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Estimation of PM_(2.5) infiltration factors and personal exposure factors in two megacities, China

机译：中国两个特大城市PM_（2.5）渗透因子和个人暴露因子的估算

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摘要

This study estimates infiltration factors (F-inf) and ambient personal exposure factors (F-pex) for fine particulate matter (PM2.5) in two Chinese megacities, and constructs predictive models to explore their determinants. Personal-indoor-outdoor PM2.5 filter samples were collected for five consecutive days from 33 residences (of retired adults) in Nanjing (NJ) and Beijing (BJ), China, in both the non-heating season (NHS) and the heating season (HS). Elemental sulfur in filter deposits was determined by energy-dispersive X-ray fluorescence for PM2.5 F-inf and F-pex estimations. Season-specific models developed by stepwise multiple linear regression were evaluated using R-2 and root mean square error (RMSE). The median [interquartile range (IQR)] of F-inf varied from 0.76 (0.15) in the HS to 0.93 (0.11) in the NHS in NJ; and from 0.67 (0.16) to 0.86 (0.12) in BJ. Similarly, F-pex was significantly higher during the NHS [NJ: 0.95 (0.07); BJ: 0.89 (0.14)] than during the HS [NJ: 0.76 (0.17); BJ: 0.67 (0.11); p 0.0001]. Common predictors of F-inf and F-pex included window opening behaviors, meteorological variables, and building age. Moreover, air conditioning and distance to the nearest major road had an influence on F-inf, while predictors of F(pex )were more related to human behavior and activity (e.g., time spent outdoors and transportation). The models accounted for 35.4%-68.1% (RMSE: 0.065-0.101) and 41.6%-77.0% (RMSE: 0.033-0.103) of the variance in F-inf and F-pex respectively. By indicating the determinants of F-inf and F-pex these models can improve ambient PM2.5 exposure assessment and reduce exposure misclassification.
机译：这项研究估算了两个中国特大城市中细颗粒物（PM2.5）的渗透因子（F-inf）和环境个人暴露因子（F-pex），并构建了预测模型以探索其决定因素。在非供暖季节（NHS）和供暖期间，连续五天从南京（NJ）和北京（BJ）的33个居住区（退休成年人）收集了个人室内外PM2.5过滤器样品季节（HS）。通过能量分散X射线荧光确定PM2.5 F-inf和F-pex估算值来确定滤池沉积物中的元素硫。使用R-2和均方根误差（RMSE）对通过逐步多元线性回归开发的特定季节模型进行了评估。 F-inf的中值[四分位数间距（IQR）]从HS的0.76（0.15）到NJ的NHS的0.93（0.11）不等；以及BJ中的0.67（0.16）至0.86（0.12）。同样，在NHS期间F-pex明显更高[NJ：0.95（0.07）； BJ：0.89（0.14）]，而不是HS [NJ：0.76（0.17）； BJ：0.67（0.11）； p <0.0001]。 F-inf和F-pex的常见预测因素包括开窗行为，气象变量和建筑年龄。此外，空调和到最近的主要道路的距离对F-inf有影响，而F（pex）的预测指标与人类的行为和活动（例如，在户外度过的时间和交通）密切相关。模型分别占F-inf和F-pex方差的35.4％-68.1％（RMSE：0.065-0.101）和41.6％-77.0％（RMSE：0.033-0.103）。通过指示F-inf和F-pex的决定因素，这些模型可以改善环境PM2.5暴露评估并减少暴露错误分类。

著录项

  • 来源
    《Building and Environment》 |2019年第2期|297-304|共8页
  • 作者

    Li Na; Liu Zhe; Li Yunpu; Li Ning; Chartier Ryan; McWilliams Andrea; Chang Junrui; Wang Qin; Wu Yaxi; Xu Chunyu; Xe Dongqun;

  • 作者单位

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

    Nanjing Jiangning Ctr Dis Control & Prevent, Nanjing 211100, Jiangsu, Peoples R China;

    RTI Int, Res Triangle Pk, NC 27709 USA;

    RTI Int, Res Triangle Pk, NC 27709 USA;

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

    Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    PM2.5; Infiltration; Personal exposure; Personal-indoor-outdoor relationship; Seasonal variability; Predictive model;

    机译：PM2.5;渗透;个人接触;人内外关系;季节变异性;预测模型;

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