While the measurement of particulate matter (PM) with a diameter of less than 2.5 μm (PM2.5) 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 (R2 = 0.49) of personal exposure to PM2.5, the use of indoor, outdoor, and transit components for modeling also generated a consistent result (R2 = 0.44).
Keywords: Exposure assessment; Fine particle; Microenvironment; Model validation; Time-activity pattern.
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