A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Abstract

Outdoor concentrations of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) are often used as a surrogate for population exposure to PM2.5 in epidemiological studies. However, people spend most of their daily activities indoors; therefore, the relationship between indoor and outdoor PM2.5 concentrations should be considered in the estimation of population exposure to PM2.5. In this study, a population exposure model was developed to predict seasonal population exposure to PM2.5 in Seoul, Korea. The input data for the population exposure model comprised 3984 time-location patterns, outdoor PM2.5 concentrations, and the microenvironment-to-outdoor PM2.5 concentrations in seven microenvironments. A probabilistic approach was used to develop the Korea simulation exposure model. The determinants for the population exposure group were identified using a multinomial logistic regression analysis. Population exposure to PM2.5 varied significantly among the three seasons (p < 0.01). The mean ± standard deviation of population exposures to PM2.5 was 21.3 ± 4.0 μg/m3 in summer, 9.8 ± 2.7 μg/m3 in autumn, and 29.9 ± 10.6 μg/m3 in winter. Exposure to PM2.5 higher than 35 μg/m3 mainly occurred in winter. Gender, age, working hours, and health condition were identified as significant determinants in the exposure groups. An “unhealthy” health condition was the most significant determinant. The high PM2.5 exposure group was characterized as a higher proportion of males of a lower age with longer working hours. The population exposure model for PM2.5 could be used to implement effective interventions and evaluate the effectiveness of control policies to reduce exposure.