Green spaces – sectoral solutions for air pollution and health
Notice bibliographique
Résumé
Green spaces -sectoral solutions for air pollution and health Technical brief Key messagesExposure to green spaces is widely associated with health benefits, including mental (e.g.reduced risk of depression and anxiety), physical (e.g.improved cardiovascular health), healthy behaviours (e.g.physical activity) and social health (e.g.reduced loneliness).These health benefits can result in major health sector cost savings.Additionally, health co-benefits of green spaces may arise from interventions aimed at improving air quality, which could partially explain the relationship between green spaces and health.Air pollution is a major environmental risk to health and urgent action is needed to reduce it.Lowering emissions at their source is the most effective strategy for reducing air pollution, but well-designed and biodiverse green spaces within communities may offer an additional policy option to improve air quality, helping to passively mitigate the impacts of harmful air pollutants and create healthier environments.However, the evidence remains limited and the effects of green spaces on reducing air pollution are variable and context dependent and may even be small or marginal depending on factors such as the type of green space, size, location, biodiversity and weather conditions.Vegetation impacts air quality both directly and indirectly and can have both positive and negative impacts on pollutant concentrations and characteristics depending on the design and setting.For example, air pollutants can be deposited on the surface of plants, and vegetation can decrease pollutant concentration, and act as a barrier to dispersion.Also, green spaces can reduce soil erosion, improve urban drainage and help prevent desertification and dust exposure.Nevertheless, unintended consequences may also occur.For example, increased concentration of pollutants due to the trapping effect (such as in street canyons -streets flanked by buildings on both sides creating a canyon-like environment), emission of biogenic volatile organic compounds (BVOCs) that contribute to the formation of ground-level ozone, and pollen production can have a negative impact on air quality when green spaces are not properly designed and managed. Air Quality, Energy and Health Science and Policy SummariesGreen spaces -sectoral solutions for air pollution and health: Technical brief Priority actions for short-term health benefits include:• the use of properly designed and maintained vegetation in highly polluted areas to reduce exposure to air pollution and decrease the associated disease burden.However, this should be considered as part of a broader strategy including other interventions (e.g.reducing emissions, improving transport infrastructure to promote active mobility, and integrating green spaces with built infrastructure such as green walls and permeable surfaces) to provide more immediate and long-term health benefits.Priority actions for long-term health benefits include:• assessment, guidance and outreach on the role of green spaces in air pollution reduction from different vegetation types and green space design in varying contexts and climates.• plans for afforestation in arid lands that generate dust, considering the climatic conditions of the area and the most efficient and resistant vegetation in each case.Stakeholders from multiple sectors, including the health sector and academia, can play an important role in planning, designing, monitoring and evaluating green spaces to enhance their co-benefits. Key definitionsGreen spaces: Surfaces partially or completely covered by vegetation (e.g.grass, trees, shrubs, etc.).These include -but are not limited to -parks, gardens, street trees, forests and fields, which can be present in urban, suburban or rural areas (1, 2).Green infrastructure: Network of natural and semi-natural systems, such as green roofs or urban forests, that provide environmental, social and economic benefits by using nature-based solutions.It helps create healthier, more resilient urban environments (3).
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,020 |
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 ».