Peak event analysis: a novel empirical method for the evaluation of elevated particulate events
Notice bibliographique
Résumé
BACKGROUND: We report on a novel approach to the analysis of suspended particulate data in a rural setting in southern Ontario. Analyses of suspended particulate matter and associated air quality standards have conventionally focussed on 24-hour mean levels of total suspended particulates (TSP) and particulate matter <10 microns, <2.5 microns and <1 micron in diameter (PM10, PM2.5, PM1, respectively). Less emphasis has been placed on brief peaks in suspended particulate levels, which may pose a substantial nuisance, irritant, or health hazard. These events may also represent a common cause of public complaint and concern regarding air quality. METHODS: Measurements of TSP, PM10, PM2.5, and PM1 levels were taken using an automated device following local complaints of dusty conditions in rural south-central Ontario, Canada. The data consisted of 126,051 by-minute TSP, PM10, PM2.5, and PM1 measurements between May and August 2012. Two analyses were performed and compared. First, conventional descriptive statistics were computed by month for TSP, PM10, PM2.5, and PM1, including mean values and percentiles (70th, 90th, and 95th). Second, a novel graphical analysis method, using density curves and line plots, was conducted to examine peak events occurring at or above the 99th percentile of per-minute TSP readings. We refer to this method as "peak event analysis". Findings of the novel method were compared with findings from the conventional approach. RESULTS: Conventional analyses revealed that mean levels of all categories of suspended particulates and suspended particulate diameter ratios conformed to existing air quality standards. Our novel methodology revealed extreme outlier events above the 99th percentile of readings, with peak PM10 and TSP levels over 20 and 100 times higher than the respective mean values. Peak event analysis revealed and described rare and extreme peak dust events that would not have been detected using conventional descriptive statistics. CONCLUSIONS: Peak event analysis underscored extreme particulate events that may contribute to local complaints regarding intermittently dusty conditions. These outlier events may not appear through conventional analytical approaches. In comparison with conventional descriptive approaches, peak event analysis provided a more analytical and data-driven means to identify suspended particulate events with meaningful and perceptible effects on local residents.
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 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 | 0,004 | 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) | 0,003 | 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 ».