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

• Published in: Chemosphere (Oxford) p. 143084
• Main Authors: Zhang, Jiake, Hu, Yuxiang, Wang, Xing, Ding, Xuejie, Cen, Xingzu, Wang, Bin, Yang, Shijie, Ye, Zi, Qiu, Weihong, Chen, Weihong, Zhou, Min
• Format: Journal Article
• Language: English
• Published: England 12.08.2024
• Subjects: BKMR model; Fine particulate matter; Lung function; Explainable Machine Learning model; WQS model; Heavy metal mixtures
• ISSN: 1879-1298

There are a few reports on the associations between fine particulate matter (PM )-bound heavy metals and lung function. To evaluate the associations of single and mixed PM -bound heavy metals with lung function. This study included 316 observations of 224 Chinese adults from the Wuhan-Zhuhai cohort over two study periods, and measured participants' personal PM -bound heavy metals and lung function. Three linear mixed models, including the single constituent model, the PM -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 -bound heavy metal mixtures and lung function. In the single exposure analyses, significant negative associations of PM -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 -bound heavy metal mixture. The BKMR models suggested negative associations of PM -bound lead and antimony with lung function. In addition, PM -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 -bound lead, manganese, and vanadium, and increased with the elevated PM -bound copper. The negative relationships were detected between PM -bound heavy metal mixture and FEV1/FVC, MMF, as well as FEF75. Among the PM -bound heavy metal mixture, PM -bound lead, antimony, manganese, and vanadium were negatively associated with FEV1/FVC, MMF, and FEF75, while PM -bound copper was positively associated with FEV1/FVC, MMF, and FEF75.