The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5

Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

Bibliographic Data

ID17800949
AuthorsEunhye Yoo (0000-0001-5057-2187, University at Buffalo, State University of New York, corresponding author), Qiang Pu (0000-0002-5446-3268, University at Buffalo, State University of New York), Youngseob Eum (0000-0002-3791-8953, University at Buffalo, State University of New York), Xiangyu Jiang (0000-0002-6620-4984, Georgia Department of Natural Resources)
Year2021
Volume18
Issue4
Pages2194-2194
Publication date2021-02-23
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph18042194
PMID33672290
OpenAlexW3132854740
LanguageEN
Citations received7
References cited14

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5

Atmospheric sciences · Effect modification · Exposure assessment · Meta-analysis · Moderation · Random effects model · Statistics · Air Quality and Health Impacts · Environmental Science · Impact of Light on Environment and Health · Mathematics · Medicine · Urban Transport and Accessibility

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