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Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application

Author

Listed:
  • Lixin Li

    (Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA)

  • Xiaolu Zhou

    (Department of Geology and Geography, Georgia Southern University, Statesboro, GA 30460, USA)

  • Marc Kalo

    (Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA)

  • Reinhard Piltner

    (Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USA)

Abstract
Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM 2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM 2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM 2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM 2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)’s AirNow program.

Suggested Citation

  • Lixin Li & Xiaolu Zhou & Marc Kalo & Reinhard Piltner, 2016. "Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application," IJERPH, MDPI, vol. 13(8), pages 1-20, July.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:8:p:749-:d:74652
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    References listed on IDEAS

    as
    1. Lixin Li & Travis Losser & Charles Yorke & Reinhard Piltner, 2014. "Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM 2.5 in the Contiguous U.S. Using Parallel Programming and ," IJERPH, MDPI, vol. 11(9), pages 1-41, September.
    2. Louis de Mesnard, 2013. "Pollution models and inverse distance weighting: some critical remarks," Post-Print hal-00778417, HAL.
    3. Pebesma, Edzer, 2012. "spacetime: Spatio-Temporal Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i07).
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    Cited by:

    1. Daniel Dunea & Stefania Iordache & Alin Pohoata, 2016. "Fine Particulate Matter in Urban Environments: A Trigger of Respiratory Symptoms in Sensitive Children," IJERPH, MDPI, vol. 13(12), pages 1-18, December.
    2. Mohammad Hashem Askariyeh & Suriya Vallamsundar & Josias Zietsman & Tara Ramani, 2019. "Assessment of Traffic-Related Air Pollution: Case Study of Pregnant Women in South Texas," IJERPH, MDPI, vol. 16(13), pages 1-19, July.

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