A multi-variate statistical model integrating passive sampler and meteorology data to predict the frequency distributions of hourly ambient ozone (O3) concentrations

S. Krupa, M. Nosal, J. A. Ferdinand, R. E. Stevenson, J. M. Skelly

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

A multi-variate, non-linear statistical model is described to simulate passive O3 sampler data to mimic the hourly frequency distributions of continuous measurements using climatologic O3 indicators and passive sampler measurements. The main meteorological parameters identified by the model were, air temperature, relative humidity, solar radiation and wind speed, although other parameters were also considered. Together, air temperature, relative humidity and passive sampler data by themselves could explain 62.5-67.5% (R2) of the corresponding variability of the continuously measured O3 data. The final correlation coefficients (r) between the predicted hourly O3 concentrations from the passive sampler data and the true, continuous measurements were 0.819-0.854, with an accuracy of 92-94% for the predictive capability. With the addition of soil moisture data, the model can lead to the first order approximation of atmospheric O3 flux and plant stomatal uptake. Additionally, if such data are coupled to multi-point plant response measurements, meaningful cause-effect relationships can be derived in the future.

Original languageEnglish (US)
Pages (from-to)173-178
Number of pages6
JournalEnvironmental Pollution
Volume124
Issue number1
DOIs
StatePublished - Jul 1 2003

Fingerprint

Meteorology
Ozone
Statistical Models
Humidity
Air
Temperature
Nonlinear Dynamics
Atmospheric humidity
Soil
Radiation
Solar wind
Soil moisture
Solar radiation
Fluxes

All Science Journal Classification (ASJC) codes

  • Toxicology
  • Pollution
  • Health, Toxicology and Mutagenesis

Cite this

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abstract = "A multi-variate, non-linear statistical model is described to simulate passive O3 sampler data to mimic the hourly frequency distributions of continuous measurements using climatologic O3 indicators and passive sampler measurements. The main meteorological parameters identified by the model were, air temperature, relative humidity, solar radiation and wind speed, although other parameters were also considered. Together, air temperature, relative humidity and passive sampler data by themselves could explain 62.5-67.5{\%} (R2) of the corresponding variability of the continuously measured O3 data. The final correlation coefficients (r) between the predicted hourly O3 concentrations from the passive sampler data and the true, continuous measurements were 0.819-0.854, with an accuracy of 92-94{\%} for the predictive capability. With the addition of soil moisture data, the model can lead to the first order approximation of atmospheric O3 flux and plant stomatal uptake. Additionally, if such data are coupled to multi-point plant response measurements, meaningful cause-effect relationships can be derived in the future.",
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A multi-variate statistical model integrating passive sampler and meteorology data to predict the frequency distributions of hourly ambient ozone (O3) concentrations. / Krupa, S.; Nosal, M.; Ferdinand, J. A.; Stevenson, R. E.; Skelly, J. M.

In: Environmental Pollution, Vol. 124, No. 1, 01.07.2003, p. 173-178.

Research output: Contribution to journalArticle

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AU - Skelly, J. M.

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