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长期暴露于细颗粒物无机化学成分与心肺死亡率之间的关联:中国北方一项为期22年的队列研究。

Association between Long-Term Exposure to PM Inorganic Chemical Compositions and Cardiopulmonary Mortality: A 22-Year Cohort Study in Northern China.

作者信息

Sun Hongyue, Chen Xi, Huang Wenzhong, Wei Jing, Yang Xueli, Shan Anqi, Zhang Liwen, Zhang Honglu, He Jiayu, Pan Chengjie, Li Jingjing, Wu Jing, Wang Tong, Chen Jie, Guo Yuming, Tong Shilu, Dong Guanghui, Tang Nai-Jun

机构信息

Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China.

Tianjin Key Laboratory of Environment Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China.

出版信息

Environ Health (Wash). 2024 Jun 5;2(8):530-540. doi: 10.1021/envhealth.4c00020. eCollection 2024 Aug 16.

DOI:
10.1021/envhealth.4c00020
PMID:39474285
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11503951/
Abstract

Particulate matter with diameters ≤2.5 μm (PM) has been identified as a significant air pollutant contributing to premature mortality. Nevertheless, the specific compositions within PM that play the most crucial role remain unclear, especially in areas with high pollution concentrations. This study aims to investigate the individual and joint mortality risks associated with PM inorganic chemical compositions and identify primary contributors. In 1998, we conducted a prospective cohort study in four northern Chinese cities (Tianjin, Shenyang, Taiyuan, and Rizhao). Satellite-based machine learning models calculated PM inorganic chemical compositions, including sulfate (SO ), nitrate (NO ), ammonium (NH ), and chloride (Cl). A time-varying Cox proportional hazards model was applied to analyze associations between these compositions and cardiorespiratory mortality, encompassing nonaccidental causes, cardiovascular diseases (CVDs), nonmalignant respiratory diseases (RDs), and lung cancer. The quantile-based g-computation model evaluated joint exposure effects and relative contributions of the compositions. Stratified analysis was used to identify vulnerable subpopulations. During 785,807 person-years of follow-up, 5812 (15.5%) deaths occurred from nonaccidental causes, including 2932 (7.8%) from all CVDs, 479 (1.3%) from nonmalignant RDs, and 552 (1.4%) from lung cancer. Every interquartile range (IQR) increase in SO was associated with mortality from nonaccidental causes (hazard ratio: 1.860; 95% confidence interval: 1.809, 1.911), CVDs (1.909; 1.836, 1.985), nonmalignant RDs (2.178; 1.975, 2.403), and lung cancer (1.773; 1.624, 1.937). In the joint exposure model, a simultaneous rise of one IQR in all four compositions increased the risk of cardiorespiratory mortality by at least 36.3%, with long-term exposure to SO contributing the most to nonaccidental and cardiopulmonary deaths. Individuals with higher incomes and lower education levels were found to be more vulnerable. Long-term exposure to higher levels of PM inorganic compositions was associated with significantly increased cardiopulmonary mortality, with SO potentially being the primary contributor. These findings offer insights into how PM sources impact health, aiding the development of more effective governance measures.

摘要

直径≤2.5微米的颗粒物(PM)已被确认为导致过早死亡的重要空气污染物。然而,PM中起最关键作用的具体成分仍不明确,尤其是在污染浓度高的地区。本研究旨在调查与PM无机化学成分相关的个体和联合死亡风险,并确定主要贡献因素。1998年,我们在中国北方四个城市(天津、沈阳、太原和日照)进行了一项前瞻性队列研究。基于卫星的机器学习模型计算了PM无机化学成分,包括硫酸盐(SO)、硝酸盐(NO)、铵(NH)和氯化物(Cl)。应用时变Cox比例风险模型分析这些成分与心肺死亡率之间的关联,包括非意外原因、心血管疾病(CVD)、非恶性呼吸道疾病(RD)和肺癌。基于分位数的g计算模型评估了联合暴露效应和成分的相对贡献。采用分层分析来确定脆弱亚人群。在785,807人年的随访期间,5812人(15.5%)死于非意外原因,其中2932人(7.8%)死于所有心血管疾病,479人(1.3%)死于非恶性呼吸道疾病,552人(1.4%)死于肺癌。SO每增加一个四分位数间距(IQR)与非意外原因死亡率相关(风险比:1.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/649e/11503951/d8e46c6399d9/eh4c00020_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/649e/11503951/7468bd5d476c/eh4c00020_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/649e/11503951/d8e46c6399d9/eh4c00020_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/649e/11503951/7468bd5d476c/eh4c00020_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/649e/11503951/d8e46c6399d9/eh4c00020_0002.jpg

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