Evaluation of RAM Model for Cleveland, Ohio, Part II

Hodanbosi, Robert F.; Brodkey, Robert S.

doi:10.1080/00022470.1984.10465758

Journal of the Air Pollution Control Association

1984

DOI: 10.1080/00022470.1984.10465758

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Evaluation of RAM Model for Cleveland, Ohio, Part II

Robert F. Hodanbosi

Robert S. Brodkey

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1985

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Cited by 3 publications

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Evaluation of the Industrial Source Complex short-term model in a large-scale multiple source region for different stability classes

Riswadkar

Kumar

1994

Environ Monit Assess

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The original Industrial Source Complex Model (ISCST, 90346) and its latest version, the ISCST2 (92090), are evaluated for the 1- and 24-hour averaging periods, using six statistical parameters. The confidence limits on two parameters were obtained using the bootstrap, jacknife, seductive, and robust resampling techniques. Evaluation is conducted in a multiple point source environment and in all the different stability categories, using a year of meteorological data, emission inventory of the Lucas County, in Toledo, Ohio, and monitoring data from two nearby stations for the year 1987. This was representative of a real time situation in which the ISCST is generally applied for regulatory work. The sensitivity analysis shows that the ISC is a poor performing model in the 1-h and 24-h averaging period, neutral and stable categories, and modifications to it are necessary in order to improve its performance. Its performance changes depending on the chosen paired values. A better relative performance is observed in the unstable category relative to the neutral and stable categories. Improved model performance may be achieved by applying modifications to the physics on which the model is based.

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Evaluation of the Industrial Source Complex short-term model in a large-scale multiple source region for different stability classes

Riswadkar

Kumar

1994

Environ Monit Assess

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Comparison of RAM model estimates with 1976 St. Louis RAPS measurements of sulfur dioxide

Turner

Irwin

Busse

1985

Atmospheric Environment (1967)

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An Adaptive Filtering Procedure for Estimating Regression Quantiles

Gorr

Hsu

1985

Management Science

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Applications of reliability theory and some forms of chance-constrained programming need real-time, nonstationary estimates of regression quantiles to trigger preventive actions, thereby avoiding undesirable system states. We have designed the Quantile Estimation Procedure (QEP) for this purpose. QEP is a new adaptive filter that nonparametrica11y estimates time-varying parameters of multivariate regression quantiles. Results of Monte Carlo tests show that QEP provides accurate estimates for a range of stochastic processes. Falling within this range is the case study of this paper on monitoring compliance with short-term air quality standards.forecasting/time series

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Evaluation of RAM Model for Cleveland, Ohio, Part II

Cited by 3 publications

References 4 publications

Evaluation of the Industrial Source Complex short-term model in a large-scale multiple source region for different stability classes

Evaluation of the Industrial Source Complex short-term model in a large-scale multiple source region for different stability classes

Comparison of RAM model estimates with 1976 St. Louis RAPS measurements of sulfur dioxide

An Adaptive Filtering Procedure for Estimating Regression Quantiles

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