Next Article in Journal
Potential Harmful Effects of PM2.5 on Occurrence and Progression of Acute Coronary Syndrome: Epidemiology, Mechanisms, and Prevention Measures
Previous Article in Journal
Socio-demographic Differences in Toxic Release Inventory Siting and Emissions in Metro Atlanta
Article Menu

Export Article

Open AccessArticle
Int. J. Environ. Res. Public Health 2016, 13(8), 749; doi:10.3390/ijerph13080749

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

1
Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA
2
Department of Geology and Geography, Georgia Southern University, Statesboro, GA 30460, USA
3
Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Kim Natasha Dirks
Received: 28 March 2016 / Revised: 16 June 2016 / Accepted: 4 July 2016 / Published: 25 July 2016
View Full-Text   |   Download PDF [12520 KB, uploaded 25 July 2016]   |  

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 PM2.5 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. PM2.5 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 PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 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. View Full-Text
Keywords: fine particulate matter (PM2.5); spatiotemporal interpolation; shape function; Inverse Distance Weighting (IDW); cross validation; population exposure; web application; visualization; real-time air pollution fine particulate matter (PM2.5); spatiotemporal interpolation; shape function; Inverse Distance Weighting (IDW); cross validation; population exposure; web application; visualization; real-time air pollution
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, L.; Zhou, X.; Kalo, M.; Piltner, R. 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. Int. J. Environ. Res. Public Health 2016, 13, 749.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top