The View from Afar: Satellite-Derived Estimates of Global PM <sub>2.5</sub>
Notice bibliographique
Résumé
Satellite-Derived Estimates of Global PM 2.5More than 3 million people died prematurely in 2010 due to ambient exposure to fine particulate matter (PM 2.5 ), according to estimates from the Global Burden of Disease Study. 1 Although air pollution measurements taken from ground-level monitors can help inform such estimates, a paucity of monitoring stations outside of North America and Western Europe make it difficult to compare levels and trends in PM 2.5 and their health effects around the world. 2 Fortunately, satellite data provide a way of filling in data gaps for areas with no ground-based monitoring.In this issue of EHP, a team of researchers report their satellite-derived estimates of global exposure trends to PM 2.5 over 15 years.3 "We found notable trends of increasing PM 2.5 in South and East Asia, where billions of people live.Meanwhile, parts of North America are getting cleaner," says study author Randall Martin, an atmospheric scientist at Dalhousie University in Halifax, Nova Scotia.Satellite sensors don't measure PM 2.5 directly.Instead, they assess how particles in the air, including PM 2.5 , scatter sunlight as it passes through the atmosphere."In a sense, the satellites we use are little more than extremely well calibrated cameras that take pictures of the earth below.When aerosol particles are present, these pictures begin to look a little hazy," explains first author Aaron van Donkelaar, also an atmospheric scientist at Dalhousie University.The extent to which aerosols scatter the light is called the aerosol optical depth (AOD).The researchers used AOD data from the National Aeronautics and Space Administration to estimate ground-level PM 2.5 at a spatial resolution of approximately 10 km × 10 km.Although some regions experienced a decrease in PM 2.5 over the period 1998-2012, the global population-weighted average increased by an estimated 2.1% per year.Rising levels of air pollution in developing regions in South and East Asia largely drove the upward trend.3 After adjusting for population changes, the researchers esti mated that the proportion of people in South and East Asia exposed to PM 2.5 at levels exceeding the World Health Organization (WHO) interim target of 35 µg/m 3 rose from 51% in 1998-2000 to 70% in 2010-2012.In contrast, the proportion of North Americans exposed to PM 2.5 at levels above the WHO air quality guideline of 10 µg/ m 3 fell from 62% in 1998-2000 to 19% in 2010-2012.4 Where ground-level PM 2.5 data were available, the researchers found a significant association with satellite-based estimates, though satellitederived PM 2.5 estimates tended to be slightly lower than ground-level readings.3 "Satellite-based estimates reported here will enable researchers to design and conduct large epidemiological studies in low-and middle-income countries that lack the extensive ground monitoring networks found in higher income countries," says Aaron Cohen, an epidemiologist at the Health Effects Institute in Boston.He was not involved in the current study.As satellite-derived PM 2.5 estimates have become available, groups such as the Global Burden of Disease Study 1 and the WHO 5 have begun to use them as the basis for estimates of the global burden of disease.The current study builds upon a previous analysis by these authors, which estimated global PM 2.5 levels from 2001 through 2006
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 | 0,003 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,029 | 0,030 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,007 |
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 ».