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Enregistrement W2763632521 · doi:10.3390/ijerph14101194

Comparison of Health Risk Assessments of Heavy Metals and As in Sewage Sludge from Wastewater Treatment Plants (WWTPs) for Adults and Children in the Urban District of Taiyuan, China

2017· article· en· W2763632521 sur OpenAlexaboutno aff
Baoling Duan, Wuping Zhang, Haixia Zheng, Chunyan Wu, Qiang Zhang, BU Yu-shan

Notice bibliographique

RevueInternational Journal of Environmental Research and Public Health · 2017
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueHeavy metals in environment
Établissements canadiensnon disponible
Organismes subventionnairesShanxi Scholarship Council of ChinaNational Natural Science Foundation of China
Mots-clésHazard quotientSewageHeavy metalsEnvironmental scienceHealth risk assessmentSewage treatmentHealth riskWastewaterSewage sludgeEnvironmental chemistryRisk assessmentIngestionEnvironmental healthEnvironmental engineeringChemistryMedicine

Résumé

récupéré en direct d'OpenAlex

Abstract: To compare the human health risk of heavy metals and As in sewage sludge between adults and children, samples were collected from five wastewater treatment plants (WWTPs) located in the urban district of Taiyuan, the capital of Shanxi. Heavy metals and As in sewage sludge can be ranked according to the mean concentration in the following order: Cu > Cr > Zn > Pb > As > Hg > Cd. Compared with the concentration limit set by different countries, the heavy metals contents in sewage sludge were all within the standard limits, except for the content of As, which was higher than the threshold limit established by Canada. A health risk assessment recommended by the United States Environmental Protection Agency (USEPA) was used to compare the non-cancer risk and cancer risk between adults and children. Based on the mean and 95% upper confidence limit (UCL) of the average daily dose (ADD), heavy metals and As can be ranked in the order of Cu > Cr > Zn > Pb > As > Hg > Cd for adults, and Cu > Cr > Zn > Pb > Hg > As > Cd for children. Moreover, results of ADDingest and ADDinhale indicated that ingestion was the main pathway for heavy metals and As exposure for both adults and children, and the sum of ADD implied that the exposure to all heavy metals and As for children was 8.65 and 9.93 times higher, respectively, than that for adults according to the mean and 95% UCL. For the non-carcinogenic risk, according to the hazard quotient (HQ), the risk of Cu, Hg and Cr was higher than the risk of Zn and Pb. The hazard index (HI) for adults was 0.144 and 0.208 for the mean and 95% UCL, which was less than the limit value of 1; for children, the HI was 1.26 and 2.25, which is higher than the limit value of 1. This result indicated that children had non-carcinogenic risk, but adults did not. Furthermore, ingestion was the main pathway for non-carcinogenic risk exposure by the HQingest and HQinhale. For the carcinogenic risk, Cd and As were classified as carcinogenic pollutants. The values of RISK for the mean and 95% UCL for adults and children all exceeded the limit value of 1 × 10−5, which implied that adults and children had a carcinogenic risk, and this risk was higher for children than for adults. The results of RISK for As and Cd implied that As was the main pollutant for carcinogenic risk. Moreover, the results of RISKingest and RISKinhale indicated that ingestion was the main pathway. Uncertainty analysis was performed, and the risk ranges of it were greater than certainty analysis, which implied that uncertainty analysis was more conservative than certainty analysis. A comparison of the non-carcinogenic risk and carcinogenic risk for adults and children indicated that children were more sensitive and vulnerable than adults when exposed to the same pollutant 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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,071
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,083
Tête enseignante GPT0,430
Écart entre enseignants0,347 · 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 tête enseignante, 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

Citations69
Publié2017
Routes d'admission1
Résumé présentoui

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