Trace Metal Accumulation in Sediments and Benthic Macroinvertebrates before and after Maintenance of a Constructed Wetland
Bibliographic record
Abstract
Stormwater best management practices (BMPs) require regular maintenance. The impact on trace metal concentrations in a constructed stormwater wetland BMP on Staten Island, New York, was investigated by analyzing sediment concentrations and tissue residues of the dominant macroinvertebrates (Tubifex tubifex) prior and subsequent to maintenance. Trace metal concentrations were assessed using standard serial extraction (for sediment) and acid digestion (for tissue burdens) techniques, followed by quantitative determination using graphite furnace atomic absorption spectrometry and inductively coupled plasma optical emission spectrometry, respectively. The results suggest that disturbance of sediment during maintenance of the BMP resulted in an increase in the most mobile fraction of trace metals, especially those associated with finer grained sediments (< 63 tm), and as a consequence, measured metal concentrations in macroinvertebrates increased. Regressions of a subset of metal concentrations (copper, lead, and zinc) in sediment and the macroinvertebrate tissue burden samples generally increased as a result of maintenance. A follow-up sampling event 9 months after maintenance demonstrated that the most readily available form of trace metal in the BMP was reduced, which supports (1) long-term sequestration of metals in the BMP and (2) that elevated bioavailability following maintenance was potentially a transient feature of the disturbance. This study suggests that in the long-term, performing sediment removal might help reduce bioavailability of trace metal concentrations in both the BMP and the receiving water to which a BMP discharges. However, alternative practices might need to be implemented to reduce trace metal bioavailability in the short-term.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".