Uncertainty and sensitivity analyses using GLUE when modeling inhibition and pharmaceutical cometabolism during nitrification

The influence of uncertainties in biokinetic parameters for ammonia and nitrite oxidizing bacteria on the performance of a two-step nitrification model was evaluated using the Generalized Uncertainty Estimation (GLUE) technique. The predictive capability of behavioral simulations generated using GLUE was assessed utilizing data from experiments comparing nitrification in the presence and absence of two pharmaceuticals - atenolol or sotalol. Results suggest that GLUE cannot account for model structural error arising when ammonia oxidation is competitively inhibited. Use of a competitive inhibition model for ammonia oxidation (i.e., correction of the model structural error), however, enables GLUE to generate meaningful uncertainty intervals. While GLUE is used in the present study, other uncertainty analysis techniques are likely to be similarly unable to account for model structural errors. Thus, results from this study emphasize the importance of model selection for efficacious uncertainty analysis. The behavioral simulations generated using GLUE based on application of the correct model was subsequently used to evaluate the sensitivity of transformation coefficients employed to describe the cometabolism of atenolol by ammonia oxidizing bacteria (AOB). Sensitivity was assessed by computing nonparametric elasticities of the cometabolism transformation coefficients to biokinetic parameters selected to describe nitrification in a novel application of a generalized nonparametric analysis. Results suggest that the AOB-growth related transformation coefficient of atenolol is relatively insensitive to variation in ammonia and nitrite oxidizing biokinetic parameters. In contrast, the non-growth related transformation coefficient describing atenolol cometabolism appears to be sensitive to the specific growth rate of AOB. Elasticities are used to assess whether estimates of atenolol-AOB cometabolic biodegradation coefficients from lab-scale experiments could be used more generally. This novel application of elasticities to biological wastewater process modeling suggests that seasonal temperature variations may be an important factor in pharmaceutical biodegradation during biological wastewater treatment.

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