No abstract

Understanding the environmental fate of human and animal pharmaceuticals and their risk assessment are of great importance due to their growing environmental concerns. Although there are many potential pathways for them to reach the environment, effluents from sewage treatment plants (STPs) are recognized as major point sources. In this study, the removal efficiencies of the 43 selected priority pharmaceuticals in a conventional STP were evaluated using two simple models: an equilibrium partitioning model (EPM) and STPWIN TM program developed by US EPA. It was expected that many pharmaceuticals are not likely to be removed by conventional activated sludge processes because of their relatively low sorption potential to suspended sludge and low biodegradability. Only a few pharmaceuticals were predicted to be easily removed by sorption or biodegradation, and hence a conventional STP may not protect the environment from the release of unwanted pharmaceuticals. However, the prediction made in this study strongly relies on sorption coefficient to suspended sludge and biodegradation half-lives, which may vary significantly depending on models. Removal efficiencies predicted using the EPM were typically higher than those predicted by STPWIN for many hydrophilic pharmaceuticals due to the difference in prediction method for sorption coefficients. Comparison with experimental organic carbon-water partition coefficients (Kocs) revealed that log KOW-based estimation used in STPWIN is likely to underestimate sorption coefficients, thus resulting low removal efficiency by sorption. Predicted values by the EPM were consistent with limited experimental data although this model does not include biodegradation processes, implying that this simple model can be very useful with reliable Koc values. Because there are not many experimental data available for priority pharmaceuticals to evaluate the model performance, it should be important to obtain reliable experimental data including sorption coefficients and biodegradation rate constants for the prediction of the fate of the selected pharmaceuticals.