Measuring and predicting the fate of contaminants of emerging concern during wastewater treatment
Bibliographic record
Abstract
The presence of contaminants of emerging concern (CECs) in the aquatic environment and the associated proven toxic impacts have increasingly alarmed researchers. The discharge of wastewater into surface water was identified as the major source for the release of CECs into the environment. Despite the available data on the removal of CECs in wastewater treatment plants (WWTPs) in the literature, previous studies had several shortcomings, resulting in an inaccurate prediction of the CEC fate. This PhD project aimed at monitoring the fate of CECs in different treatment steps with special consideration to the hydrodynamics of the treatment units and adsorption to sludge. The project also aimed at developing and calibrating a model to predict the fate of target CECs in the most widespread secondary treatment technology: the activated sludge process. Among the various classes of CECs, this thesis focused on the widely consumed pharmaceuticals, personal care products, drugs of abuse, hormones, stimulants and artificial sweeteners based on evidences of their presence in treated wastewater implying their inefficient removal during treatment. Recently, the hydraulic characteristics of WWTPs were demonstrated to bias the calculations of CEC removal if not accounted for. To address this issue, the fractionated approach that integrates hydraulic modelling and improved sampling strategies was recently proposed. In order to verify the capability of the fractionated approach at capturing the hydraulic differences, the temperature and electric conductivity of wastewater were used as tracers to model the hydraulics of two full-scale WWTPs. Results demonstrated that a distinctive model was necessary to describe the hydraulics in each WWTP, requiring different number of days for sampling, as well as different CEC removal calculations.In order to explore the contribution of the different fate pathways to the removal of CEC during wastewater treatment, a sampling campaign was performed in a WWTP using an optimized sampling strategy based on the fractionated approach to collect and chemically analyze both wastewater and sludge samples. This allowed performing a mass balance on the incoming load of CECs, which was carried out for the first time with consideration to the hydraulic characteristics. Results indicated that for 21 out of 24 investigated CECs, degradation was the major removal process, with sorption accounting for <10% of the input CEC load fate in the primary clarifier and <5% in the activated sludge process. Most target CECs (22 out of 25) were relatively persistent in rotating biological contactors and sand filtration compared to activated sludge treatment. In order to predict the fate of CECs, a fate model based on the widespread Activated Sludge Model No. 2d (ASM2d) was further modified to better describe the CEC fate processes in aeration tanks. The state-of-the-art Bürger-Diehl secondary clarifier model was extended to include the CEC fate processes for the first time. The resulting secondary treatment model was calibrated to predict the fate of four target CECs that belong to different classes and undergo different fate processes. Results from global sensitivity analysis indicated that depending on the contaminant's properties, a different set of parameters deserved more attention. Further, dynamic sensitivity analysis should be taken into consideration in future sampling campaigns for model calibration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".