Oil And Gas Development And the Potential For Contamination of Moose (Alces Alces) In Northeast BC
Notice bibliographique
Résumé
Abstract We analyzed moose tissue samples for extractable petroleum hydrocarbons (EPH), heavy metals and polycyclic hydrocarbons (PAH) and compared levels of concentration in tissues between two areas with noticeably different levels of oil and gas activity. Our treatment area (extensive oil and gas activity) had 135 oil and gas wells in a 2,100 km2 area in 2003 and the control area (2,900 km2) had no active oil and gas wells in 2003. EPH analysis showed the treatment area had significantly higher levels of C32, C33, C37 and C38 than the control area. Significantly higher levels of heavy metal concentrations in 14 of 28 heavy metals tested were found in tissues sampled from the treatment area. No significant difference in PAH were found between the study areas. This study suggests that oil and gas activity may have negative impacts on the health of moose in Northeast BC. In order to quantify this impact, further work needs to be undertaken that will monitor moose activities as they relate to landscape feature availability to determine the use and impact of oil and gas facilities on the health of moose in Northeast BC. Introduction First Nations of Northern Canada today remain reliant on their traditional sources of food for meat, berries and fish. Much of the meat protein that is consumed by First Nations is obtained by hunting and trapping in traditional areas. First Nations people of the Northwest Territories have relied on moose (Alces alces) as a traditional food source for thousands of years(1), as many First Nations people have, including the communities of Northeast British Columbia. Moose meat is the most sought after meat by the West Moberly and Saulteau First Nations of Northeast British Columbia. As the level of oil and gas exploration and development activity in northeastern British Columbia has increased, concerns regarding the consumption of contaminated meat have increased. Oil and gas activity releases contaminants in the form of heavy metals, polycyclic aromatic hydrocarbons (PAH), extractable petroleum hydrocarbons (EPH) and volatile by-products of oil and gas to the water, soil and air during production, processing, storage and distribution(2). These contaminants may become bio-magnified or may bio-accumulate in the environment through predation and consumption of plants and animals. Contamination of wild game from industrial activity has been documented in the past(3–5). Indian and Northern Affairs Canada issued a fact sheet in 2003(1) that suggested contaminants in moose (cadmium) resulted from naturally occurring cadmium up-take during plant growth, and that these plants, when consumed by moose, will transfer this contaminant to tissues and organs. Cadmium is only one such heavy metal that may contaminate large mammals such as moose. Heavy metals occur naturally, and can enter aquatic ecosystems by anthropogenic activities such as mining, burning, deforestation, agriculture and urban activity(4). Consumption by wildlife of heavy metals that enter surface and subsurface aquatic ecosystems occurs when the metals are transferred to animals either directly by consumption of water, or indirectly through the uptake by plants and the eventual consumption of contaminated plants by wildlife.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».