A systematic study of interaction effects between plants, microbes, and metals in a model constructed wetland system treating mining influenced waters
Bibliographic record
Abstract
Constructed wetlands (CW)s have been utilized for decades to treat acid mine drainage (AMD), either directly or as a final polishing step in a series of treatment processes. However, the role of wetland plants and how they influence the treatment potential is still poorly understood, especially when testing is done in an open field-based setting. The main goal of my thesis research was to investigate how plants influence the underlying geochemical conditions and microbial communities in wetland soils and how they in turn affect metals removal and storage potential when wetlands are used to treat waters impacted by mine drainage. In order to achieve this goal, I designed a novel constructed floating wetland (CFW) system that was capable of replicating processes that occur in a subsurface flow wetland and survive repeated freeze-thaw cycles (Chapter 2). The successful CFWs design contained a 20 cm deep, organic rich sediment profile, and was planted with Carex lacustris, Typha latifolia and Juncus canadensis. Five of my designed CFWs were deployed in two waterbodies impacted by mine drainage and located either near an active, or closed, Ni and Cu metal smelter in Sudbury Ontario.I found that both C. lacustris and T. latifolia promoted reductive processes in the CFW soil profile and had significantly higher porewater sulfide than the unplanted control for the entire duration of our experiment (Chapter 2). Additionally, the sediments of these two plants had a higher relative abundance of microbes involved in sulfur cycling and higher relative abundances of enzymes involved in the assimilatory and dissimilatory sulfate reduction pathways (Chapter 3). On the other hand, J. canadensis promoted oxidative processes and had 2-3 times higher porewater sulfate concentration compared to the open water. Additionally, sediments of J. canadensis did not show any selectiveness towards sulfur reducing microbes, or the enzymes involved in the sulfate reduction pathway. Lastly, compared to the unplanted control, both C. lacustris and T. latifolia planted sediments had higher metal concentrations of Co, Cu and Ni, while J. canadensis did not. All the plants influenced metal partitioning to a certain degree and there was a strong site-specific influence on CFWs ability to remove metals (Chapter 4). My research provides a comprehensive look at how plants can either up-or-down regulate treatment potential of constructed wetlands and thus improves our understanding of how such systems can be used to better manage mining impacted waters.
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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) | 0.000 | 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 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".