对于长期暴露于多种空气污染物与心肺死亡之间的关联，尤其是对于污染物水平较高的发展中地区，证据有限。我们旨在描述长期暴露于空气污染物与心肺死亡的个体和联合（多污染物）关联，并确定主要贡献于死亡风险的空气污染物。我们在中国北方的四个城市（天津、沈阳、太原和日照）追踪了37,442名参与者，平均年龄为43.5岁，时间跨度为1998年1月至2019年12月。用卫星衍生的机器学习模型和监测站的日均数值估计了年均直径≤2.5μm（PM2.5）、≤10μm（PM10）、二氧化硫（SO2）和二氧化氮（NO2）的颗粒物浓度。采用时变Cox比例风险模型评估了空气污染物与非意外死因、心血管疾病（CVDs）、非恶性呼吸疾病（RDs）和肺癌的死亡率之间的个体关联，调整了人口统计和社会经济因素。还检查了年龄、性别、收入和教育水平对结果的修饰作用。此外，还应用基于分位数的g-计算与时间-事件数据相结合的方法，评估空气污染物的联合效应和贡献的相对权重。在785,807人年的随访期间，5812人（15.5%）因非意外原因死亡，其中2932人（7.8%）因CVDs死亡，479人（1.3%）因非恶性RDs死亡，552人（1.4%）因肺癌死亡。长期暴露于PM10（均值[基线]：136.5 μg/m3）、PM2.5（均值[基线]：70.2 μg/m3）、SO2（均值[基线]：113.0 μg/m3）和NO2（均值[基线]：39.2 μg/m3）与所有死亡结果均呈相反而一致的关联。PM2.5每10 μg/m3增加与非意外死亡的死亡率（风险比1.20；95%置信区间1.17-1.23）、CVDs（1.23；1.19-1.28）、非恶性RDs（1.37；1.25-1.49）和肺癌（1.14；1.05-1.23）相关联。在个体或联合暴露于空气污染物的暴露-反应关系中观察到线性或超线性形状的单调增加曲线，没有证据显示存在阈值。PM2.5始终对与空气污染物混合物相关的提高的死亡风险做出最大贡献，其次是SO2或 PM10。在高暴露环境中，长期个体和联合暴露于PM10、PM2.5、SO2和NO2与非意外死因、CVDs、非恶性RDs和肺癌的死亡率存在强烈且正相关的关联，PM2.5可能是主要贡献物质。这些关联形状与线性或超线性的暴露-反应关系一致，在本研究所测浓度范围内没有观察到下限阈值。本研究得到了中国国家重点研发计划、中国国家留学基金委员会、中国国家自然科学基金和广东省自然科学基金的支持。© 2023 The Author(s).

Evidence on the associations between long-term exposure to multiple air pollutants and cardiopulmonary mortality is limited, especially for developing regions with higher pollutant levels. We aimed to characterise the individual and joint (multi-pollutant) associations of long-term exposure to air pollutants with cardiopulmonary mortality, and to identify air pollutant that primarily contributes to the mortality risk.We followed 37,442 participants with a mean age of 43.5 years in four cities in northern China (Tianjin, Shenyang, Taiyuan, and Rizhao) from January 1998 to December 2019. Annual particulate matter (PM) with diameters ≤2.5 μm (PM2.5), ≤10 μm (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) were estimated using daily average values from satellite-derived machine learning models and monitoring stations. Time-varying Cox proportional hazards model was used to evaluate the individual association between air pollutants and mortality from non-accidental causes, cardiovascular diseases (CVDs), non-malignant respiratory diseases (RDs) and lung cancer, accounting for demographic and socioeconomic factors. Effect modifications by age, sex, income and education level were also examined. Quantile-based g-Computation integrated with time-to-event data was additionally applied to evaluate the co-effects and the relative weight of contributions for air pollutants.During 785,807 person-years of follow-up, 5812 (15.5%) died from non-accidental causes, among which 2932 (7.8%) were from all CVDs, 479 (1.3%) from non-malignant RDs, and 552 (1.4%) from lung cancer. Long-term exposure to PM10 (mean [baseline]: 136.5 μg/m3), PM2.5 (mean [baseline]: 70.2 μg/m3), SO2 (mean [baseline]: 113.0 μg/m3) and NO2 (mean [baseline]: 39.2 μg/m3) were adversely and consistently associated with all mortality outcomes. A 10 μg/m3 increase in PM2.5 was associated with higher mortality from non-accidental causes (hazard ratio 1.20; 95% confidence interval 1.17-1.23), CVDs (1.23; 1.19-1.28), non-malignant RDs (1.37; 1.25-1.49) and lung cancer (1.14; 1.05-1.23). A monotonically increasing curve with linear or supra-linear shape with no evidence of a threshold was observed for the exposure-response relationship of mortality with individual or joint exposure to air pollutants. PM2.5 consistently contributed most to the elevated mortality risks related to air pollutant mixture, followed by SO2 or PM10.There was a strong and positive association of long-term individual and joint exposure to PM10, PM2.5, SO2, and NO2 with mortalities from non-accidental causes, CVDs, non-malignant RDs and lung cancer in high-exposure settings, with PM2.5 potentially being the main contributor. The shapes of associations were consistent with a linear or supra-linear exposure-response relationship, with no lower threshold observed within the range of concentrations in this study.National Key Research and Development Program of China, the China Scholarship Council, the National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province.© 2023 The Author(s).