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中国上海住宅细颗粒物渗透的估算。

Estimation of residential fine particulate matter infiltration in Shanghai, China.

机构信息

School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China; Environmental Health Department, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.

School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai, China.

出版信息

Environ Pollut. 2018 Feb;233:494-500. doi: 10.1016/j.envpol.2017.10.054. Epub 2017 Nov 5.

Abstract

Ambient concentrations of fine particulate matter (PM) concentration is often used as an exposure surrogate to estimate PM health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM infiltration factor (F) for each individual residence, we aimed to build models for residential PMF prediction and to evaluate seasonal F variations among residences. We repeated collected paired indoor and outdoor PM filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM-bound sulfur on the filters was measured by X-ray fluorescence for PMF calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PMF across all observations was 0.83 (±0.18). F was found higher and more varied in transitional season (12-25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%-68.4% of the variance in 7-day averages of F, The LOOCV analysis showed an R of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PMF between seasons and across residences within season indicated the important source of outdoor-generated PM exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM exposure.

摘要

环境细颗粒物(PM)浓度常被用作暴露替代物,以在流行病学研究中估计 PM 对健康的影响。在估计 PM 暴露时,如果忽略了室外 PM 进入室内环境的量的潜在变化,可能会导致暴露错误分类,尤其是当人们大部分时间都在室内时。由于对每个单独的住宅进行 PM 渗透因子(F)的测量是不切实际的,因此我们旨在建立住宅 PMF 预测模型,并评估住宅之间季节性 F 变化。我们在中国上海的 48 个典型住宅中,每个季节(热、冷和过渡季节)连续 7 天重复采集室内外 PM 滤膜对样。通过 X 射线荧光法测量滤膜上的 PM 结合硫,用于 PMF 计算。然后,我们使用逐步多元线性回归,使用气候变量和基于问卷的预测因子构建具有季节特异性的模型。所有模型均通过留一法交叉验证(LOOCV)的决定系数(R)和均方根误差(RMSE)进行评估。所有观测结果的 7 天平均 PMF(±SD)为 0.83(±0.18)。在过渡季节(12-25°C),F 高于热季节(>25°C)和冷季节(<12°C),且变化更大。在热和冷季节,空调使用和气象因素是最重要的预测因子;在过渡季节,住宅楼层和建筑年代是最好的预测因子。模型预测了 7 天平均 F 的 60.0%-68.4%的方差。LOOCV 分析显示 R 为 0.52,RMSE 为 0.11。我们发现季节间住宅 PMF 差异较大,且同一季节内住宅间差异较大,这表明户外生成 PM 暴露异质性是流行病学研究中的一个重要来源。我们基于现成数据建立的模型可能会提高 PM 暴露健康影响估计的准确性。

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