Dong Zhaomin, Wang Hao, Yin Peng, Wang Lijun, Chen Renjie, Fan Wenhong, Xu Yilu, Zhou Maigeng
School of Space and Environment, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China.
School of Space and Environment, Beihang University, Beijing, China.
Lancet Planet Health. 2020 Aug;4(8):e343-e351. doi: 10.1016/S2542-5196(20)30164-9.
Most previous assessments of the hazardous effects attributable to fine particulate matter (PM) exposure have used ambient PM as an exposure metric, resulting in substantial bias in effect estimates. We did a study to examine the association between cause-specific mortality and the time-weighted average of PM exposure after accounting for indoor exposure in 267 cities in China.
We did a nationwide study, using Laser Egg air quality monitors in 36 cities to obtain data for indoor PM concentrations from 18 484 anonymised households between Nov 1, 2015 and July 2, 2018. We developed and validated a nationwide indoor PM prediction model for a further 302 cities by retrieving raw records of hourly concentrations from residents' air sensors; the model was used to predict indoor PM during 2013 to 2018. Daily ambient PM concentration data were estimated by averaging hourly ambient PM concentrations obtained from China's National Urban Air Quality Real-time Publishing Platform. Daily numbers of deaths from all non-accidental causes were obtained from 324 cities from the Disease Surveillance Point System of China between Jan 1, 2013, to Dec 31, 2017, and calculated for 267 cities that had an average daily mortality above three, and data for PM concentrations and meteorological information for at least 1 year between 2013 and 2017. We used distributed lag non-linear models to estimate city-specific associations between cause-specific mortality and reconstructed PM exposure by considering indoor PM exposure. We combined the city-specific effect estimates at the national level using a random effects meta-analysis.
13 972 records of daily indoor PM concentrations for 36 cities, extracted from 47 459 183 raw records from the sensors were included for modelling indoor PM levels. The nationwide indoor PM concentration was 40 μg/m (SD 21) between 2013 and 2017, which was approximately 20% lower than the ambient PM concentration of 50 μg/m (42). An increase of 10 μg/m in time-averaged PM exposure concentrations was associated with increased daily mortality estimates of 0·44% (95% CI 0·33-0·54) for total non-accidental causes, 0·50% (0·37-0·63) for cardiovascular diseases, 0·46% (0·28-0·63) for coronary heart disease, 0·49% (0·32-0·66) for stroke, 0·59% (0·39-0·79) for respiratory diseases, and 0·69% (0·45-0·92) for chronic obstructive pulmonary disease, respectively. Compared with previous estimations based on ambient PM, our estimates approximately doubled the size of the effects related to PM.
This nationwide study revealed a higher mortality risk attributed to time-averaged indoor and ambient PM exposure compared with the risk associated with ambient PM exposure alone, which indicates that caution should be exercised when using ambient PM as a surrogate for PM exposure.
National Natural Science Foundation of China (Youth Program) and the Fundamental Research Project of Beihang University.
以往大多数关于细颗粒物（PM）暴露所致有害影响的评估都将环境PM作为暴露指标，这导致效应估计存在很大偏差。我们开展了一项研究，以探讨在中国267个城市中，特定病因死亡率与考虑室内暴露因素后的PM暴露时间加权平均值之间的关联。
我们进行了一项全国性研究，在36个城市使用激光蛋空气质量监测仪，获取了2015年11月1日至2018年7月2日期间18484户匿名家庭的室内PM浓度数据。通过从居民空气传感器中检索每小时浓度的原始记录，我们开发并验证了另外302个城市的全国室内PM预测模型；该模型用于预测2013年至2018年期间的室内PM。每日环境PM浓度数据通过对从中国国家城市空气质量实时发布平台获取的每小时环境PM浓度进行平均来估算。2013年1月1日至2017年12月31日期间，从中国疾病监测点系统的324个城市获取了所有非意外原因的每日死亡人数，并针对平均每日死亡率高于3的267个城市进行了计算，以及2013年至2017年期间至少1年的PM浓度和气象信息数据。我们使用分布滞后非线性模型，通过考虑室内PM暴露来估计特定病因死亡率与重建的PM暴露之间的城市特定关联。我们使用随机效应荟萃分析在国家层面合并城市特定效应估计值。
从传感器的47459183条原始记录中提取了36个城市的13972条室内PM浓度每日记录，用于室内PM水平建模。2013年至2017年全国室内PM浓度为40μg/m³（标准差21），比环境PM浓度50μg/m³（标准差42）低约20%。时间平均PM暴露浓度每增加10μg/m³，与总非意外原因的每日死亡率估计值增加0.44%（95%置信区间0.33 - 0.54）、心血管疾病增加0.50%（0.37 - 0.63）、冠心病增加0.46%（0.28 - 0.63）、中风增加0.49%（0.32 - 0.66）、呼吸系统疾病增加0.59%（0.39 - 0.79）以及慢性阻塞性肺疾病增加0.69%（0.45 - 0.92）相关。与以往基于环境PM的估计相比，我们的估计使与PM相关的效应大小增加了约一倍。
这项全国性研究表明，与仅与环境PM暴露相关的风险相比，时间平均室内和环境PM暴露所致的死亡风险更高，这表明在将环境PM用作PM暴露的替代指标时应谨慎。
中国国家自然科学基金（青年项目）和北京航空航天大学基础研究项目。
