Remote sensing and in-situ characterisation of atmospheric aerosol pollution
Notice bibliographique
Résumé
This work focuses on characterising various aspects of clean-background and polluted aerosol, mainly focusing on the North East Atlantic and Europe, using a range of in-situ and remote sensing instrumentation. Prior to achieving the objectives of aerosol characterisation, effort was invested in characterising a new, near-real-time satellite aerosol profiling products. The near-real-time Level 1.5 Cloud-Aerosol Light Detection and Ranging (lidar) with Orthogonal Polarization (CALIOP) products were evaluated against data from ground-based European Aerosol Research Lidar Network (EARLINET). A statistical study was performed on 48 CALIOP overpasses with ground tracks within a 100 km distance from operating stations over three years period. For the whole data set, the correlation coefficient (R) was 0.86. The correlation was reduced somewhat to R=0.6 when the analysis was repeated for the planetary boundary layer (PBL) on its own. Further filtering to remove free troposphere (FT) layers with high attenuated backscatter did not improve the agreement either and suggests that the considerable variability across the data set, leading to the low correlation, is due to spatial inhomogeneities in the PBL. Additional evaluation between CALIOP and ground-based lidar close to Atlanta (United States (US)) also showed poor agreement. This poor agreement led us to consider the whole feasibility of a Calibration & Validation (CALVAL) exercise using a ground-based reference stations for space-borne CALVAL platforms. The required accuracy necessitates uncertainties of the order of 2% to be meaningful. To achieve this level of uncertainty with a polar-orbiting satellite would require averaging the ground-based lidar profiles along the ground track of CALIOP for distances of at least 1,500 km, which, clearly, cannot be achieved with ground-based lidars. Satellite remote sensing is useful for characterising sources and extent of long range aerosol transport and, in particular, quantifying its contribution to transboundary air pollution. Using a threshold Aerosol Optical Depth (AOD) value of 0.5 for extremely polluted conditions, we analysed the frequency of occurrence of such events at the Mace Head Global Atmosphere Watch station and found that over a period of 6 years, a total of 17 extremely-polluted cases were identified. Such events were associated with continental pollution outflow from Europe, Sahara Dust outflow, or a combination of both. In addition, volcanic ash eruptions and forest fires in Canada also contributed. Analysis of a 35 year record of sulphur air pollution revealed a 75% reduction in pollution associated with reductions in emissions following intervention and establishment of the European Monitoring and Evaluation Programme under the United Nations Economic Commission for Europe (UNECE) Convention. Over the last 6 years, the pollution levels over the Mace Head region of the North East Atlantic have levelled off, providing us with the opportunity to conduct a detailed statistical analysis on the cleanliness of North Atlantic air and the frequency of occurrence on clean and polluted air at Mace Head. The unique dataset comprising 6-years online measurements of black carbon, aerosol size distributions and aerosol chemistry, including organic aerosol, revealed that air masses arriving at Mace Head were clean to pristine for 65% of the time, moderately polluted to polluted for 35% of the time, and extremely polluted for 1.5% of the time in a 6-year period. In clean-to-pristine air, sulphate mass dominates over organics, sea-salt and nitrates, contributing 42% to the total, while carbonaceous aerosol (of which more than 90% comprises organics and less than 10% black carbon) mass dominates with a 60% contribution in polluted air, increasing to 90% in the most polluted. Worryingly, organic aerosol mass, which is the single largest contributor to particulate mass pollution, is not measured in regulatory networks.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 | 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 ».