Developing a method for measuring mercury photoreduction in snow with a LED Solar Simulator
Bibliographic record
Abstract
The investigation of mercury photoreduction in snow can be challenging due to the complex nature of the snowpack and of the surrounding environment from both physical proprieties and chemical composition1. The mercury air-snow interaction and exchange driven by the photoreduction in snow plays a central role in its geochemical cycling. Experiments in natural world snowpack have clearly demonstrate the occurrence of mercury re-emission2. However, the influences from different physical proprieties and chemical composition is virtually impossible to be disentangle in studies carried out in the natural environment. Laboratory studies in a closed photochemical reactor provide valuable proof of concept to better understand the photoreduction process and its reaction kinetics in a controlled environment where chemical and physical parameters can be (semi)controlled and modified1–3. Here we propose an experimental scheme to simulate the photochemical emission of gaseous mercury from urban snow at cold temperatures, using a custom-made LED solar simulator. Multiple factors such as the irradiating wavelengths were examined and their role in affecting the determined mercury photoreduction process was studied. Specific attention was made in the cleanliness of the experimental set-up and on the air flow above the snow to optimise the condition in which experiment will be perform. The system development was tested at the University of Manitoba (Winnipeg, Canada) for multiple experiments. They showed the role of the UV radiation in the mercury photoreduction activation, with an increase in GEM concentration when the light was on and a decrease over time, supporting that the experiment set up is valid to evaluate the UV driven mercury photoreduction in the snow, and the collection and measurement of the produced GEM. The estimation of a preliminary reduction rate constant (kr) was also possible, finding a rate constant ranging from 0.712 h-1 to 0.757 h-1. The system represents the technical base to further mercury laboratory snow photochemical experiment in different conditions including the possibility to modify the chemical composition of the snow, the inlet air composition (for ex. by implementing an ozone producer system) and physical parameters (temperature, solar radiation, relative humidity).Dommergue, A., Bahlmann, E., Ebinghaus, R., Ferrari, C. & Boutron, C. Laboratory simulation of Hg0 emissions from a snowpack. Anal Bioanal Chem 388, 319–327 (2007). Lalonde, J. D., Poulain, A. J. & Amyot, M. The Role of Mercury Redox Reactions in Snow on Snow-to-Air Mercury Transfer. Environ. Sci. Technol. 36, 174–178 (2002). Mann, E. A., Mallory, M. L., Ziegler, S. E., Tordon, R. & O’Driscoll, N. J. Mercury in Arctic snow: Quantifying the kinetics of photochemical oxidation and reduction. Science of The Total Environment 509–510, 115–132 (2015).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".