The differences between remote sensing and in situ air pollutant measurements over the Canadian oil sands
Notice bibliographique
Résumé
Ground-based remote sensing instruments have been widely used for atmospheric research, but applications for air quality monitoring remain limited. Compared to an in situ instrument that provides air quality conditions at the ground level, most remote sensing instruments (nadir viewing) are sensitive to a broad range of altitudes, often providing only integrated column observations. These column data can be more difficult to interpret and to relate to surface values and hence to “nose-height-level” health factors. This research utilized ground-based remote sensing and in situ air quality observations in Canada's Athabasca oil sands region to investigate some of their differences. Vertical column densities (VCDs) of SO 2 and NO 2 retrieved by Pandora spectrometers located at the Oski-Otin site at Fort McKay (Alberta, Canada) from 2013–2019 were analyzed along with measurements of SO 2 and NO 2 surface concentrations and meteorological data. Aerosol optical depth (AOD) observations by a CIMEL sunphotometer were compared with surface PM 2.5 data. The Oski-Otin site is surrounded by several large bitumen mining operations within the Athabasca oil sands region with significant NO 2 emissions from the mining fleet. Two major bitumen upgraders that are 20 km south-east of the site have total SO 2 and NO 2 emissions of about 40 and 20 kt yr −1 , respectively. It was demonstrated that remote sensing data from Pandora and CIMEL combined with high-vertical-resolution wind profiles can provide information about pollution sources and plume characteristics. Elevated SO 2 VCDs were clearly observed for times with south and south-eastern winds, particularly at 200–300 m altitude (above ground level). High NO 2 VCD values were observed from other directions (e.g., north-west) with less prominent impacts from 200–300 m winds. In situ ground observations of SO 2 and NO 2 show a different sensitivity to wind profiles, indicating they are less sensitive to elevated plumes than remote sensing instruments. In addition to measured wind data and lidar-observed boundary layer height (BLH), modelled wind profiles and BLH from ECMWF Reanalysis v5 (ERA5) have been used to further examine the correlation between column and surface observations. The results show that the height of emission sources (e.g., emissions from high stacks or near the surface) will determine the ratio of measured column and surface concentration values (i.e., could show positive or negative correlation with BLH). This effect will have an impact on the comparison between column observations (e.g., from the satellite or ground-based remote sensing instruments) with surface in situ measurements. This study explores differences between remote sensing and in situ instruments in terms of their vertical, horizontal, and temporal sampling differences. Understanding and resolving these differences are critical for future analyses linking satellite, ground-based remote sensing and in situ observations in air quality monitoring and research.
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 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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 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 ».