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Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants
Abstract Two related time series models have been developed to forecast concentrations of various air pollutants and have been tested on carbon monoxide and oxidant data for the Los Angeles basin. One model forecasts daily maximum concentrations of a particular pollutant using only past daily maximum values of that pollutant as input. The other model forecasts 1-h average concentrations using only the past hourly average values. Both are found to be significantly more accurate than persistence, i.e. forecasting for tomorrow what occurred today (or yesterday). Model forecasts for 1972 of the daily instantaneous maxima for total oxidant made using only past pollutant concentration data were found to be somewhat more accurate than those made by the Los Angeles APCD using meteorological input as well as pollutant concentrations. Although none of these models forecast as accurately as might be desired for a health warning system, the relative success of simple time series models, even though based solely on pollutant concentration, suggests that models incorporating meteorological data and using either multi-dimensional times series or pattern recognition techniques should be tested.
Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants
Abstract Two related time series models have been developed to forecast concentrations of various air pollutants and have been tested on carbon monoxide and oxidant data for the Los Angeles basin. One model forecasts daily maximum concentrations of a particular pollutant using only past daily maximum values of that pollutant as input. The other model forecasts 1-h average concentrations using only the past hourly average values. Both are found to be significantly more accurate than persistence, i.e. forecasting for tomorrow what occurred today (or yesterday). Model forecasts for 1972 of the daily instantaneous maxima for total oxidant made using only past pollutant concentration data were found to be somewhat more accurate than those made by the Los Angeles APCD using meteorological input as well as pollutant concentrations. Although none of these models forecast as accurately as might be desired for a health warning system, the relative success of simple time series models, even though based solely on pollutant concentration, suggests that models incorporating meteorological data and using either multi-dimensional times series or pattern recognition techniques should be tested.
Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants
McCollister, George M. (author) / Wilson, Kent R. (author)
Atmospheric Environment ; 9 ; 417-423
1974-10-08
7 pages
Article (Journal)
Electronic Resource
English
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