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Enregistrement W7052809443

Source apportionment of Arctic and remote marine carbonaceous aerosols

2021· other· en· W7052809443 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueeScholarship (California Digital Library) · 2021
Typeother
Langueen
DomaineEngineering
ThématiqueNuclear reactor physics and engineering
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of California, IrvineEnvironment and Climate Change Canada
Mots-clésSulfate aerosolTable (database)ArcticCarbon oxide
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Carbonaceous aerosols are critical, short-lived climate forcers (SLCFs) that play complex roles in the climate system through their interaction with solar radiation, cloud nucleation, and are also a major contributor to air pollution. Globally and within the Arctic, changing aerosol burden associated with the decline of sea ice, shifts in the productivity of marine and terrestrial ecosys-tems, wildfire, and anthropogenic activities, remains an important uncertainty for projections of future climate change. To develop and evaluate effective air quality and climate change mitiga-tion policy, we urgently need a better understanding of emissions sources. An important step forward in unraveling the complexity of carbonaceous aerosols lies in the analysis of specific aerosol fractions that have different emissions sources, lifetimes, and cli-mate- and health impacts. A minor component with significant climate and health implications is black carbon (BC), a light absorbing SLFC emitted directly through incomplete combustion that leads to increased air column temperatures, accelerated ice and snow melt, shifts in cloud for-mation, cover, and lifetime, and have adverse effects on human health. The vast majority are or-ganic carbon (OC) aerosols, that are light-scattering, also emitted through combustion processes, and formed secondarily in the atmosphere. In this thesis, I combine OC/BC analysis with stable (12C, 13C) and radioactive (14C) carbon isotope data to improve our understanding of BC and OC sources (fossil vs. modern and terrestrial vs. marine) and their spatiotemporal variations within the High Arctic, which are considered primarily marine. I also explore aerosol composition in cur-rently understudied marine source regions. \nDespite significant history of Arctic aerosol monitoring networks and power of isotopic (and specifically 14C data) for source attribution, consistent 14C observations of Arctic aerosol remain sparse. This is largely driven by the small sample sizes of aerosol collected in remote envi-ronments. To make such data more readily accessible for current and future monitoring networks, I evaluate the efficacy of the ECT9 protocol, a temperature protocol designed to physically sepa-rate and trap OC and BC microsamples (<100 µg C) for accurate δ13C and 14C analysis. This is done by measuring the 14C content of individual and mixed OC and BC standards of varying sizes to quantify the extraneous carbon incorporated throughout the analytical process and the efficacy of OC/BC physical separation. The total modern and fossil extraneous carbon incorpo-rated by the set-up was 0.9±0.45 and 0.4±0.2 µg C respectively. The ECT9 technique was found effective at physically separating exclusively non-refractory OC and highly refractory BC and can be applied directly to monitoring networks using this protocol to quantify OC/BC concentra-tions.\nI utilize the ECT9 protocol to quantify BC concentrations and fossil fuel contribution to BC in total suspended particulates (TSP) and snow collected at the Dr. Neil Trivett Global At-mosphere Watch Observatory at Alert, Nunavut Canada, a long-term monitoring facility, over the course of one year (2014-2015). I determine the seasonal cycle of fossil fuel source contributions to show that BC is primarily dominated by fossil sources throughout fall- spring (47-70% fossil) and have major geographical sources from the Russian Arctic sector, though long-range contribu-tions from Asia cannot be excluded. Additionally, summer BC (20-52% fossil) is dominated by biomass burning in the North American Arctic sector as shown by GFED v4.1 Summer 2014 bi-omass burning emissions and enriched 14C values. BC in snow was enriched relatively to BC in TSP, though this effect was not homogeneous (53-88% biomass). High biomass burning contribu-tions in snow BC suggests wet deposition may be a key pathway for long-range transport of bi-omass burning emissions from the upper troposphere. \nFurthermore, I explore the various marine and terrestrial sources to OC aerosol across the northern Pacific and the Arctic Ocean. I combine dual isotopes (13C, and 14C) in a multi-source model to calculate and quantify the contributions from surface marine refractory dissolved organ-ic carbon (RDOC), fresh biomass, and liquid fossil to ambient aerosol. The data shows that re-mote marine aerosol is dominated by RDOC in the Pacific (90% RDOC) and to a lesser extent in the Bering Sea (47% RDOC). This work suggests marine RDOC and fresh biomass are important contributors to marine OC and may play an important role in future Arctic climate change.\nTogether, my dissertation research established new analytical capabilities, produced criti-cal benchmark dataset, and advanced our understanding of carbonaceous aerosol in the rapidly changing Arctic.

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)0,001
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 enseignants0,008
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

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

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.

Tête enseignante Opus0,006
Tête enseignante GPT0,168
Écart entre enseignants0,162 · 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

Citations0
Publié2021
Routes d'admission2
Résumé présentoui

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