Critical review of phytoremediation for the removal of antibiotics and antibiotic resistance genes in wastewater.
Bibliographic record
Abstract
Antibiotics in wastewater are a growing environmental concern. Increased prescription and consumption rates have resulted in higher antibiotic wastewater concentration. Conventional wastewater treatment methods are often ineffective at antibiotic removal. Given the environmental risk of antibiotics and associated antibiotic resistant genes (ARGs), finding methods of improving antibiotic removal from wastewater is of great importance. Phytoremediation of antibiotics in wastewater, facilitated through constructed wetlands, has been explored in a growing number of studies. To assess the removal efficiency and treatment mechanisms of plants and microorganisms within constructed wetlands for specific antibiotics of major antibiotic classes, the present review paper considered and evaluated data from the most recent published research on the topics of bench scale hydroponic, lab and pilot scale constructed wetland, and full scale constructed wetland antibiotic remediation. Additionally, microbial and enzymatic antibiotic degradation, antibiotic-ARG correlation, and plant effect on ARGs were considered. It is concluded from the present review that plants readily uptake sulfonamide, macrolide, tetracycline, and fluoroquinolone antibiotics and that constructed wetlands are an effective applied phytoremediation strategy for the removal of antibiotics from wastewater through the mechanisms of microbial biodegradation, root sorption, plant uptake, translocation, and metabolization. More research is needed to better understand the effect of plants on microbial community and ARGs. This paper serves as a synthesis of information that will help guide future research and applied use of constructed wetlands in the field antibiotic phytoremediation and wastewater treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".