Influence of Matrix Composition on the Bioaccessibility of Copper, Zinc, and Nickel in Urban Residential Dust and Soil
Notice bibliographique
Résumé
ABSTRACT This study examines factors affecting oral bioaccessibility of metals in household dust, in particular metal speciation, organic carbon content, and particle size, with the goal of addressing risk assessment information requirements. Investigation of copper (Cu) and zinc (Zn) speciation in two size fractions of dust (< 36 μ m and 80–150 μ m) using synchrotron X-ray absorption spectroscopy (XAS) indicates that the two metals are bound to different components of the dust: Cu is predominately associated with the organic phase of the dust, while Zn is predominately associated with the mineral fraction. Total and bioaccessible Cu, nickel (Ni), and Zn were determined (on dry weight basis) in the < 150 μ m size fraction of a set of archived indoor dust samples (n = 63) and corresponding garden soil samples (n = 66) from the City of Ottawa, Canada. The median bioaccessible Cu content is 66 μ g g−1 in dust compared to 5 μ g g−1 in soil; the median bioaccessible Ni content is 16 μ g g−1 in dust compared to 2 μ g g−1 in soil; and the median bioaccessible Zn content is 410 μ g g−1 in dust compared to 18 μ g g−1 in soil. For the same data set, the median total Cu content is 152 μ g g−1 in dust compared to 17 μ g g−1 in soil; the median total Ni content is 41 μ g g−1 in dust compared to 13 μ g g−1 in soil; and the median total Zn content is 626 μ g g−1 in dust compared to 84 μ g g−1 in soil. Organic carbon is elevated in indoor dust (median 28%) compared to soil (median 5%), and is a key factor controlling metal partitioning and therefore bioaccessibility. The results show that house dust and soil have distinct geochemical signatures and should not be treated as identical media in exposure and risk assessments. Separate measurements of the indoor and outdoor environment are essential to improve the accuracy of residential risk assessments. Key Words: soilhouse dustbioaccessibilitymetal speciationorganic carbonSynchrotron XASresidential exposure assessment ACKNOWLEDGMENTS Part of this research was conducted at the National Synchrotron Light Source, Brookhaven National Laboratory (Upton, NY), which is supported by the U.S. Department of Energy, Division of Materials Sciences and Division of Chemical Sciences. The authors gratefully acknowledge support from Health Canada's Contaminated Sites Division and the NSERC MITHE Research Network (see www.mithe-rn.org for a complete list of sponsors). Sincere appreciation is extended to David Miller and Virginia Salares for their collaboration in the original 2001–2002 survey, to Jianjun Niu and Nicolas Gilbert for their helpful review of an early version of the manuscript, to Howard David Gardner for valuable assistance with database design and management, and to HERA Editor Peter Chapman and three anonymous reviewers for their many constructive and insightful comments. Notes *Two of the 66 soil samples were below Limit of Detection (LOD) for Cu (value of 0.5 * LOD was substituted in calculations). †Zncarbhyd: Zn hydroxyl carbonate; ZncopptFe: Zn co-precipitated with ferrihydrite at a Fe/Zn molar ratio of 100; ZnadsFe: Zn adsorbed on ferrihydrite at 300 mmol Zn per kg ferrihydrite; Zn sulfide: values reported are for ZnS but similar fitting results were obtained with sphalerite; ‡Σ : sum of fractions before normalization; χ2: chi-square values; ¥% of total Zn after normalization to sum = 100% ± computed standard errors for the linear coefficients. †Cu_cysteine: Cu adsorbed on cysteine (pH 7.0); Cu ads. Fe: Cu adsorbed on 2-line ferrihy- drite at a rate of 125 mmol Cu per kg ferrihydrite (pH 6.0); ¥Σ : sum of fractions before normalization; χ2: chi-square values; ‡% of total Cu after normalization to sum = 100% ± computed standard errors for the linear coefficients; ††nd: not determined. * p < .002; for all others p < .0001.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 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 | 0,001 |
| 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 tête enseignante, 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 ».