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Enregistrement W2755388125 · doi:10.5194/acp-18-7263-2018

How important is biomass burning in Canada to mercury contamination?

2018· article· en· W2755388125 sur OpenAlexafffundabout
A. Fraser, Ashu Dastoor, Andrei Ryjkov

Notice bibliographique

RevueAtmospheric chemistry and physics · 2018
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAtmospheric and Environmental Gas Dynamics
Établissements canadiensEnvironment and Climate Change Canada
Organismes subventionnairesNational Eye InstituteClean Air Regulatory AgendaEnvironment and Climate Change CanadaNational Center for Atmospheric Research
Mots-clésMercury (programming language)Environmental scienceBiomass burningAir quality indexAtmospheric sciencesPeatElemental mercuryClimate changeEnvironmental chemistryAerosolMeteorologyChemistryEcologyGeologyOceanographyGeography

Résumé

récupéré en direct d'OpenAlex

Abstract. Wildfire frequency has increased in past four decades in Canada and is expected to increase in future as a result of climate change (Wotton et al., 2010). Mercury (Hg) emissions from biomass burning are known to be significant; however, the impact of biomass burning on air concentration and deposition fluxes in Canada has not been previously quantified. We use estimates of burned biomass from FINN (Fire INventory from NCAR) and vegetation-specific emission factors (EFs) of mercury to investigate the spatiotemporal variability of Hg emissions in Canada. We use Environment and Climate Change Canada's GEM-MACH-Hg (Global Environmental Multi-scale, Modelling Air quality and Chemistry model, mercury version) to quantify the impact of biomass burning in Canada on spatiotemporal variability of air concentrations and deposition fluxes of mercury in Canada. We use North American gaseous elemental mercury (GEM) observations (2010–2015), GEM-MACH-Hg, and an inversion technique to optimize the EFs for GEM for five vegetation types represented in North American fires to constrain the biomass burning impacts of mercury. The inversion results suggest that EFs representing more vegetation types – specifically peatland – are required. This is currently limited by the sparseness of measurements of Hg from biomass burning plumes. More measurements of Hg concentration in the air, specifically downwind of fires, would improve the inversions. We use three biomass burning Hg emissions scenarios in Canada to conduct three sets of model simulations for 2010–2015: two scenarios where Hg is emitted only as GEM using literature or optimized EFs and a third scenario where Hg is emitted as GEM using literature EFs and particle bound mercury (PBM) emitted using the average GEM∕PBM ratio from lab measurements. The three biomass burning emission scenarios represent a range of possible values for the impacts of Hg emissions from biomass burning in Canada on Hg concentration and deposition.We find total biomass burning Hg emissions to be highly variable from year to year and estimate average 2010–2015 total atmospheric biomass burning emissions of Hg in Canada to be between 6 and 14 t during the biomass burning season (i.e. from May to September), which is 3–7 times the mercury emission from anthropogenic sources in Canada for this period. On average, 65 % of the emissions occur in the provinces west of Ontario. We find that while emissions from biomass burning have a small impact on surface air concentrations of GEM averaged over individual provinces/territories, the impact at individual sites can be as high as 95 % during burning events. We estimate average annual mercury deposition from biomass burning in Canada to be between 0.3 and 2.8 t, compared to 0.14 t of mercury deposition from anthropogenic sources during the biomass burning season in Canada. Compared to the biomass burning emissions, the relative impact of fires on mercury deposition is shifted eastward, with on average 54 % percent of the deposition occurring in provinces west of Ontario. While the relative contribution of Canadian biomass burning to the total mercury deposition over each province/territory is no more than 9 % between 2010 and 2015, the local contribution in some locations (including areas downwind of biomass burning) can be as high as 80 % (e.g. northwest of Great Slave Lake in 2014) from May to September. We find that northern Alberta and Saskatchewan, central British Columbia, and the area around Great Slave Lake in the Northwest Territories are at greater risk of mercury contamination from biomass burning. GEM is considered to be the dominant mercury species emitted from biomass burning; however, there remains an uncertainty in the speciation of mercury released from biomass burning. We find that the impact of biomass burning emissions on mercury deposition is significantly affected by the uncertainty in speciation of emitted mercury because PBM is more readily deposited closer to the emission sources than GEM; an addition of ∼ 18 % percent of mercury emission from biomass burning in the form of PBM in the model increases the 6-year average deposition by ∼ 4 times.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,272

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,003
Tête enseignante GPT0,178
Écart entre enseignants0,175 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations55
Publié2018
Routes d'admission3
Résumé présentoui

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