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Probabilistic estimates of variability in exposure to traffic-related air pollution in the Greater Vancouver Regional District - a spatial perspective

2008· dissertation· en· W7005225577 sur OpenAlexaboutno aff

Notice bibliographique

RevueUVic’s Research and Learning Repository (University of Victoria) · 2008
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCell Image Analysis Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAir pollutionPollutionProbabilistic logicSpatial variabilityCensusWork (physics)Exposure assessment
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

A probabilistic spatial exposure simulation model (SESM) was designed to investigate the effect of time spent at work and commuting on estimates of chronic exposure to traffic-related air pollution in large populations. The model produces distributions of exposure estimates in six microenvironments (home indoor, work indoor, other indoor, outdoor, transit to work and transit other) for workers and non-workers, using randomly sampled time-activity patterns from the Canadian Human Activity Pattern Survey and work flow data from Statistics Canada. The SESM incorporates geographic detail through the use of property assessment data, shortest route analysis, and the use of a geographic information system (GIS) to develop pollution concentration distributions. The SESM was implemented and tested using data for 382 census tracts in the Greater Vancouver Regional District of British Columbia.
\nSimulation results were found to be relatively insensitive to the choice of distance used to represent the typical range of non-work related trips; the use of a simple annual average pollution estimate versus a time-stratified annual average; and the use of different indoor/outdoor ratios representing the infiltration of ambient pollution into indoor locations. Substantial sensitivity was observed based on the use of different methods for producing spatial estimates of ambient air pollution.
\nThe SESM was used to explore variability in annual total exposure of workers to traffic-related nitrogen dioxide (NO2). Total exposure ranged from 8 μg/m3 to 35 μg/m3 of
\niv
\nannual average hourly NO2 and was highest where ambient pollution levels are highest, reflecting the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. Within census tract variation was observed in the partial exposure estimates associated with time spent at work locations, particularly in suburban areas where longer commuting distances are more prevalent. In these areas, some workers may have exposures 1.3 times higher than other workers residing in the same census tract. Exposures to NO2 associated with the activity of commuting to work were negligible.
\nNo statistically significant difference in total exposure estimates was found between female and male commuters, although there were small but observable differences at the upper end of the exposure distributions associated specifically with the work indoor microenvironment. These differences were highest in suburban areas (up to 3 μg/m3 of annual hourly average NO2 higher for female commuters, in relation to 99th percentile total exposures levels of approximately 37 μg/m3), illustrating the impact of systematically different work locations for female compared to male commuters in these same census tracts.
\nSimulated exposures for workers, non-workers, and a base scenario where all time is spent at the residence only were compared. Statistically significant differences were found in the exposure distributions for workers versus non-workers, workers versus residence only, and non-workers versus residence only. Differences in exposure within census tracts were highest at the 10th and 90th percentiles, on the order of -5.4 to +6.5 μg/m3 of annual average hourly NO2 respectively for workers compared to non-workers, in relation to exposure estimates between 10 and 40 μg/m3 of annual average hourly NO2 on average.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,752
Score d'incertitude au seuil0,626

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
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 recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,011
Tête enseignante GPT0,262
Écart entre enseignants0,252 · 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.

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
Publié2008
Routes d'admission1
Résumé présentoui

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