Biological removal of sulfamethoxazole and 17 α-ethinylestradiol and the impact of ozone on biodegradability, estrogenicity and toxicity
Bibliographic record
Abstract
The potential development of antibacterial resistance and endocrine disruption has led to increased research investigating the removal of antibiotics like sulfamethoxazole (SMX) and estrogens like 17alpha-ethinylestradiol (EE2) during biological wastewater treatment (activated sludge). Lab-scale studies have been carried out by researchers using activated sludge samples and bacterial isolates to investigate the biodegradability of these pharmaceutical compounds with varying and sometimes contradictory results. Both SMX and EE2 can react quickly with ozone (O₃) thus the implementation of O₃ as a final disinfection step during wastewater (WW) treatment may also result in the removal and transformation of these pharmaceutical compounds. However, the identification and characteristics of the ozonation by-products formed as a result of this treatment has yet to be fully explored. Ozone has also been predicted to increase WW biodegradability, suggesting its potential as a pre-treatment to activated sludge. The work presented in this thesis proposes an innovative way to evaluate changes in biodegradability and investigate the potential impact of transformation products. The use of controlled mixtures of pure bacterial cultures to model the biodegradation of SMX and EE2 at lab-scale was studied. This approach ensures a consistent microbial population that can be precisely repeated throughout a set of experiments, allowing the direct comparison of results, which is not possible using activated sludge samples due to their variable composition. The results showed that specific individual bacteria (R. equi and R. rhodocrous) were capable of successfully degrading SMX and EE2, however when they were combined with other bacteria to form mixtures there were no additive or synergistic effects observed. Using the controlled bacterial mixtures to model the biodegradation of SMX and EE2 separately, we were able to investigate the effects of the addition of an excess carbon source as well as ozone pre-treatment on the biodegradability of these compounds. The results demonstrated that there were no overall trends of co-metabolism of either compound with the excess carbon source and that slight differences in the bacterial composition of the mixture used (±1 to 2 bacterial strains) can alter the degradation trends observed (i.e. rate of compound removal). In general, ozonation increased the biodegradation of SMX by the bacterial mixtures; however the removal of EE2 was decreased. This was explained by the formation of an ozonation by-product that was preferentially biodegraded over EE2. We also investigated the ozone by-products formed after the complete disappearance of EE2. Two of these by-products were identified as open phenolic ring structures, suggesting a decrease in estrogenic activity. Using the YES assay these by-products were determined to be significantly less estrogenic than EE2, however they were observed to be more toxic to male fetal rats (15.5. days post-conception) demonstrating a greater negative impact on testosterone secretion. This emphasizes that a better understanding of emerging treatments such as ozonation is necessary before applying them in water treatment since the by-products may exhibit a greater toxic effect than the untreated parent compound.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".