MétaCan
Menu
Back to cohort
Record W4408473224 · doi:10.5194/egusphere-egu25-31

Developing a method for measuring mercury photoreduction in snow with a LED Solar Simulator 

2025· preprint· en· W4408473224 on OpenAlexaffabout
Giuditta Celli, Andrea Spolaor, Warren R. L. Cairns, Debbie Armstrong, Zhiyuan Gao, Fei Wang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSnowMercury (programming language)Solar simulatorSimulationEnvironmental scienceComputer scienceMeteorologyElectrical engineeringEngineeringPhysicsPhotovoltaic system

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.027
GPT teacher head0.282
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same topicSmart Materials for ConstructionFrench-language works237,207