CHEMICAL AND MINERALOGICAL CHARACTERIZATION OF AIRBORNE PARTICULATE MATTER ORIGINATING FROM MINING AND INDUSTRIAL OPERATIONS
Notice bibliographique
Résumé
Activities performed at mining and other industrial operations are capable of producing significant quantities of particulate matter that, if not properly contained, can be harmful to workers, and can enter surrounding communities causing damage to local environments. Analysis of particulate matter through routine monitoring is not always sufficient for characterizing substances of concern and differentiating between sources that contribute to emissions. To better understand the types of particulate matter generated at Ni and Cu mining and processing operations in Canada and Europe, a combination of quantitative mineralogy and methods of geochemistry (bulk chemistry, sequential leaching, trace-element geochemistry, and Pb isotope systematics) were performed. Particulate matter samples with varying size ranges (PM10, PM2.5, respirable, and inhalable) were collected within the workplace and at ambient air monitoring stations adjacent to the operations. The composition of the overall dust and the types of metal compounds present in the dust were determined with the objective of assessing worker exposure and investigating the extent to which emissions reach the adjacent communities. Baseline analysis of particulate matter generated during key activities at the operations, and analysis of source materials and settled dust provided additional information that was used for calculating source apportionment and investigating strategies for mitigating emissions. \nThe key findings of the study are that, due to the complexity of sources, workplace exposure at any operation investigation beyond government-mandated guidelines to identify relationships between documented health effects in workers and specific exposures. Routine methods of characterizing hazardous metals in dust, such as sequential leaching, should be calibrated for individual operations that deal with a unique set of substances, and should include more than one type of analysis. When activities are performed at operations, only minimal wind speeds are necessary to transport dust distances at least 2 km from an operation into the surrounding environment and community, and a complex variety of particulate matter is present in urban areas surrounding industrial operations and can have multiple sources that are not related to the operation itself. The apportionment data allowed personnel at the operations to determine the source of emissions relative to activity, and mitigation strategies were implanted to reduce emissions.
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 | 0,000 | 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) | 0,001 | 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 ».