National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli, 35053, Taiwan.
Department of Environmental Engineering and Health, Yuanpei University of Medical Technology, Hsinchu, 30015, Taiwan.
Environ Pollut. 2020 Aug;263(Pt A):114522. doi: 10.1016/j.envpol.2020.114522. Epub 2020 Apr 10.
While the measurement of particulate matter (PM) with a diameter of less than 2.5 μm (PM) has been conducted for personal exposure assessment, it remains unclear how models that integrate microenvironmental levels with resolved activity and location information predict personal exposure to PM. We comprehensively investigated PM concentrations in various microenvironments and estimated personal exposure stratified by the microenvironment. A variety of microenvironments (>200 places and locations, divided into 23 components according to indoor, outdoor, and transit modes) in a community were selected to characterize PM concentrations. Infiltration factors calculated from microenvironmental/central-site station (M/S) monitoring campaigns with time-activity patterns were used to estimate time-weighted exposure to PM for university students. We evaluated exposures using a four-stage modeling approach and quantified the performance of each component. It was found that the SidePak monitor overestimated the concentration by 3.5 times as compared with the filter-based measurements. Higher mean concentrations of PM were observed in the Taoist temple and night market microenvironments; in contrast, lower concentrations were observed in air-conditioned offices and car microenvironments. While the exposure model incorporating detailed time-location information and infiltration factors achieved the highest prediction (R = 0.49) of personal exposure to PM, the use of indoor, outdoor, and transit components for modeling also generated a consistent result (R = 0.44).
虽然已经进行了测量直径小于 2.5μm 的颗粒物(PM)的个人暴露评估,但将微观环境水平与解析的活动和位置信息相结合的模型如何预测个人 PM 暴露仍不清楚。我们全面调查了各种微观环境中的 PM 浓度,并按微观环境对个人暴露进行分层估计。选择社区中的各种微观环境(>200 个地点和位置,根据室内、室外和过渡模式分为 23 个组件)来描述 PM 浓度。使用带有时间-活动模式的微观环境/中心站点(M/S)监测活动计算的渗透因子来估计大学生对 PM 的时间加权暴露。我们使用四阶段建模方法评估暴露,并量化每个组件的性能。结果发现,与基于过滤器的测量相比,SidePak 监测器高估了浓度 3.5 倍。在道观和夜市微观环境中观察到较高的 PM 平均浓度;相比之下,在空调办公室和汽车微观环境中观察到的浓度较低。虽然纳入详细时间-位置信息和渗透因子的暴露模型对个人 PM 暴露的预测最高(R=0.49),但用于建模的室内、室外和过渡组件也产生了一致的结果(R=0.44)。