides an improved understanding of desorption kinetics in batch and column systems. The results support the hypothesis for the existence of three desorption regimes in columns for a soil-contaminant combination, given that the same observational regimes exist in batch systems. The results also indicate that packing the aggregate material in soil columns limits desorption as a result of an increase in diffusion path lengths, which causes a greater fraction of the soil matrix to behave in a rate-limited mode.
The experimental evidence also suggests that a small fraction of contaminant becomes desorption resistant immediately on contact with the solid phase. An increase in the soil-contaminant contact time results in a significant shift of contaminant from the rate-limited domain to the desorption-resistant domain. However, the effect of contact time on desorption rate coefficients, which describe desorption from the rate-limited domain, is not significant.
Application of mathematical models to describe desorption in batch and column systems confirmed the importance of representing observational regimes with a compatible mathematical description for improved predictions and highlights the need for models based on time-independent parameters. This study also reveals that an increase in the number of fitting parameters other than the minimum required to represent the observational regimes is not justified.