Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?
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Notice bibliographique
Résumé
The Critical Review of Hoff and Christopher, along with the discussants, provides an important perspective on the interface between satellite measurement science and air quality observations. A top-down picture of the usefulness of satellite observations in terms of air quality regulatory and technical support requirements can be summarized. The air quality requirements are (1) determination of compliance with the ambient air quality standards, (2) inference of human and ecosystem exposure, (3) identification of intra- and intercontinental events relevant to EE, (4) establishment of trends in ambient concentrations relevant to accountability, (5) regulatory and forecast model applications, and (6) extension of fundamental knowledge relevant to air quality. Each of these topics is important to air quality management, and each has detailed technical issues associated with spatial and temporal resolution, accuracy, and precision, etc. In any case, one can summarize the broad capabilities of measurement systems to address these requirements as listed in Table 1. From this rather superficial summary table, investigators should be encouraged to forward increased interaction between the various measurement communities and to facilitate the utility of a comprehensive portfolio of measurements and adjunct analyses for improved air quality applications. The Critical Review has done much to educate air quality scientists on the possibilities for using satellite remote sensing for various purposes. However, space scientists also need a better education on air quality science. Recently published reviews on PM air quality measurements are available that complement the Hoff-Christopher paper on this topic. The need for greater collaboration of air quality and space scientists is evident in an article published in the July issue of the journal. Al-Hamdan et al. provide an interesting and useful analysis of relationships between surface air quality and space-based satellite AOD to estimate human exposure. They obtain mostly urban PM data from EPA's Air Quality System (AQS), but they neglect the potentially more useful PM2.5 and chemical speciation data from the nonurban Interagency Monitoring of Protected Visual Environments (IMPROVE) and the Southeastern Aerosol Research and Characterization (SEARCH) networks. They correlate PM2.5 mass with optical depth, although visibility assessments show that light extinction is better represented by a weighted sum of PM2.5 sulfate, nitrate, organic carbon, elemental carbon, and soil dust. Their comparison of hourly measurements with filter measurements does not specify the source of the hourly values as TEOM or BAM. Spatial outliers for ground-level measurements are removed to improve the correlation of PM2.5 with AOD, although these "outliers" are probably real values that relate to human exposure or a nearby source effect. The point here is not to overly criticize a good publication that will be highly cited. The intent is to demonstrate the value of air quality and space scientists working together more closely on this topic. This is something the review authors alluded to in their review, but if, as they concluded, the "promised land" has not been reached, then perhaps it is an appropriate time for the atmospheric community to ask, "Can near-term satellite observations play a role in characterizing broad-based (outdoor) exposure to pollutants and consequently influence public health improvement?" and, if so, then, "What comprehensive, integrated system is needed if satellite observations are to be used together with ground-based observations and modeling to continue improving air quality management options?"
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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 | 0,002 | 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) | 0,000 | 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 découle