132 research outputs found
Removal of Antibiotics in Biological Wastewater Treatment Systems—A Critical Assessment Using the Activated Sludge Modeling Framework for Xenobiotics (ASM-X)
Many
scientific studies present removal efficiencies for pharmaceuticals
in laboratory-, pilot-, and full-scale wastewater treatment plants,
based on observations that may be impacted by theoretical
and methodological approaches used. In this Critical Review, we evaluated factors influencing observed removal efficiencies of three
antibiotics (sulfamethoxazole, ciprofloxacin, tetracycline) in pilot-
and full-scale biological treatment systems. Factors assessed include
(i) retransformation to parent pharmaceuticals from e.g., conjugated metabolites
and analogues, (ii) solid retention time (SRT), (iii) fractions sorbed
onto solids, and (iv) dynamics in influent and effluent loading. A
recently developed methodology was used, relying on the comparison
of removal efficiency predictions (obtained with the Activated Sludge
Model for Xenobiotics (ASM-X)) with representative measured data
from literature. By applying this methodology, we demonstrated that
(a) the elimination of sulfamethoxazole may be significantly underestimated
when not considering retransformation from conjugated metabolites,
depending on the type (urban or hospital) and size of upstream catchments;
(b) operation at extended SRT may enhance antibiotic removal, as shown
for sulfamethoxazole; (c) not accounting for fractions sorbed in influent
and effluent solids may cause slight underestimation of ciprofloxacin
removal efficiency. Using tetracycline as example substance, we ultimately
evaluated implications of effluent dynamics and retransformation on
environmental exposure and risk prediction
A systematic model identification method for chemical transformation pathways – the case of heroin biomarkers in wastewater
Abstract This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology
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