Michael A. Butkus

Soil-washing design methodology for a lead-contaminated sandy-soil

Soils located near high traffic roadways, particularly where renovations have taken place, typically exhibit elevated levels of trace metals in the upper soil horizon. Regulators are currently seeking an efficient method of site characterization and treatment system design which will lend itself to timely and environmentally efficacious clean-up. The soil investigated in this study was a silty sand collected near a bridge abutment for a major interstate highway. The soil had a total lead content of 1392 mg/kg. In addition, the soil contained a considerable fraction of organic carbon (approximately 6.3%). A sequential chemical extraction indicated that a fraction of contaminants were in labile soil phases and thus amenable to chemical extraction (soil-washing). A soil washing design methodology is presented based on surface chemistry and equilibrium stage operation. In this work, a double layer surface complexation model was used to describe equilibrium sorption behavior and a preliminary design of an ex-situ counter-current equilibrium stage extraction process is presented. Model calibration was conducted using sorption data obtained from a 1:40 solid to liquid ratio (s/l), adsorption edge. Model validation was accomplished with batch titration data and a 1:20 s/l, adsorption edge. The model accurately predicted leachable lead concentrations over a wide pH range. The required number of ideal equilibrium stages was highly sensitive to pH.

A comparative study of neural network and mechanistic models for surface complexation

Abstract This paper demonstrates the use of a feed-forward neural network model to quantify the partitioning of phosphate onto water treatment residual (WTR) as a function of pH. Reasonably good results were obtained with a limited amount of experimental data. The neural network models were essentially as good as the specific mechanistic model used. Comparison of the neural network models with simple models obtained from statistical regression shows the neural network models to be superior. Quantification of the distribution of phosphate in this system may allow accurate prediction of available phosphate in a land application scenario. In surface complexation studies where mechanistic models are not available, it is recommended that neural network models be used.