An optimal spatial sampling design for intra-urban population exposure assessment

Research output: Contribution to journalComment/debate

27 Scopus citations

Abstract

This article offers an optimal spatial sampling design that captures maximum variance with the minimum sample size. The proposed sampling design addresses the weaknesses of the sampling design that Kanaroglou, P.S., M. Jerrett, J. Morrison, B. Beckerman, M.A. Arain, N.L. Gilbert, and J.R. Brook (2005. Establishing an air pollution monitoring network for intra-urban population exposure assessment: a location-allocation approach. Atmospheric Environment 39(13), 2399-409) used for identifying 100 sites for capturing population exposure to NO2 in Toronto, Canada. Their sampling design suffers from a number of weaknesses and fails to capture the spatial variability in NO2 effectively. The demand surface they used is spatially autocorrelated and weighted by the population size, which leads to the selection of redundant sites. The location-allocation model (LAM) available with the commercial software packages, which they used to identify their sample sites, is not designed to solve spatial sampling problems using spatially autocorrelated data. A computer application (written in C++) that utilizes spatial search algorithm was developed to implement the proposed sampling design. The proposed design has already been tested and implemented in three different urban environments - namely Cleveland, OH; Delhi, India; and Iowa City, IA - to identify optimal sample sites for monitoring airborne particulates.

Original languageEnglish (US)
Pages (from-to)1153-1155
Number of pages3
JournalAtmospheric Environment
Volume43
Issue number5
DOIs
StatePublished - Feb 2009
Externally publishedYes

Keywords

  • Intra-city exposure
  • Optimal spatial sampling design
  • Spatial autocorrelation
  • Variance maximization

ASJC Scopus subject areas

  • Atmospheric Science
  • Environmental Science(all)

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