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Enregistrement W6963770385 · doi:10.25439/rmt.27597018

Identification of trace organic chemicals attached to plastic fragments collected in Melbourne, Port Philip Bay, Victoria

2022· dissertation· en· W6963770385 sur OpenAlexaboutno aff

Notice bibliographique

RevueRMIT Research Repository (RMIT University Library) · 2022
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueMicroplastics and Plastic Pollution
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMicroplasticsDebrisPlastic pollutionMarine debrisContaminationPollutionBiodegradationAquatic ecosystemPlastic waste

Résumé

récupéré en direct d'OpenAlex

The increase in plastic pollution from plastic debris in the aquatic environment has caused increasing concern worldwide over the past decade. According to available statistics, the global production of plastics reached 359 million tonnes in 2018. Extensive use of plastics with high chemical stability and resistance has seen a concomitant increase in their release into the environment due to poor waste management practices. Most of the plastic in the marine environment comes from the land, although around 20% is due to marine activities such as shipping and fishing. Plastic litter breaks down over time following exposure to solar radiation, oxidation processes and physical processes that convert large plastic debris into smaller particles. According to their sizes, they can be meso-plastics (5-40 mm), microplastics (1-5000 µm) and nano plastics (NPs) (1-1000 nm). Microplastics (MPs) are classified as either primary or secondary MPs. Primary MPs enter the environment without change, i.e., fugitive particles such as plastic resin pellets used in microplastic production or consumer products such as microbeads in cosmetics. Secondary MPs are the result of the degradation of larger plastics. When plastic particles become smaller, and nano particles form, their physical and chemical properties, such as the ratio of surface area to size, strength, conductivity, reactivity, and biological response, change dramatically. In addition, sorption of organic chemicals and metals, weathering, UV, and biodegradation are factors that affect the composition of microplastics. There is limited information on MPs in Australian aquatic environments, and to date, no studies have explored chemical concentrations on MP debris in Victoria. Consequently, the project sought to identify and quantify organic chemical pollutants attached to the plastic fragments. The plastic samples were obtained by the Port Philip Ecocentre from Stony Creek, the Yarra and Maribyrnong rivers, and from 6 beaches along the eastern coastline of Port Phillip Bay, including Brighton, Rickett’s Point, Canadian Bay, Seaford, Frankston, and Mount Martha. To achieve the results, a comprehensive analytical multi-residue method using gas chromatography-mass spectroscopy with automated identification and quantification system database (AIQS-DB) was employed to identify and quantify chemicals presented in extracted liquid from plastic samples. The different types of plastic polymer in the debris were identified by FT-IR, then the pollution profiles of these types of plastics were detected by GC-MS spectrometry database (AIQS-DB). Microplastic samples were collected from Stony Creek after an industrial fire in nearby West Footscray. The purpose was to estimate the pollutant loads on plastic debris in the runoff flow path and if there was any possibility that this plastic debris accumulated fire-related chemicals. The result showed that samples close to the fire contained more fire-related than the two other sites. To obtain an insight into pollution profile for organic chemicals present on plastic debris on trawl samples collected from Maribyrnong and Yarra rivers, MPs were extracted from trawls made on 6th September 2018. The data demonstrated that fragments had a higher abundance in the collected samples, and polypropylene and polyethylene were the predominant polymers. Forty-six and forty-two chemicals were detected in trawl samples in Yarra and Maribyrnong River samples, respectively. The other objective of this research was to discuss regarding associated chemicals of plastic litter in 6 beaches on the eastern coastline of Port Phillip Bay. These plastic debris is divided into two groups pellets and plastic fragments. Polyethylene and polypropylene were the common types of plastics in beached samples. Analysis data showed seventy-eight and sixty chemicals in resin pellet beached samples, while seventy-seven and sixty-three of various chemicals were identified in extracts of fragments collected in Port Phillip Bay in 2019 and 2020, respectively. The toxicity of plastic particles depends not only on the size of particles, their concentration, and the period of exposure of any organism to them, but also on the sorption of contaminants by plastics or their additives and their combinations. These particles can be ingested by organisms and block their ingestion system, or they can release their chemicals either the ones sorbed on them or their additives. Ecotoxicological studies have shown some adverse effects of microplastics on some organisms. Although some sort of estimation in terms of polymer hazards considered for collected samples of beaches and rivers, the accurate risk assessment of microplastics could not be performed in this study because of lack of data and uncertainty in the measurements and sampling. However, this estimation categorised Yarra River and some beaches including Brighton and Ricketts Point samples as highly polluted environment in terms of their polymer risk index of samples. This study adds to the existing information on the distribution of contaminants in Melbourne’s aquatic environment. It may help determine the role of plastic debris in transferring trace organic chemicals to marine organisms. It may be useful for policy makers who want to take adequate measures to reduce MP related pollution.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,193
Score d'incertitude au seuil0,383

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,010
Tête enseignante GPT0,246
Écart entre enseignants0,236 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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