We present a process model that predicts the removal of the antibiotic micropollutants, sulfamethoxazole (SMX), tetracycline (TCY), and ciprofloxacin (CIP), in an activated sludge treatment system. A novel method was developed to solve the inverse problem of inferring process rate, sorption, and correction factor parameter values from batch experimental results obtained under aerobic and anoxic conditions. Instead of spiking the batch reactors with reference substances, measurements were made using the xenobiotic organic micropollutant content of preclarified municipal sewage. Parent compound formation and removal were observed, and the model developed using the simulation software West showed limited efficiency to describe the selected micropollutants profiles, when growth substrate removal occurs. The model structure was optimized by accounting for competitive inhibition by readily biodegradable substrates on the cometabolic micropollutant biotransformation processes. Our results suggest that, under anoxic conditions, hydrophobicity-independent mechanisms can significantly impact solid-liquid partitioning that our model takes into account by using the sorption coefficient as a lumped parameter. Forward dynamic simulations were carried out to evaluate the developed model and to confirm it for SMX using data obtained in a full-scale treatment plant. Evaluation of measured and simulation results suggest that, robust model prediction can be achieved by approximating the influent load of chemicals biodegrading via a given parent compound, e.g., human conjugates, as an antibiotic mass that is proportional to the parent compound load.
ASJC Scopus subject areas
- Environmental Chemistry