Assessment of personal exposure using movement trajectory and hourly 1-km PM2.5 concentrations

Abstract

Most health studies have used residential addresses to assess personal exposure to air pollution. These exposure assessments may suffer from bias due to not considering individual movement. Here, we collected 45,600 hourly movement trajectory data points for 185 individuals in Nanjing from COVID-19 epidemiological surveys. We developed a fusion algorithm to produce hourly 1-km PM2.5 concentrations, with a good performance for out-of-station cross validation (correlation coefficient of 0.89, root-mean-square error of 5.60 μg / m3, and mean absolute error (MAE) of 4.04 μg / m3). Based on these PM2.5 concentrations and location data, PM2.5 exposures considering individual movement were calculated and further compared with residence-based exposures. Our results showed that daily residence-based exposures had an MAE of 0.19 μg / m3 and were underestimated by <1 % overall. For hourly residence-based exposures, the MAE exhibited a diurnal variation: it decreased from 0.58 μg / m3 at 09:00 to 0.44 μg / m3 at 12:00 and then continuously increased to 0.74 μg / m3 at 17:00. The biases also depended on activity types and distances from home to activity locations. Specifically, the largest MAE (3.86 μg / m3) occurred in visits that were among the top four types of activity other than being at home. As distances changed from <10 to >30 km, the degree of underestimation for hourly residence-based exposures increased from 1% to 6%. This trend was more obvious for work activities, suggesting that personal exposure assessments should consider individual movement for work cases with long commuting distances.