How relevant is the deposition of mercury onto snowpacks? – Part 1: A statistical study on the impact of environmental factors
Notice bibliographique
Résumé
Abstract. A portion of the highly toxic methylmercury that bioaccumulates in aquatic life is created from mercury entering bodies of water with snowpack meltwater. To determine the importance of meltwater as a source of aquatic mercury, it is necessary to understand the environmental processes that govern the behavior of snowpack-related mercury. In this study we investigate relationships among 5 types of snowpack-related mercury observations and 20 model environmental variables. The observation types are the 24-h fractional loss of mercury from surface snow, and the concentrations of mercury in surface snow, seasonal snowpacks, the snowpack meltwater's ionic pulse, and long-term snowpack-related records. The model environmental variables include those related to atmospheric mercury, insolation, wind, atmospheric stability, snowpack physical characteristics, atmospheric pressure, and solid precipitation. Bivariate and multiple linear regressions were performed twice for each mercury observation type: once with all observations, and once excluding observations from locations where the snowpack's burden of oxidizing and stabilizing halogens is known or presumed to affect snowpack mercury. Since no observations from long-term snowpack-related records were considered affected by halogens, this group of observations was included with the sets of uninfluenced observations and was not discussed with the complete, original sets of observations. When all observations are included, only 37% of their variability can be explained, on average, with significance confidence levels averaging 81%; a separate regression model predicts each mercury observation type. Without the influence of halogens, the regression models are able to explain an average of 79% of the observations' variability with significance confidence levels averaging 97%. The snowpack-related mercury observations are most strongly controlled by the dry and wet depositions of oxidized mercury, and by precipitation. Mercury deposited through wet processes is more strongly retained by snowpacks than mercury deposited through dry processes. Revolatilization of mercury deposited through wet processes may be inhibited through burial by fresh snowfalls and/or by its more central location, compared to that of mercury deposited through dry deposition, within snowpack snow grains. The two depositions of oxidized mercury together explain 84% of the variability in observed concentrations of mercury in surface snow, 52% of the variability of observed concentrations of mercury in seasonal snowpacks and their meltwater's ionic pulse, and only 20% of the variability of observed concentrations of mercury in long-term snowpack-related records; other environmental controls seemingly gain in relevance as time passes. The concentration of mercury in long-term records is apparently primarily affected by latitude; both the primary sources of anthropogenic mercury and the strong upper-level zonal winds are located in the midlatitudes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».