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Int. J. Environ. Res. Public Health 2014, 11(9), 9101-9141; doi:10.3390/ijerph110909101

Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree

1
Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA
2
Department of Geosciences, Murray State University, Murray, KY 42071, USA
3
Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USA
*
Author to whom correspondence should be addressed.
Received: 4 March 2014 / Revised: 18 August 2014 / Accepted: 18 August 2014 / Published: 3 September 2014

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 PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 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 PM2.5 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, PM2.5 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. View Full-Text
Keywords: fine particulate matter PM2.5; spatiotemporal interpolation; inverse distance weighting (IDW); parallel programming; k-d tree; leave-one-out cross-validation; k-fold cross validation; web-based application; visualization fine particulate matter PM2.5; spatiotemporal interpolation; inverse distance weighting (IDW); parallel programming; k-d tree; leave-one-out cross-validation; k-fold cross validation; web-based application; visualization
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Li, L.; Losser, T.; Yorke, C.; Piltner, R. Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree. Int. J. Environ. Res. Public Health 2014, 11, 9101-9141.

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