We perform a climatology of factors influencing ambient carbon monoxide (CO), in which we examine the relationships between meteorology, traffic patterns, and CO at seasonal, weekly, and diurnal time scales in Phoenix, Arizona. From this analysis we identify a range of potentially important variables for statistical CO modeling. Using stepwise multivariate regression, we create a suite of models for hourly and 8-h ambient CO designed for daily operational forecasting purposes. The resulting models include variables and interaction terms related to anticipated nocturnal atmospheric stability as well as antecedent and climatological CO behavior. The models are evaluated using a range of error statistics and skill measures. The most successful approach employs a two-stage modeling strategy in which an initial prediction is made that may, depending on the forecast value, be followed by a second prediction that improves upon the first. The best models provide accurate daily forecasts of CO, with explained variances approaching 0.9 and errors under 1 ppm.

Original languageEnglish (US)
Pages (from-to)5023-5036
Number of pages14
JournalAtmospheric Environment
Issue number30
StatePublished - Dec 1999


  • Air pollution forecast
  • Climate
  • Inversion
  • Statistical modeling
  • Urban air quality

ASJC Scopus subject areas

  • General Environmental Science
  • Atmospheric Science


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