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Enregistrement W4393757762 · doi:10.5281/zenodo.3610560

Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models

2020· dataset· en· W4393757762 sur OpenAlexaffabout
Elisa I. Boutzis, Junhua Zhang, Michael D. Moran

Notice bibliographique

RevueFigshare · 2020
Typedataset
Langueen
DomaineEngineering
ThématiqueVehicle emissions and performance
Établissements canadiensEnvironment and Climate Change Canada
Organismes subventionnairesnon disponible
Mots-clésParticulatesRepresentation (politics)Environmental scienceParticle-size distributionAtmospheric sciencesDistribution (mathematics)Particle sizeChemistryPhysicsMathematics

Résumé

récupéré en direct d'OpenAlex

Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model. A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system. This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (http://markummitchell.github.io/engauge-digitizer/) and WebPlot Digitizer (https://directory.fsf.org/wiki/WebPlotDigitizer). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices. These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication: Elisa I. Boutzis, Junhua Zhang &amp; Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air &amp; Waste Management Association</em>, 70:11, 1067-1100, DOI: 10.1080/10962247.2020.1743794

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,356
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0100,000

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 Opus0,015
Tête enseignante GPT0,234
Écart entre enseignants0,219 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

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
Publié2020
Routes d'admission2
Résumé présentoui

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