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.

Charles Yorke,Travis Losser,Lixin Li,Reinhard Piltner

doi:10.3390/ijerph110909101

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.

Highlights

The goal of this paper is to develop and implement a fast inverse distance weighting (IDW)-based spatiotemporal interpolation method to estimate the daily PM2.5 concentration values in 2009 at the centroids of counties and census block groups for the entire contiguous U.S, using the existing PM2.5 measurements as input
The inverse distance function can be considered a special case of a radial basis function
Spatiotemporal interpolation is necessary in this study, because PM2.5 concentrations are measured only at certain locations and time instances by monitoring stations

Summary

Introduction

Since the beginning of the nineteenth century, the human population has been increasing at an alarming rate. Do human needs and demands for various resources for their survival. Demand for food, potable water, clean air and energy, as well as the demand for habitable land are increasing. Population increase requires more land for liquid and solid waste disposal [1]. The need for more land for liquid and solid waste disposal leads to an increase in the amounts of pollutants in our environment that affect the health of more and more people, including elderly and children [2,3,4]. The health effects of pollutants have been subject to intense study in recent years. This paper focuses on monitoring the trend of daily air pollution using fine particulate air pollutant (PM2.5 ) concentration in the contiguous United States

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Conclusion