An Optimal Spatial Sampling Design for Intra-Urban Population Exposure Assessment.

Atmospheric Environment
Naresh Kumar

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 et al. (2005) 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. This design was implemented in three different urban environments - namely Cleveland, OH; Delhi, India; and Iowa City, IA - to identify optimal sample sites for monitoring airborne particulates.

References


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Citations

Feb 9, 2010·Environmental Monitoring and Assessment·Farhad NejadkoorkiKamal Hadad
Jan 16, 2013·PloS One·Jin-Feng WangYu Zhao
Dec 2, 2009·Journal of the Air & Waste Management Association·Naresh KumarThomas Peters
Mar 22, 2011·Journal of the Air & Waste Management Association·Charles L BlanchardNehzat Motallebi
Oct 2, 2012·The Science of the Total Environment·Hendrik MerbitzChristoph Schneider
Mar 22, 2018·Annals of Work Exposures and Health·Jesse D BermanKirsten A Koehler
Aug 18, 2020·Environmental Pollution·Marta Doval MiñarroAntonia Baeza Caracena
Apr 14, 2010·Environmental Science & Technology·Carolyn PersoonKeri C Hornbuckle

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