Modeling Ambient Carbon Monoxide Trends to Evaluate Mobile Source Emissions Reductions

Robin L. Dennis Atmospheric Science Research Laboratory, U.S. EPA, Research Triangle Park, NC 27711

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Mary W. Downton The National Center for Atmospheric Research, Boulder, CO 80307

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Abstract

Regression models have been used with poor success to detect the effect of emission control programs in ambient concentration measurements of carbon monoxide. An advanced CO regression model is developed whose form is based on an understanding of the physical processes of dispersion. Its performance is shown to be superior to the more traditionally developed regression and time series models. The model separates the effects of emissions change from the effects of fluctuations in meteorological conditions. The separation appears to be most reliable for winter conditions. The model has sufficient precision to identify present trends in emissions ambient concentration data. This model should be useful for detecting changes in emission trends due to implementation of a control program on vehicular emissions such as an inspection and maintenance program.

Abstract

Regression models have been used with poor success to detect the effect of emission control programs in ambient concentration measurements of carbon monoxide. An advanced CO regression model is developed whose form is based on an understanding of the physical processes of dispersion. Its performance is shown to be superior to the more traditionally developed regression and time series models. The model separates the effects of emissions change from the effects of fluctuations in meteorological conditions. The separation appears to be most reliable for winter conditions. The model has sufficient precision to identify present trends in emissions ambient concentration data. This model should be useful for detecting changes in emission trends due to implementation of a control program on vehicular emissions such as an inspection and maintenance program.

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