Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4472159 | Waste Management | 2012 | 8 Pages |
An assessment of the risk to human health and the environment associated with the presence of organic contaminants (OCs) in landfills necessitates reliable predictive models. The overall objectives of this study were to (1) conduct column experiments to measure the fate and transport of an OC in a simulated solid waste mixture, (2) compare the results of column experiments to model predictions using HYDRUS-1D (version 4.13), a contaminant fate and transport model that can be parameterized to simulate the laboratory experimental system, and (3) determine model input parameters from independently conducted batch experiments. Experiments were conducted in which sorption only and sorption plus biodegradation influenced OC transport. HYDRUS-1D can reasonably simulate the fate and transport of phenol in an anaerobic and fully saturated waste column in which biodegradation and sorption are the prevailing fate processes. The agreement between model predictions and column data was imperfect (i.e., within a factor of two) for the sorption plus biodegradation test and the error almost certainly lies in the difficulty of measuring a biodegradation rate that is applicable to the column conditions. Nevertheless, a biodegradation rate estimate that is within a factor of two or even five may be adequate in the context of a landfill, given the extended retention time and the fact that leachate release will be controlled by the infiltration rate which can be minimized by engineering controls.
► Anaerobic column experiments were conducted at 37 °C using a simulated waste mixture. ► Sorption and biodegradation model parameters were determined from batch tests. ► HYDRUS simulated well the fate and transport of phenol in a fully saturated waste column. ► The batch biodegradation rate and the rate obtained by inverse modeling differed by a factor of ∼2. ► Tracer tests showed the importance of hydrodynamic parameters to improve model estimates.