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Enregistrement W7080625003 · doi:10.25949/27885312

Measuring and evaluating contamination in homes using geochemical analysis: sources, pathways, and health risks

2024· dissertation· en· W7080625003 sur OpenAlexaboutno aff

Notice bibliographique

RevueMacquarie University · 2024
Typedissertation
Langueen
DomaineComputer Science
ThématiqueGeochemistry and Geologic Mapping
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContaminationParticulatesTrace metalSmeltingHealth riskTRACE (psycholinguistics)Human health

Résumé

récupéré en direct d'OpenAlex

Residents spend significant time at home, aggravating concerns about their exposure to contaminants and the associated health risks, highlighting the critical importance of addressing home contamination. This thesis delves into the intricate relationship between outdoor and indoor environments and their contamination pathways. This study enhances our understanding of home environment contamination and human exposure by employing a multi-approach that includes analytical methods, comprehensive probabilistic health risk assessment, analysis of trace metal sources, quantification of particle infiltration, and identification of indoor particulate matter sizes. Unravelling these connections and pathways is essential for taking informed actions and implementing effective measures to minimize exposure risks.The utility of pXRF as a practical and cost-effective method for analysing dust wipes in the mining and smelting areas of Noumea, and Thio, New Caledonia and Tsumeb, Namibia was carried out in Chapter 2. A comparison of pXRF and ICP-MS dust wipes analysis (n = 87) revealed strong agreement, with Spearman Rho correlations (0.489 - 0.956, p < 0.01) and coefficients of variation (r²: 0.432 - 0.989). Additionally, equations derived from ICP-MS results corrected pXRF data, improving mean recovery from 75-303% to 92-110%.Chapter 3 focused on studying polycyclic aromatic hydrocarbons and trace metal levels, including chromium (Cr), nickel (Ni), copper (Cu), lead (Pb), and zinc (Zn), their sources, and health risks in homes across the industrialised Illawarra region and Australia. Elevated concentrations of these trace metals were found in home garden soils and indoor dust near industrial areas, showing how outdoor industrial contamination affects indoor trace metal levels. Associations between arsenic (As), lead (Pb), and zinc (Zn) concentrations in indoor dust and home age were noted in Illawarra homes, highlighting the combined effects of aging homes and outdoor industrial activities on trace metal levels, often surpassing outdoor levels. Low polycyclic aromatic hydrocarbons (PAHs) were found in 7 of 23 homes, remaining below carcinogenic thresholds. Using Monte Carlo probabilistic human health risk assessment, higher non-carcinogenic and carcinogenic risks among children than adults were revealed when exposed to indoor dust and garden soils in the Illawarra region and Australian homes (p<0.01).Chapter 4 explored the pathways of trace metals from road dust and garden soils into indoor dust within homes. Higher Cu concentrations in road dust than in garden soils indicated an interaction between these environmental mediums. Similarly, correlations between garden soils and indoor dust for As and Zn showed their migration and interaction, with indoor dust having significantly higher concentrations of these trace metals. Conversely, Pb showed persistence and correlation between road dust, garden soils, and indoor dust, confirming its presence and resuspension across outdoor and indoor environments in Greater Sydney. Furthermore, a detailed analysis of individual dust particles using Scanning Electron Microscope (SEM) was used to estimate the proportion of indoor dust particles originating from outdoor sources. The study revealed that tracked-in outdoor particles represented an average of 57% of the total particle number in indoor dust samples.Chapter 5 measured and evaluated particle size distribution, morphometry, and morphology in indoor dust, drawing from observations on migration patterns, heightened levels of trace metals indoors than outdoors (p < 0.05), and the fine texture of indoor dust (Chapter 3 and 4).Analysis revealed that particles <250 μm comprised 45% of the bulk, with particles smaller than 20 μm making up 9% of the sample. PM2.5 (particles < 2.5 μm) constituted a substantial portion (76%) of suspended indoor 'settled dust' smaller than 20 μm from homes in Australia, Canada, China, Ghana, and Mexico, characterised by notably high circularity. These fine particles pose inhalation risks due to their size and shape, potentially penetrating deeply into the respiratory system.Particles < 20 μm showed higher concentrations of arsenic (As), chromium (Cr), and nickel (Ni) compared to < 250 μm particles, raising concerns about potential health effects from inhalation or ingestion. On the other hand, consistent concentrations of lead (Pb) across particle sizes indicate potential health risks regardless of size or exposure route, particularly in Australian homes where mean Pb concentrations exceeded those in other homes studied. Furthermore, particles smaller than or equal to 2.5 μm corresponding to cerussite, a 'white' lead paint manufacturing component, were identified as capable of reaching deep into the alveolar region of the lungs. Cerussite's solubility under acidic conditions found in the alveolar region suggests potential health risks upon inhalation or ingestion.By uncovering the link between outdoor and indoor contamination and potential health risks across its chapters, the findings in this thesis contributes invaluable insights to the existing literature on home environments and holds significant implications for policy development highlighting the urgency of addressing home contamination as a crucial step in safeguarding human health.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

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

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.

Tête enseignante Opus0,089
Tête enseignante GPT0,294
Écart entre enseignants0,205 · 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

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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