Reduction of antimicrobial resistance genes in wastewater through phytoremediation
Bibliographic record
Abstract
Developing strategies for the prevention of antimicrobial resistance genes (ARG) in wastewater treatment facilities, such as municipal wastewater sewage lagoons, is important to prevent ARG introduction and proliferation to the environment. While previous studies have shown promising results in reducing ARGs through biological and phytoremediation systems like Constructed Wetlands, the underlying mechanisms and the impact of water chemistry on ARG reduction remain poorly understood. This study used metagenomics to investigate the effect of three different biological wastewater treatments on the microbial community and resistome. The results indicated that each biological treatment significantly reduced ARGs and metal resistance genes (MRGs). A network analysis identified eight bacterial genera strongly associated with resistance genes, six of which belonged to the phylum Pseudomonadota and two to Campylobacteria. Of the six genera in Pseudomonadota, four were facultative anaerobes. A strong correlation was observed between iron (Fe) concentration in water and total ARG abundance ( r = 0.95), as well as between total metal concentration (across eight measured metals) and ARGs ( r = 0.93). Additionally, there was a significant correlation between ARG abundance and Fe resistance ( r = 0.86). This implies that high Fe and metal concentrations may favor ARG-harboring facultative bacteria by providing an electron donor for anaerobic respiration. This study suggests that phytoextraction of metals, including Fe, may play a significant role in reducing ARG abundance in treated water columns. These findings offer valuable insights into how metal removal and phytoremediation can effectively reduce ARG levels in wastewater.
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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 | 0.000 | 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) | 0.000 | 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".