Accurate prediction of pharmaceutical concentrations in wastewater effluents requires that the specific biochemical processes responsible for pharmaceutical biodegradation be elucidated and integrated within any modeling framework. The fate of three selected beta blockers-atenolol, metoprolol, and sotalol-was examined during nitrification using batch experiments to develop and evaluate a new cometabolic process-based (CPB) model. CPB model parameters describe biotransformation during and after ammonia oxidation for specific biomass populations and are designed to be integrated within the Activated Sludge Models framework. Metoprolol and sotalol were not biodegraded by the nitrification enrichment culture employed herein. Biodegradation of atenolol was observed and linked to the activity of ammonia-oxidizing bacteria (AOB) and heterotrophs but not nitrite-oxidizing bacteria. Results suggest that the role of AOB in atenolol degradation may be disproportionately more significant than is otherwise suggested by their lower relative abundance in typical biological treatment processes. Atenolol was observed to competitively inhibit AOB growth in our experiments, though model simulations suggest inhibition is most relevant at atenolol concentrations greater than approximately 200 ng·L(-1). CPB model parameters were found to be relatively insensitive to biokinetic parameter selection suggesting the model approach may hold utility for describing pharmaceutical biodegradation during biological wastewater treatment.

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Graphical abstract

Extensive measurements of flaming and smoldering fires and nuisancelenvironmental sources were performed with Fourier Transform Infrared (lT-IR) spectroscopy of gas phase products. A neural network model was formulated using the so-called Learning Vector Quantization (LVQ) network approach. The LVQ approach contains input and output layers with a hidden layer being a Kohenen layer. The hidden layer learns and performs classification. The inputs to the network are concentrations (from FT-IR measurements) of eighteen (18) gas species. The outputs of the network are classification of the input data as a flaming fire, smoldering fire, nuisance or environmental source. The network was trained and tested using the test data collected during this project. The results were very successful as, among the 248 cases tested, only 12 cases were misclassified, mostly due to the difficulties in classifying the modes of combustion during a transition from a smoldering to a flaming fire. Each case represents the gas phase concentration data at a time step from one of the validation fires, which were different types of fires from the training set. A first generation fire detection system using FT-IR gas measurements and neural networks has been built and implemented.

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