Sun Hongyue, Pan Chengjie, Yan Mengfan, Wang Zhongli, He Jiayu, Zhang Honglu, Yang Ze, Wang Zinuo, Wang Yiqing, Liu Hongyan, Yang Xueli, Hou Fang, Wei Jing, Yu Pei, Chen Xi, Tang Nai-Jun
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.
School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
Sci Total Environ. 2025 Jan 10;959:178219. doi: 10.1016/j.scitotenv.2024.178219. Epub 2024 Dec 23.
Particulate matter with diameters ≤2.5 μm (PM) is a significant air pollutant associated with hypertension and diabetes. However, the specific contributions of its components and their joint exposure with green spaces remain poorly understood, especially in developing regions.
This study aims to investigate the individual and joint impacts of PM and its components on the middle-aged and older adults, identify primary risk factors, and assess disease risks associated with simultaneous exposure to green spaces.
We conducted a prospective cohort study in Tianjin from 2014 to 2021, involving individuals aged ≥45 years. Satellite-based machine learning models quantified PM components, including black carbon (BC), organic matter (OM), sulfate (SO), nitrate (NO), ammonium (NH), and chloride (Cl). Residential greenness was assessed using the Normalized Difference Vegetation Index (NDVI). A time-varying Cox proportional hazards model analyzed associations between PM components and the incidence of hypertension and diabetes. The quantile g-computation model evaluated joint exposure effects and relative contributions of the components. Pollutants and NDVI were dichotomized using median values and combined to create a joint exposure model, aimed at exploring the potential effects of NDVI. Stratified analyses were performed to identify vulnerable subpopulations.
Over 241,528.73 person-years of follow-up, there were 15,747 (38.34 %) new cases of hypertension and 8945 (13.59 %) new cases of diabetes. Each standard deviation (SD) increase in OM was associated with increased incidence of hypertension (hazard ratio: 1.609; 95 % confidence interval: 1.583, 1.636) and diabetes (1.484; 1.453, 1.515). Joint exposure to components is linked to higher incidence of hypertension and diabetes, with OM identified as the primary contributor. The joint exposure model indicated elevated population risk in areas with low NDVI and high PM concentrations, particularly affecting males and individuals younger than 60 years.
Long-term exposure to higher levels of PM components is significantly associated with increased hypertension and diabetes, with OM potentially being the primary contributor. Joint exposure to green space may mitigate these risks. These findings highlight how PM sources impact health, informing more effective governance measures.
直径≤2.5微米的颗粒物（PM）是一种与高血压和糖尿病相关的重要空气污染物。然而，其成分的具体作用以及它们与绿地的联合暴露情况仍知之甚少，尤其是在发展中地区。
本研究旨在调查PM及其成分对中老年人的个体和联合影响，确定主要风险因素，并评估与同时接触绿地相关的疾病风险。
我们于2014年至2021年在天津进行了一项前瞻性队列研究，纳入年龄≥45岁的个体。基于卫星的机器学习模型对PM成分进行了量化，包括黑碳（BC）、有机物（OM）、硫酸盐（SO）、硝酸盐（NO）、铵（NH）和氯化物（Cl）。使用归一化植被指数（NDVI）评估居住绿地情况。采用时变Cox比例风险模型分析PM成分与高血压和糖尿病发病率之间的关联。分位数g计算模型评估了成分的联合暴露效应和相对贡献。将污染物和NDVI按中位数进行二分法划分，并结合起来创建联合暴露模型，旨在探索NDVI的潜在影响。进行分层分析以确定脆弱亚人群。
在超过241,528.73人年的随访中，有15,747例（38.34%）新发高血压病例和8945例（13.59%）新发糖尿病病例。OM每增加一个标准差（SD），与高血压发病率增加（风险比：1.609；95%置信区间：1.583, 1.636）和糖尿病发病率增加（1.484；1.453, 1.515）相关。成分的联合暴露与高血压和糖尿病的更高发病率相关，其中OM被确定为主要贡献因素。联合暴露模型表明，在NDVI低且PM浓度高的地区人群风险升高，尤其影响男性和60岁以下个体。
长期暴露于较高水平的PM成分与高血压和糖尿病发病率增加显著相关，OM可能是主要贡献因素。与绿地的联合暴露可能会减轻这些风险。这些发现突出了PM来源对健康的影响，为更有效的治理措施提供了依据。
