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The Impact of Individual Mobility on Long-Term Exposure to Ambient PM 2.5 : Assessing Effect Modification by Travel Patterns and Spatial Variability of PM 2.5

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  • Eun-hye Yoo

    (Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA)

  • Qiang Pu

    (Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA)

  • Youngseob Eum

    (Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA)

  • Xiangyu Jiang

    (Georgia Environmental Protection Division, Atlanta, GA 30354, USA)

Abstract

The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM 2.5 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 PM 2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 .

Suggested Citation

  • Eun-hye Yoo & Qiang Pu & Youngseob Eum & Xiangyu Jiang, 2021. "The Impact of Individual Mobility on Long-Term Exposure to Ambient PM 2.5 : Assessing Effect Modification by Travel Patterns and Spatial Variability of PM 2.5," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:2194-:d:504430
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    References listed on IDEAS

    as
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    2. Mei-Po Kwan, 2018. "The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect," IJERPH, MDPI, vol. 15(9), pages 1-4, August.
    3. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    4. Francesco Finazzi & Lucia Paci, 2019. "Quantifying personal exposure to air pollution from smartphone‐based location data," Biometrics, The International Biometric Society, vol. 75(4), pages 1356-1366, December.
    5. EunHye Yoo & C. Rudra & M. Glasgow & L. Mu, 2015. "Geospatial Estimation of Individual Exposure to Air Pollutants: Moving from Static Monitoring to Activity-Based Dynamic Exposure Assessment," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(5), pages 915-926, September.
    6. Bin Chen & Yimeng Song & Tingting Jiang & Ziyue Chen & Bo Huang & Bing Xu, 2018. "Real-Time Estimation of Population Exposure to PM 2.5 Using Mobile- and Station-Based Big Data," IJERPH, MDPI, vol. 15(4), pages 1-14, March.
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