Search Results
You are looking at 1 - 4 of 4 items for :
- Author or Editor: Robin L. Dennis x
- Article x
- Refine by Access: All Content x
Abstract
The development and use of a version of the Regional Acid Deposition Model/Engineering Model (RADM/FM) called the Comprehensive Sulfate Tracking Model (COMSTM) is reported. The COMSTM is used to diagnose the relative contributions of each sulfate production pathway to the total atmospheric ambient sulfate predicted by RADM. Thirty meteorological cases are used to aggregate the results into annual estimates. For the operational RADM (denoted RADM 2.6), nonprecipitating cloud production of ambient sulfate dominates over precipitating cloud production, and the hydrogen peroxide pathway dominates over four other aqueous formation routes. Gas-phase production of sulfate contributes ten than 40% of the ambient budget. Further, the COMSTM is used to explore the sensitivity of the RADM's cloud water and rainwater pH's and ambient sulfate predictions to uncertainties in the ammonia emissions inventory. By developing correction floors to improve in-cloud and deposited ammonia, and utilizing them in the COMSTM, it is shown that the uncertainties should have a minimal effect on predicted cloud water and rainwater pH's and on the overall ambient sulfate budget in the operational PADM 2.6.
Abstract
The development and use of a version of the Regional Acid Deposition Model/Engineering Model (RADM/FM) called the Comprehensive Sulfate Tracking Model (COMSTM) is reported. The COMSTM is used to diagnose the relative contributions of each sulfate production pathway to the total atmospheric ambient sulfate predicted by RADM. Thirty meteorological cases are used to aggregate the results into annual estimates. For the operational RADM (denoted RADM 2.6), nonprecipitating cloud production of ambient sulfate dominates over precipitating cloud production, and the hydrogen peroxide pathway dominates over four other aqueous formation routes. Gas-phase production of sulfate contributes ten than 40% of the ambient budget. Further, the COMSTM is used to explore the sensitivity of the RADM's cloud water and rainwater pH's and ambient sulfate predictions to uncertainties in the ammonia emissions inventory. By developing correction floors to improve in-cloud and deposited ammonia, and utilizing them in the COMSTM, it is shown that the uncertainties should have a minimal effect on predicted cloud water and rainwater pH's and on the overall ambient sulfate budget in the operational PADM 2.6.
Abstract
Statistical measures for evaluating the performance of urban air quality models have recently been strongly recommended by several investigators. Problems that were encountered in the use of recommended performance measures in an evaluation of three versions of an urban photochemical model are described. The example demonstrates the importance of designing an evaluation to take into account the way in which the model will be used in regulatory practice, and then choosing performance measures on the basis of that design. The evaluation illustrates some limitations and possible pitfalls in the use and interpretation of statistical measures of model performance. Drawing on this experience, a procedure for evaluation of air quality models for regulatory use is suggested.
Abstract
Statistical measures for evaluating the performance of urban air quality models have recently been strongly recommended by several investigators. Problems that were encountered in the use of recommended performance measures in an evaluation of three versions of an urban photochemical model are described. The example demonstrates the importance of designing an evaluation to take into account the way in which the model will be used in regulatory practice, and then choosing performance measures on the basis of that design. The evaluation illustrates some limitations and possible pitfalls in the use and interpretation of statistical measures of model performance. Drawing on this experience, a procedure for evaluation of air quality models for regulatory use is suggested.
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.
Abstract
The development of an episode selection and aggregation approach, designed to support distributional estimation for use with the Models-3 Community Multiscale Air Quality (CMAQ) model, is described. The approach utilized cluster analysis of the 700-hPa east–west and north–south wind field components over the time period of 1984–92 to define homogeneous meteorological clusters. Alternative schemes were compared using relative efficiencies and meteorological considerations. An optimal scheme was defined to include 20 clusters (five per season), and a stratified sample of 40 events was selected from the 20 clusters using a systematic sampling technique. The light-extinction coefficient, which provides a measure of visibility, was selected as the primary evaluative parameter for two reasons. First, this parameter can serve as a surrogate for particulate matter with diameter of less than 2.5 μm, for which few observational data exist. Second, of the air quality parameters simulated by CMAQ, this visibility parameter has one of the most spatially and temporally comprehensive observational datasets. Results suggest that the approach reasonably characterizes synoptic-scale flow patterns and leads to strata that explain the variation in extinction coefficient and other parameters (temperature and relative humidity) used in this analysis, and therefore the approach can be used to achieve improved estimates of these parameters relative to estimates obtained using other methods. Moreover, defining seasonally based clusters further improves the ability of the clusters to explain the variation in these parameters and therefore leads to more precise estimates.
Abstract
The development of an episode selection and aggregation approach, designed to support distributional estimation for use with the Models-3 Community Multiscale Air Quality (CMAQ) model, is described. The approach utilized cluster analysis of the 700-hPa east–west and north–south wind field components over the time period of 1984–92 to define homogeneous meteorological clusters. Alternative schemes were compared using relative efficiencies and meteorological considerations. An optimal scheme was defined to include 20 clusters (five per season), and a stratified sample of 40 events was selected from the 20 clusters using a systematic sampling technique. The light-extinction coefficient, which provides a measure of visibility, was selected as the primary evaluative parameter for two reasons. First, this parameter can serve as a surrogate for particulate matter with diameter of less than 2.5 μm, for which few observational data exist. Second, of the air quality parameters simulated by CMAQ, this visibility parameter has one of the most spatially and temporally comprehensive observational datasets. Results suggest that the approach reasonably characterizes synoptic-scale flow patterns and leads to strata that explain the variation in extinction coefficient and other parameters (temperature and relative humidity) used in this analysis, and therefore the approach can be used to achieve improved estimates of these parameters relative to estimates obtained using other methods. Moreover, defining seasonally based clusters further improves the ability of the clusters to explain the variation in these parameters and therefore leads to more precise estimates.
