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Associations of personal PM 2.5 -bound heavy metals and heavy metal mixture with lung function: Results from a panel study in Chinese urban residents.
- Source :
-
Chemosphere [Chemosphere] 2024 Sep; Vol. 364, pp. 143084. Date of Electronic Publication: 2024 Aug 13. - Publication Year :
- 2024
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Abstract
- Background: There are a few reports on the associations between fine particulate matter (PM <subscript>2.5</subscript> )-bound heavy metals and lung function.<br />Objectives: To evaluate the associations of single and mixed PM <subscript>2.5</subscript> -bound heavy metals with lung function.<br />Methods: This study included 316 observations of 224 Chinese adults from the Wuhan-Zhuhai cohort over two study periods, and measured participants' personal PM <subscript>2.5</subscript> -bound heavy metals and lung function. Three linear mixed models, including the single constituent model, the PM <subscript>2.5</subscript> -adjusted constituent model, and the constituent residual model were used to evaluate the association between single metal and lung function. Mixed exposure models including Bayesian kernel machine regression (BKMR) model, weighted quantile sum (WQS) model, and Explainable Machine Learning model were used to assess the relationship between PM <subscript>2.5</subscript> -bound heavy metal mixtures and lung function.<br />Results: In the single exposure analyses, significant negative associations of PM <subscript>2.5</subscript> -bound lead, antimony, and cadmium with peak expiratory flow (PEF) were observed. In the mixed exposure analyses, significant decreases in forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC), maximal mid-expiratory flow (MMF), and forced expiratory flow at 75% of the pulmonary volume (FEF75) were associated with the increased PM <subscript>2.5</subscript> -bound heavy metal mixture. The BKMR models suggested negative associations of PM <subscript>2.5</subscript> -bound lead and antimony with lung function. In addition, PM <subscript>2.5</subscript> -bound copper was positively associated with FEV1/FVC, MMF, and FEF75. The Explainable Machine Learning models suggested that FEV1/FVC, MMF, and FEF75 decreased with the elevated PM <subscript>2.5</subscript> -bound lead, manganese, and vanadium, and increased with the elevated PM <subscript>2.5</subscript> -bound copper.<br />Conclusions: The negative relationships were detected between PM <subscript>2.5</subscript> -bound heavy metal mixture and FEV1/FVC, MMF, as well as FEF75. Among the PM <subscript>2.5</subscript> -bound heavy metal mixture, PM <subscript>2.5</subscript> -bound lead, antimony, manganese, and vanadium were negatively associated with FEV1/FVC, MMF, and FEF75, while PM <subscript>2.5</subscript> -bound copper was positively associated with FEV1/FVC, MMF, and FEF75.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 Elsevier Ltd. All rights reserved.)
- Subjects :
- Humans
Male
Female
Middle Aged
Adult
China
Environmental Exposure statistics & numerical data
Respiratory Function Tests
Forced Expiratory Volume
Bayes Theorem
Vital Capacity
Cohort Studies
Air Pollution statistics & numerical data
Aged
East Asian People
Particulate Matter analysis
Metals, Heavy analysis
Air Pollutants analysis
Lung drug effects
Subjects
Details
- Language :
- English
- ISSN :
- 1879-1298
- Volume :
- 364
- Database :
- MEDLINE
- Journal :
- Chemosphere
- Publication Type :
- Academic Journal
- Accession number :
- 39142394
- Full Text :
- https://doi.org/10.1016/j.chemosphere.2024.143084