Predictive mapping of air pollution involving sparse spatial observations

Jeremy E. Diem, Andrew C. Comrie

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

A limited number of sample points greatly reduces the availability of appropriate spatial interpolation methods. This is a common problem when one attempts to accurately predict air pollution levels across a metropolitan area. Using ground-level ozone concentrations in the Tucson, Arizona, region as an example, this paper discusses the above problem and its solution, which involves the use of linear regression. A large range of temporal variability is used to compensate for sparse spatial observations (i.e. few ozone monitors). Gridded estimates of emissions of ozone precursor chemicals, which are developed, stored, and manipulated within a geographic information system, are the core predictor variables in multiple linear regression models. Cross-validation of the pooled models reveals an overall R2 of 0.90 and approximately 7% error. Composite ozone maps predict that the highest ozone concentrations occur in a monitor-less area on the eastern edge of Tucson. The maps also reveal the need for ozone monitors in industrialized areas and in rural, forested areas.

Original languageEnglish (US)
Pages (from-to)99-117
Number of pages19
JournalEnvironmental Pollution
Volume119
Issue number1
DOIs
StatePublished - Mar 20 2002

Keywords

  • Air pollution
  • GIS
  • Linear regression
  • Mapping
  • Ozone

ASJC Scopus subject areas

  • Toxicology
  • Pollution
  • Health, Toxicology and Mutagenesis

Fingerprint Dive into the research topics of 'Predictive mapping of air pollution involving sparse spatial observations'. Together they form a unique fingerprint.

  • Cite this