Air Pollution |

[Under Review] Impact of Temporally-Varying Geographic Predictors on Long-Term Air Pollution Exposure Estimation for Epidemiology

Sat, 01 Nov 2025 00:00:00 +0000

Personal Exposure using GPS and Time-Activity Diary

Fri, 01 Aug 2025 00:00:00 +0000

Summary

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.

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.

To fill these research gaps, our study applies an integrated approach combining time-activity diaries and GPS data to investigate personal exposure dynamics.

(1) Time-Activity Diary: 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 Activity–Place–Transport (APT) 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.

(2) GPS-Based Mobility Component: In parallel, we used one-second GPS data to construct 20 quantitative mobility indices representing multiple dimensions of individual mobility (extent, activity, timing, stability, and elongation). These indices were summarized and analyzed using Partial Least Squares (PLS) 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.

[Manuscript in Preparation] Analyzing Personal PM2.5 Exposure Through APT (Activity-Place-Transport) variables: Integration of Time Activity Patterns, Place, and Transportation

Tue, 01 Oct 2024 00:00:00 +0000

[Manuscript in Preparation] Correlation of Air Pollution and Traffic Volume: Comparison between Road Density and Traffic Volume Estimates

Thu, 01 Sep 2022 00:00:00 +0000

Air Pollution Predictive Modeling

Sun, 27 Dec 2020 00:00:00 +0000

Summary

This project aims to predict air pollution concentrations at locations across South Korea where direct measurements are unavailable. Using the Universal Kriging (UK) model, we estimate the annual concentrations of key air pollutants—PM10, PM2.5, O3, and NO2. The UK model leverages observed pollutant concentrations at monitoring sites and incorporates spatial and environmental characteristics represented by geographic variables, along with spatial autocorrelation, to predict concentrations at unmonitored locations. The observational data consists of annual air pollution measurements collected from air quality monitoring stations nationwide. Additionally, the model integrates 320 geographic variables, derived from spatial data processing pipelines, which capture factors related to air pollution levels, such as road networks, elevation, population density, land use, and vegetation index (NDVI). To manage the high dimensionality of these variables, we employ Partial Least Squares (PLS) regression to reduce the dataset to 2–3 primary predictive components, which serve as inputs for the UK model. This dimensionality reduction optimizes model performance by focusing on the most relevant predictors. In many cases, our team utilizes this model to estimate air pollution exposure for cohort study participants based on their residential locations. This process involves an initial geocoding step, where participants’ address information is converted into geographic coordinates. Subsequently, approximately 320 geographic variables are calculated for each residential location. These variables are then reduced to key components via PLS, which are used as inputs in the UK model to estimate pollutant concentrations for individual residences. Through this project, we enhance the precision of individual exposure assessments by estimating air pollution concentrations directly at individuals’ residential locations rather than relying on the concentrations from nearby monitoring sites. This approach enables more accurate evaluations of personal exposure and contributes to more precise health effect assessments related to air pollution exposure.