Modeling of time-resolved light extinction and its applications to visibility management in the Lower Fraser Valley of British Columbia, Canada
Notice bibliographique
Résumé
UNLABELLED: Fine particulate matter (PM2.5) is the dominant cause of atmospheric visibility degradation in the Lower Fraser Valley (LFV) of British Columbia, where poor visibility due to air pollution is of concern. The spatial coverage of the current LFV visibility monitoring network is relatively low, with large parts of the airshed not being represented. Given the desire on the part of local and regional governments to manage visibility in the LFV airshed, the development of a method that allows near real-time estimation of 1-hr light extinction data from the dense network of PM measurements would be highly beneficial. This paper describes a simple linear algorithm, developed using the Hybrid method, to estimate near real-time 1-hr total light extinction at four monitoring sites in the LFV. Model inputs include ambient hourly PM2.5, NO2, relative humidity measurements, and historical monthly-averaged aerosol composition. The results indicate that the developed model can provide relatively accurate and time-resolved estimates of extinction in regions where visibility is not being monitored, thus extending the spatial coverage of the regional visibility monitoring network. The model was also applied to a number of policy-related scenarios to inform visual air quality management in the study area. Results indicated that in order to achieve a perceptible improvement (1.0 deciview) relative to baseline average visibility conditions in the LFV airshed, average ambient PM2.5 concentration would have to decrease by 17% from baseline conditions. Furthermore, to achieve a 20% increase in the number of daylight hours with "excellent" visibility, average PM2.5 would need to be reduced by 30%. Model simulations also indicated that "across-the-board" emission reduction policies would result in greater improvements for the "worst 20%" visibility conditions than for the "best 20%" conditions, suggesting that reducing the number of "poor" visibility days would be easier than improving the number of "excellent" visibility days. IMPLICATIONS: This study describes the development of a model using standard air quality monitoring data (PM2.5, NO2, relative humidity, and PM speciation profiles) to provide near real-time estimates of time-resolved extinction in regions where direct optical monitoring is not available. Applications of the model include extension of spatial coverage of a visibility network, testing various air quality scenarios to inform visibility management, and as a tool for setting visual air quality standards in impacted airsheds.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».