Investigating the fate of gold nanoparticles taken up by trees through leaf and root pathways
Notice bibliographique
Résumé
Industrial activities and human population growth have resulted in an unprecedented increase in the release of particulate matter (PM) into the environment. Incidental nanoparticles (NPs) as a byproduct of industrial processes and engineered NPs are being discharged into terrestrial and aquatic ecosystems. Several studies on the impact of PM and NPs on human health have been conducted this century, but their effects on plants are poorly understood. What happens to them in forest ecosystems and trees has yet to be explored. The use of dendrochemistry to monitor air pollution is essential to provide past levels of contamination. Several studies have shown the ability of trees to accumulate pollutants into their annual rings, but the effect of particles at nano-scale is still largely unknown and their presence in tree rings unexplored. In July 2019, a greenhouse experiment was conducted in order a) to confirm the uptake and transport of NPs in trees, b) to determine the delivery efficiency of different NPs entry pathways (leaves and roots), and c) to investigate the influence of surface-charged NPs on their uptake and transport. The fate of gold nanoparticles (AuNPs) was investigated in two tree species, European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.). In the experiment, 40nm surface-charged AuNPs (positive, negative, and neutral AuNPs, hereafter referred to as treatments) were supplied once, separately to leaves and to roots. Twenty days after the treatment, Au concentration (mg kg-1) in leaves, stem and roots was determined by ICP-MS. In the leaf supply, Au concentrations were higher in leaves (98.3% and 99.2% on average, in beech and Scots pine respectively) and stems (1.4% and 0.45% on average) than in roots (0.3% and 0.35% on average). In the root supply, higher Au concentration was found in the roots (99.9 % on average in both species) than in the stems (0.1% on average in both species), whereas gold was not detected in the leaves. In the majority of cases, the measured Au was greater in beech than in Scots pine, probably due to their higher stomatal activity. AuNP concentrations among the treatments were significantly different (p value < 0.05), but distribution pattern in Scots pine were not discernible. In conclusion, AuNPs can be taken up by roots and leaves and transported to different compartments of trees. Different entry pathways influence the NP delivery within the plant tissues through transport mechanisms that are still unclear. It seems that NPs are allowed to move faster from the leaves through the phloem to the xylem and are further distributed throughout the plant system, including to the roots. The influence of surface-charged nanoparticles on their uptake and transport is not completely clear, and further research is needed in order to understand their behavior in trees. This study shows the potential of trees as proxies to monitor NPs in forest ecosystems. Using tree rings as spatiotemporal indicators of the impact of particles on the environment will help a quantitative risk assessment and management of atmospheric particulate matter and NPs concentrations in the environment.
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 | 0,000 |
| 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 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 ».