SUBMERSIBLE OPTICAL SENSORS EXPOSED TO CHEMICALLY-DISPERSED CRUDE OIL: WAVE TANK SIMULATIONS FOR IMPROVED OIL SPILL MONITORING
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Notice bibliographique
Résumé
In situ fluorometers were deployed during the Deepwater Horizon (DWH) Gulf of Mexico oil spill to provide critical measurements for tracking the subsea oil plume. In the wake of the spill, uncertainties regarding instrument specifications, capabilities and reliability necessitated performance testing of sensors (commonly used during spill response) exposed to simulated, dispersed oil plumes. Moreover, concerns on the applicability of laboratory calibrations (at high concentrations and insufficient mixing energies) to field conditions and on sensor reliability to detect dispersed oil persist. To address these uncertainties the performance of select commercially-available sensors (from Chelsea Technologies Group, Satlantic, Turner Designs, WetLabs Inc) was evaluated using a wave tank facility at the Bedford Institute of Oceanography in Halifax, Nova Scotia. Breaking waves were generated within the tank to simulate mixing energies and achieve dispersant effectiveness observed in the field. Presented here are the results of the sensors exposed to chemically-dispersed MC252 crude oil using Corexit 9500, DOR=1:20. Stepwise additions of dispersed oil (0.3 – 12 ppm) to the tank were used to establish linearity. Model 1 linear least squares regressions were calculated and applied to sensor data during validation experiments to simulate dilution of an oil plume. Dynamic ranges of the sensors, exposed to fresh and artificially weathered crude oil, were determined. Sensors were standardized against known oil volumes and measured Total Petroleum Hydrocarbons (TPH) and Benzene-Toluene-Ethylbenzene-Xylene (BTEX) values – both collected during spills, providing oil estimates during dilution experiments. Results were validated against particle size data (Sequoia LISST). All sensors estimated oil concentrations down to 300 ppb oil, refuting previous reports. Low percent differences and absolute errors between chemistry and sensor results were metrics to evaluate performance. Discussed will be the application of this vicarious calibration approach as a means to calibrate the DWH fine-scale fluorescence data into oil concentrations. This allows for filling in coarse-scale field chemistry data, improved assessment of DWH spill measurements mined from the NOAA NODC, and understanding the fate and transport of the DWH oil plume.
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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 | 0,001 |
| 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 découle