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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 k-d Tree

Author

Listed:
  • Lixin Li

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

  • Travis Losser

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

  • Charles Yorke

    (Department of Geosciences, Murray State University, Murray, KY 42071, USA)

  • Reinhard Piltner

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

Abstract

Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM 2.5 exposure risk and monitoring day-to-day changes in PM 2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM 2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM 2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:9:p:9101-9141:d:39898
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    References listed on IDEAS

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    1. Louis de Mesnard, 2013. "Pollution models and inverse distance weighting: some critical remarks," Post-Print hal-00778417, HAL.
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    Cited by:

    1. 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.
    2. Keith April G. Arano & Shengjing Sun & Joaquin Ordieres-Mere & and Bing Gong, 2019. "The Use of the Internet of Things for Estimating Personal Pollution Exposure," IJERPH, MDPI, vol. 16(17), pages 1-25, August.
    3. Lee, Donghyun & Kim, Mingyu & Lee, Beomhui & Chae, Sangwon & Kwon, Sungjun & Kang, Sungwon, 2022. "Integrated explainable deep learning prediction of harmful algal blooms," Technological Forecasting and Social Change, Elsevier, vol. 185(C).

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