Determining the spatial scale for analysing mobile measurements of air pollution

Christy Lightowlers, Trisalyn Nelson, Eleanor Setton, C. Peter Keller

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

When dealing with spatial data or modelling in a geographical context, identifying an appropriate scale for analysis is a critical precursor; however, it is difficult to determine due to limited availability of data at an adequate spatial resolution. This paper describes a mobile monitoring method to collect spatially representative measurements of woodsmoke particulates in support of spatial modelling. A geostatistical technique is described to characterize the spatial scale of woodsmoke particulates collected for 19 evenings over two heating seasons in Victoria, British Columbia, Canada. Semivariograms were applied to 20 data sets (19 evenings and a combined data set) to characterize the appropriate spatial-analysis scale as defined by the semivariogram range, the maximum distance of spatial dependence. Typically, the semivariogram range occurred at 2673 m. This method can be used to identify an optimal sampling interval for woodsmoke data collection, to define the neighbourhood size for performing spatial analyses, and to produce robust model variables and parameters by characterizing the degree of spatial autocorrelation in the data set.

Original languageEnglish (US)
Pages (from-to)5933-5937
Number of pages5
JournalAtmospheric Environment
Volume42
Issue number23
DOIs
StatePublished - Jul 1 2008
Externally publishedYes

Keywords

  • Air pollution
  • Mobile monitoring
  • Semivariogram
  • Spatial autocorrelation
  • Spatial scale

ASJC Scopus subject areas

  • Environmental Science(all)
  • Atmospheric Science

Fingerprint Dive into the research topics of 'Determining the spatial scale for analysing mobile measurements of air pollution'. Together they form a unique fingerprint.

  • Cite this