Assessment of PM2.5 population exposure of a community using sensor-based air monitoring instruments and similar time-activity groups

Abstract

Although fixed ambient air monitoring stations provide data regarding ambient PM2.5 concentrations within a given community, they present limitations in terms of assessing the actual exposure to individuals and populations using time-activity patterns. The population exposure of a community may be estimated by classifying the population according to the time-activity pattern and modeling their exposure. In this study, we provide a possible methodology to assess the population exposure to PM2.5 in a given community. Five field technicians conducted exposure simulations for similar time-activity groups of preschool children, school students, homemakers, office workers, and elderly, with PM2.5 personal exposure monitoring (PEM) equipment, in Guro-gu, Seoul, Korea. The PM2.5 exposure concentrations were modeled by interpolation (point in polygon, inverse distance weighted, and ordinary kriging methods) and regression models using GPS data and a sensor-based air monitoring instrument network, and were compared with the PEM data. The population exposure to PM2.5 was estimated using the population-weighted average through Monte–Carlo simulation. Elderly presented the highest average PM2.5 exposure concentration followed by office workers, homemakers, preschool children, and school students. The correlation between the measured and modeled exposure was good, in the order of ordinary kriging (R2 = 0.822), inverse distance weighted (R2 = 0.747), and point in polygon methods (R2 = 0.721). Thirty-seven percent of the population in a community was exposed to PM2.5 concentrations higher than 35 μg/m3, which is the Korean Atmospheric Environmental Standard for PM2.5 (24-h average). It is suggested that this methodology could be applied to assess the real-time and long-term cumulative exposures of a given community. It is expected that an exposure surveillance system can be developed based on these results.