/tags/individual-exposure/Individual ExposureSource Themes Academic (https://sourcethemes.com/academic/)en-usSat, 01 Nov 2025 00:00:00 +0000/img/my.jpg/tags/individual-exposure//publication/timevaring/Sat, 01 Nov 2025 00:00:00 +0000/publication/timevaring//project/individual_pm25/Fri, 01 Aug 2025 00:00:00 +0000/project/individual_pm25/<h2 id="summary">Summary</h2> <p><img src="/project/individual_pm25/featured.png"> <img src="/project/individual_pm25/featured2.png"> <img src="/project/individual_pm25/featured3.png"> <img src="/project/individual_pm25/featured4.png"></p> <p>This project aims to develop a methodology for assessing and predicting individual exposure to fine particulate matter (PM2.5) across outdoor, indoor, and personal environments, to better evaluate the health impacts of PM2.5 on elderly populations within cohorts (KoGES, KFACS). Individual PM2.5 exposure is categorized into outdoor, indoor, and personal exposure, each with distinct sources and spatiotemporal characteristics. Correlations among these domains have been reported as relatively low, indicating the need for independent yet integrative approaches.</p> <p>Sampling was conducted for approximately 60 older adults across various households, and PM2.5 concentrations were measured in outdoor, indoor, and personal environments over four seasons using RTI’s MicroPEM device with gravimetric corrections. Participants carried the device for about five days per season and recorded hourly information on activities, visited locations, and transportation use. A GPS logger was attached to the MicroPEM to track movement trajectories.</p> <p>To fill these research gaps, our study applies an integrated approach combining time-activity diaries and GPS data to investigate personal exposure dynamics.</p> <p><strong>(1) Time-Activity Diary:</strong> We analyzed time-activity patterns associated with personal PM2.5 exposure among 68 older adults in South Korea who provided hourly monitoring data over five days, including weekdays and weekends, across two seasons. To quantify behavioral context, we developed a single integrated metric, the <em>Activity–Place–Transport (APT)</em> index, which classifies each hour into eight APT categories. Using this metric, we examined how time spent in each APT class is associated with changes in personal PM2.5 exposure, and whether these relationships vary by season and day of the week.</p> <p><strong>(2) GPS-Based Mobility Component:</strong> In parallel, we used one-second GPS data to construct 20 quantitative <em>mobility indices</em> representing multiple dimensions of individual mobility (extent, activity, timing, stability, and elongation). These indices were summarized and analyzed using <em>Partial Least Squares (PLS)</em> regression to identify latent mobility components and assess how personal PM2.5 exposure varies according to life-space extent, movement regularity, and activity intensity. Our findings suggest that personal exposure tends to increase with broader life-space and higher activity mobility, up to certain thresholds where it plateaus or declines.</p>/publication/apt/Tue, 01 Oct 2024 00:00:00 +0000/publication/apt/