Biodegradation behaviour of pharmaceutical compounds and selected metabolites in activated sludge. A forecasting decision system approach

Abstract

Society's support upon chemicals over the last few decades has led to their increased production, application and discharge into the environment. Wastewater treatment plants (WWTPs) contain a multitude of these chemicals such us; pharmaceutical compounds (PCs). Often, their biodegradability by activated sludge microorganisms is significant for their elimination during wastewater treatment. In this paper the focus is laid on two PCs carbamazepine (CBZ) and diclofenac (DCF) and their main transformation products (TPs). Laboratory degradation tests with these two pharmaceuticals using activated sludge as inoculum under aerobic conditions were performed and microbial metabolites were analyzed by liquid chromatography-mass spectrometry (LC/MS-MS). In two different Mixed liquid Suspended Solids (MLSS) concentrations the biodegradability by activated sludge of CBZ and DCF were evaluated. Also, this article proposes a decision support system to optimize the prediction process of this type of pharmacological compounds. A study and analysis of the techniques of Support Vector Machine, Random Forest, Decision Trees and Multilayer Perceptron Network is carried out to select the most reliable and accurate predictor for the decision system. There are not significant differences in the removal of DCF with 30 mg MLSS/L and 60 mg MLSS/L. DCF was better removed than CBZ in all experiments studied. The TP detected in the samples were mainly 4-OH-DCF for DCF and 10, 11 EPOXICBZ for CBZ. The results show that the best models are obtained with Random Forest and Multilayer Perceptron Network techniques, with a model fit of more than 95% for both carbamazepine and diclofenac metabolites. Obtaining a root means square errors of 0.80 µg/L for the metabolite 4-OH-DCF for DCF with the technique Random Forest and a root means square errors of 1.13 µg/L for the metabolite 10, 11 EPOXICBZ for CBZ with the Multilayer Perceptron Network technique.