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Enregistrement W4406113784 · doi:10.3389/ijph.2024.1608249

Editorial: Science to Foster the WHO Air Quality Guideline Values

2025· editorial· en· W4406113784 sur OpenAlexaboutno aff
Hanna Boogaard, Pallavi Pant, Nino Künzli

Notice bibliographique

RevueInternational Journal of Public Health · 2025
Typeeditorial
Langueen
DomaineEnvironmental Science
ThématiqueAir Quality and Health Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAir quality indexInterimEnvironmental healthAir pollutionPublic healthHealth effectMedicineGuidelineParticulatesEpidemiologyEnvironmental protectionEnvironmental scienceGeographyMeteorologyPathology

Résumé

récupéré en direct d'OpenAlex

There is a large global body of evidence linking exposure to air pollution, especially fine particulate matter (PM2.5), with impacts on all major human organ systems. Furthermore, epidemiological studies have now documented health effects at levels below current national ambient air quality standards. The Health Effects Institute recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in Europe, Canada and the United States [3]. Particular strengths of the studies included the large populations (7-69 million people), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. All three studies documented positive associations between mortality and exposure to PM2.5 at levels as low as 4 μg/m 3 or even lower. Furthermore, the studies observed linear (United States), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality, with no evidence for a threshold. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions in air pollution could yield larger benefits than previously anticipated [3].Based on these and other studies, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) in September 2021. They recommended that annual mean concentrations of PM2.5 should not exceed 5 μg/m 3 , finding that adverse health effects occur above this concentration [4]. They also recommended a set of interim targets, meant to provide a step wise pathway towards achievement of the AQG values set at 35, 25, 15, and 10 μg/m 3 . Governments in the United States and Europe have recently moved toward more stringent PM2.5 standards-9 and 10 µg/m 3 , respectively-to align more closely with the 2021 WHO AQG [5,6]. Meanwhile, the Federal Commission of Air Hygiene advised the Swiss Government to adopt the new WHO AQG values as the national standards [7]. Others such as Uganda have recently adopted National Air Quality Standards for the first time [8], and Brazil has adopted the National Air Quality Policy with progressive air quality targets consistent with the 2021 WHO AQG [9]. This Special Issue, entitled "Science to Foster the WHO Air Quality Guideline Values", presents recent science that underpins the WHO AQG and offers insights into pathways for action. One issue is abundantly clear -the disease burden from air pollution is not borne equally across the world, with countries in Asia, Africa, and the Middle East experiencing the highest levels of ambient PM2.5 and associated health impacts (Figure 1). Hence, there is a particular need to improve air quality in those regions [e.g., 10,11].Much of what is currently known about the adverse effects of ambient air pollution and its solutions come from studies conducted in high-income regions, especially North America and Western Europe, with relatively low air pollution levels, and in more recent years, from studies in China where air pollution levels are relatively high [12,13]. As governments around the world act to improve air quality, there is a continuing need for research to strengthen the local evidence base on disease risk at relatively high levels of air pollution, identify the air pollution sources most responsible for disease burden and assess the public health effectiveness of actions taken to improve air quality. Such studies are also invaluable for strengthening local scientific and infrastructure capacities, raising awareness of local communities, and supporting evidence-based decision making. To strengthen awareness, there is also a need to update Air Quality Index toolsused by many authorities to communicate the state of air quality on a daily basis -with the 2021 WHO AQG [14]. More research is also needed to capture the direct and indirect health effects of climate change more fully, including the interactions with air pollution.Overall, bold air quality and climate actions are needed at all levels-international, national, localand across all sectors such as transport, energy, industry, agriculture, and residential. There is cause for optimism: there are various examples from locations across the globe that show that if action is taken to improve air quality, so does population health [15]. Scientific data and evidence such as that presented in the articles in the Special Issue, will continue to play a fundamental role in fostering evidence-based air quality and climate actions, to reduce the inequity in air quality both within and across countries, and to close the gap between national air quality standards and the 2021 WHO AQG.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,047
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,027
Score d'incertitude au seuil0,090

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,047
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0030,002
Études des sciences et des technologies0,0030,004
Communication savante0,0090,007
Science ouverte0,0050,002
Intégrité de la recherche0,0260,030
Charge utile insuffisante (le modèle a refusé de juger)0,0270,026

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.

Tête enseignante Opus0,077
Tête enseignante GPT0,455
Écart entre enseignants0,378 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2025
Routes d'admission1
Résumé présentoui

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