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Enregistrement W2942507904 · doi:10.25358/openscience-3850

Investigation of atmospheric transport and chemistry of semivolatile organic pollutants using earth system models

2018· article· en· W2942507904 sur OpenAlexaboutno aff
Mega Octaviani

Notice bibliographique

RevueGutenberg Open Science · 2018
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental Impact and Sustainability
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPollutantEarth (classical element)Atmospheric chemistryEnvironmental chemistryEnvironmental scienceChemistryAstrobiologyEarth system scienceAtmospheric sciencesGeologyOzoneOceanography

Résumé

récupéré en direct d'OpenAlex

The global atmospheric cycling of persistent organic pollutants is complex because of partitioning among phases of the aerosol and revolatilization. Many of the substances are detrimental to human health and the environment. Global dynamical multicompartmental chemistry and transport models are needed to investigate their fate and distributions. The first study investigates climate change influences on the meridional transports of dichlorodiphenyltrichloroethane (DDT) and polychlorinated biphenyls (PCBs) to and from the Arctic by application of the MPI-MCTM model. The objectives are to determine major transport gates along the Arctic Circle, the trends in import and export fluxes, and the relationships between transports and two selected patterns of climate variability, the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO), under present-day (1970−1999) and future (2070−2099) climate. The pollutants enter the Arctic by passing through the Alaska−Northwest Territories regions, Greenland, the Norwegian Sea−Northwestern Russia, and the Urals−Siberian; whereas they leave the Arctic via the Canadian Arctic and Eastern Russia. DDT import fluxes to the Arctic show a decreasing trend during the present climate, but the trend is expected to change to increasing import fluxes. In contrast, PCB153 export from the Arctic is expected to be increasing in the future climate. The zonal mean meridional fluxes across the Arctic Circle are positively correlated with AO/NAO in winter, corresponding to high net imports when the frequency of positive AO/NAO increases. Under the future climate, there will be an increasing significance of the correlations for DDT while the correlations for PCB153 are expected to weaken. It is concluded that the long-term accumulation trends of other persistent pollutants in the Arctic need to be studied specifically. In the second study, the new module SVOC was developed and coupled to the ECHAM/MESSy Atmospheric Chemistry (EMAC) model to facilitate a continuous development of modeling semivolatile organic compounds through a modular framework. Parameterizations of air−surface mass exchange in the EMAC-SVOC model are similar to those in MPI-MCTM. Other physics parameterizations were improved in the following ways: The gas−particle partitioning is described using poly-parameter linear free energy relationships; and aerosol particle size is discretized into a series of log-normally distributed modes. Through a sensitivity analysis with factor separation technique, the study examined the effects of four factors. These include the aforementioned parameterizations, as well as volatilization and temporal resolution of emissions. The focus here is set on four polycyclic aromatic hydrocarbons (PAHs), i.e., phenanthrene (PHE), pyrene (PYR), fluoranthene (FLT), and benzo(a)pyrene (BaP). The results indicate that seasonal emissions show dominant effects on PHE concentrations, notwithstanding the non-negligible effects from revolatilization. Other species are more sensitive to the change of internal model features. For all PAHs, the degree of model response is more determined by the interactions among factors with their contributions overall being stronger than individual factor contributions. Predicted near-surface concentrations using optimum model configuration were compared against observations. The model underestimates PHE concentrations in the Arctic and tropics but overestimates in the mid-latitudes. FLT and PYR tend to be overestimated over the Arctic and mid-latitudes, and underestimated over the tropics. There is a consistent underestimation of BaP in all regions, with bias increasing from mid-latitudes to the Arctic. The systematic underestimation of BaP concentrations is related to a too fast particulate-phase oxidation by ozone. This issue is addressed in the third study through a better description of BaP multiphase degradation. A new kinetic scheme was developed by considering the dependence of BaP reaction rate on two environmental parameters, that is, temperature (T) and relative humidity (RH). These parameters influence not only the phase state and diffusivity of organic aerosol coating but also the chemical reactivity of BaP. The significance of the new scheme (ROI-T) for distributions and fate was quantitatively assessed by regional (WRF-Chem-BaP) and global scale (EMAC-SVOC) modeling. In comparison to laboratory-based degradation schemes, the ROI-T scheme consistently shows better predictions and improved bias against observations at near-source sites, mid-latitude sites and most substantially at Arctic sites. The new scheme reasonably simulates the effect of low T and RH conditions to increase BaP atmospheric lifetime, leading to a more efficient transport at high altitudes or in a cold season/regions. The scheme can be adopted for modeling the multiphase degradation of other semivolatile organics.

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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,077
Score d'incertitude au seuil0,152

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,019
Tête enseignante GPT0,243
Écart entre enseignants0,224 · 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'étudeSimulation ou modélisation
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
Publié2018
Routes d'admission1
Résumé présentoui

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