Effect of Dissolved Organic Carbon on the Photoproduction of Dissolved Gaseous Mercury in Lakes: Potential Impacts of Forestry
Bibliographic record
Abstract
The production of dissolved gaseous mercury (DGM) in freshwater lakes is induced by solar radiation and is also thought to be linked to processes mediated by dissolved organic carbon (DOC). Studies investigating these processes using comparisons between lakes are often confounded by differences in DOC content and structure. In this study, we investigated the link between DOC concentrations and DGM production by using tangential ultrafiltration to manipulate DOC concentrations in water samples taken from a given lake. In this way, a range of samples with different DOC concentrations was produced without substantial changes to DOC structure or dissolved ions. This was repeated for four lakes in central Quebec: two with highly logged drainage basins and two with minimally logged drainage basins. On two separate days for each lake, water samples (filtered to remove >99% of microorganisms) with varying DOC concentrations were incubated in clear and dark Teflon bottles on the lake surface. DGM concentrations were measured at 3.5-h intervals over the course of 10.5 h. Levels of DGM concentrations increased with increasing cumulative irradiation for all lakes until approximately 4000 kJ m(-2) (400-750 nm, photosynthetically active radiation (PAR)), when DGM concentrations reached a plateau (between 20 and 200 pg L(-1)). When we assumed that DGM production was limited by the amount of photoreducible mercury, reversible first-order reaction kinetics fitted the observed data well (r2 ranging between 0.59 and 0.98, p < 0.05 with the exception of N70 100% DOC, 0% DOC, and K2 0% DOC with p = 0.06, 0.10, and 0.11, respectively). The DGM plateaus were independent of DOC concentrations but differed between lakes. In contrast, photoproduction efficiency (DGMprod) (i.e., the amount of DGM produced per unit radiation (fg L(-1) (kJ/m2)(-1)) below 4000 kJ m(-2) PAR) was linearly proportional to DOC concentration for both logged lakes (r2 = 0.97, p < 0.01) and nonlogged lakes (r2 = 0.52, p = 0.018) studied. Furthermore, logged lakes had a lower DGMprod per unit DOC (p < 0.01) than the nonlogged lakes. In these four lakes, the rate of DGM production per unit PAR was dependent on the concentration of DOC. The DGM plateau was independent of DOC concentration; however, there was a significant difference in DGM plateaus between lakes presumably due to different DOC structures and dissolved ions. This research demonstrates an important mechanism by which logging may exacerbate mercury levels in biota.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".