Authors: Yoo Min Park*, Geography, Planning & Environment, East Carolina University
Topics: Medical and Health Geography, Geographic Information Science and Systems, Spatial Analysis & Modeling
Keywords: Personal exposure; Traffic-related air pollution, PM2.5, Air dispersion model, Human mobility
Session Type: Paper
Presentation File: No File Uploaded
This study proposes a novel method for measuring personal exposure to traffic-generated PM2.5 by integrating human mobility and high-resolution traffic-related PM2.5 concentration data. Assessing exposure to air pollution from road vehicle emissions and its health risk at a personal level poses several challenges due to the limited availability of a large volume of individual movement pattern data and complexity of modeling the portion of air pollution attributable to traffic sources at a fine spatiotemporal scale. This study develops a method for reconstructing individual movement trajectories from a large volume of travel-activity diaries. It also uses an on-road source air dispersion model and hourly emission data to characterize the spatiotemporal variability of traffic-related PM2.5 concentrations at a fine scale. The integration of the movement data and high-resolution air pollution data reveals that there are within- and between-individual variations in traffic-related exposure over the course of a day. It also finds that being in transit contributes 7.8% of the total daily exposure to traffic-related PM2.5 although the time fraction of day spent in transit is relatively small (4.5%). It indicates people may encounter peak exposure during transport, which can have significant health impacts. This study highlights the significance of characterizing both dynamic movement patterns of individuals and spatiotemporal variability of traffic-related air pollution concentrations for accurate assessments of traffic-related exposure and health risks.
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