Integrated modelling of two xenobiotic organic compounds

Simultaneous elimination of antibiotics and antibiotics resistance genes in nitritation of source-separated urine

Antibiotics in human urine could accelerate dissemination of antibiotics resistance genes (ARGs), posing potential threat to sewage. The nitritation of source-separated urine was a critical step to realize the urine resourcelization and nitrogen stabilization. However, the synergic control on antibiotics and ARGs during urine nitritation was unrevealed. This study investigated the removal profiles of five typical antibiotics and the shifts of microbial community and ARGs during stable nitritation. The result showed that sulfamethoxazole and roxithromycin were effectively eliminated with high removal efficiency of (95 ± 5) % and (90 ± 10) %, followed by enrofloxacin with removal efficiency of (60 ± 5) %, whereas trimethoprim and chloramphenicol showed low removal efficiency of less than 40 %. Ammonia oxidation bacteria and heterotrophic bacteria equally contributed to elimination of sulfamethoxazole with a high biodegradation rate of 0.1534 L/gVSS·h, while sorption and biodegradation jointly promoted other antibiotics removal. The total relative abundance of top 25 bacteria genera was decreased by 10 %. The total relative abundance of top 30 ARGs was decreased by more than 20 %, which was corresponding to the variation of bacterial community. The findings in this research would get a deeper insight into the eliminating antibiotics and controlling ARGs dissemination during nitritation of source-separated urine.

Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2-4h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The 'traditional' variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities.

Impacts of competitive inhibition, parent compound formation and partitioning behavior on the removal of antibiotics in municipal wastewater treatment

We present a process model that predicts the removal of the antibiotic micropollutants, sulfamethoxazole (SMX), tetracycline (TCY), and ciprofloxacin (CIP), in an activated sludge treatment system. A novel method was developed to solve the inverse problem of inferring process rate, sorption, and correction factor parameter values from batch experimental results obtained under aerobic and anoxic conditions. Instead of spiking the batch reactors with reference substances, measurements were made using the xenobiotic organic micropollutant content of preclarified municipal sewage. Parent compound formation and removal were observed, and the model developed using the simulation software West showed limited efficiency to describe the selected micropollutants profiles, when growth substrate removal occurs. The model structure was optimized by accounting for competitive inhibition by readily biodegradable substrates on the cometabolic micropollutant biotransformation processes. Our results suggest that, under anoxic conditions, hydrophobicity-independent mechanisms can significantly impact solid-liquid partitioning that our model takes into account by using the sorption coefficient as a lumped parameter. Forward dynamic simulations were carried out to evaluate the developed model and to confirm it for SMX using data obtained in a full-scale treatment plant. Evaluation of measured and simulation results suggest that, robust model prediction can be achieved by approximating the influent load of chemicals biodegrading via a given parent compound, e.g., human conjugates, as an antibiotic mass that is proportional to the parent compound load.

Modélisation dynamique du comportement des métaux lourds dans des stations d’épuration

Le but de ce travail était de développer un modèle permettant de décrire le comportement des métaux lourds, en plus des polluants traditionnels, dans une station d’épuration par boues activées et pouvant être utilisé pour sa conception et son optimisation. Le modèle, qui considère la sorption des métaux lourds sur des matières en suspension comme processus réactionnel, a été évalué à partir de données recueillies durant dix jours dans un système réel de traitement par boues activées. Les résultats démontrent la sensibilité du modèle face aux importantes variations instantanées des concentrations de métaux lourds dans l’affluent. Il est néanmoins capable de prévoir l’évolution des concentrations de métaux lourds à l’intérieur de la station et à l’effluent.