Bioconcentration of triclosan, methyl-triclosan, and triclocarban in the plants and sediments of a constructed wetland
Bibliographic record
Abstract
Constructed wetlands are a potential method for the removal of two pharmaceutical and personal care products from wastewater effluent. Triclosan (TCS; 5-chloro-2-[2,4-dichlorophenoxy]phenol) and triclocarban (TCC; 3,4,4'-trichlorocarbanillide) are antimicrobial agents added to a variety of consumer products whose accumulation patterns in constructed wetlands are poorly understood. Here, we report the accumulation of TCS, its metabolite methyl-triclosan (MTCS; 5-chloro-2-[2,4-dichlorophenoxy]), and TCC in wetland plant tissues and sediments. Three wetland macrophytes: Typha latifolia, Pontederia cordata, and Sagittaria graminea were sampled from a constructed wetland in Denton, Texas, USA. MTCS concentrations were below the method detection limit (MDL) for all species. TCS root tissue concentrations in T. latifolia were significantly greater than root concentrations in P. cordata (mean±SE in ng g(-1): 40.3±11.3 vs. 15.0±1.9, respectively), while for TCC, shoot tissue concentrations in S. graminea were significantly greater than in T. latifolia (22.8±9.3 vs. 9.0 (MDL), respectively). For both TCS and TCC, T. latifolia root tissue concentrations were significantly greater than shoot concentrations (TCS: 40.3±11.3 vs. 17.2±0.2, TCC: 26.0±3.6 vs. 9.0, (MDL)). TCC concentrations in P. cordata roots were significantly greater than in shoots (34.4±5.3 vs. 15.4±2.8, respectively). TCS concentrations in T. latifolia roots and sediments and TCC concentrations in sediments generally decreased from wetland inflow to outflow. To our knowledge, this is the first study documenting species and tissue specific differences in the accumulation of TCS and TCC in plants from an operational constructed wetland. The species specific differences in bioaccumulation suggest TCS and TCC removal from constructed wetlands could be enhanced through targeted plantings.
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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".