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 NO
2 in Toronto, Canada. Their sampling design suffers from a number of weaknesses and fails to capture the spatial variability in NO
2 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.
