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Geospatial Estimation of Individual Exposure to Air Pollutants: Moving from Static Monitoring to Activity-Based Dynamic Exposure Assessment

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  • EunHye Yoo
  • C. Rudra
  • M. Glasgow
  • L. Mu

Abstract

Spatiotemporal variability of air pollutant concentrations and individuals' mobility are likely to play an important role in health outcomes and, therefore, time–activity-based exposure assessments are likely to be more sensitive compared to static residence-based air pollution estimates. Applied research on the effects of the variability underlying air pollutant concentrations and individuals' mobility on personal exposure estimates remain limited, however. We demonstrate how consideration of individuals' mobility and the spatiotemporal variability of ambient air pollution affect personal exposure estimates using both real-world data and simulated environmental conditions. Our findings suggest that time–activity-based exposure estimates might be quite similar to static estimates if spatiotemporal patterns of air pollution concentration surfaces lack autocorrelation or if an individual has a low level of mobility. There can be substantial differences, though, between two approaches when the air pollution concentrations are characterized by a model of air pollution that shows low variation over time and space and individuals' time spent away from home is substantial.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:raagxx:v:105:y:2015:i:5:p:915-926
    DOI: 10.1080/00045608.2015.1054253
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    Citations

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    Cited by:

    1. 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.
    2. Mingxiao Li & Song Gao & Feng Lu & Huan Tong & Hengcai Zhang, 2019. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data," IJERPH, MDPI, vol. 16(22), pages 1-20, November.
    3. Jeremy Mennis & Michael Mason & Donna L. Coffman & Kevin Henry, 2018. "Geographic Imputation of Missing Activity Space Data from Ecological Momentary Assessment (EMA) GPS Positions," IJERPH, MDPI, vol. 15(12), pages 1-15, December.
    4. Huagui Guo & Weifeng Li & Jiansheng Wu, 2020. "Ambient PM2.5 and Annual Lung Cancer Incidence: A Nationwide Study in 295 Chinese Counties," IJERPH, MDPI, vol. 17(5), pages 1-18, February.
    5. Mirjam Schindler & Geoffrey Caruso, 2020. "Emerging urban form – Emerging pollution: Modelling endogenous health and environmental effects of traffic on residential choice," Environment and Planning B, , vol. 47(3), pages 437-456, March.
    6. Yuan Shi & Edward Ng, 2017. "Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong," IJERPH, MDPI, vol. 14(9), pages 1-16, September.

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