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Understanding the USEPA’s AERMOD Modeling System for Environmental Managers

Understanding the USEPA’s AERMOD Modeling System for Environmental Managers. Model Evaluation. Ashok Kumar University of Toledo akumar@utnet.utoledo.edu. Evaluation Studies on AERMOD. USEPA Evaluation using field studies No evaluation using ambient air monitoring network in an urban area.

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Understanding the USEPA’s AERMOD Modeling System for Environmental Managers

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  1. Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Model Evaluation Ashok Kumar University of Toledo akumar@utnet.utoledo.edu Air Pollution Research Group

  2. Evaluation Studies on AERMOD • USEPA • Evaluation using field studies • No evaluation using ambient air monitoring network in an urban area Air Pollution Research Group

  3. Lucas County: Sources and Monitoring Stations Air Pollution Research Group

  4. Input Data Flow in AERMOD Air Pollution Research Group

  5. Data Requirements for Model Evaluation • Emission Inventory • Properties of stacks and super stacks • Meteorological data • Receptor data • Air monitoring data Air Pollution Research Group

  6. AERMET - Input • Meteorological Input Parameters – Multi-Level WS, WD, and Temperature, Opaque Cloud Cover, Ceiling Height, RH, Pressure, Surface Heat Flux, Friction Velocity, and Roughness Length, Delta-T , Solar Radiation, Upper Air Data • Data Formats - CD144, SCRAM, SAMPSON (surface data) • TD 3280 (surface data ) • TD6201 (upper air data) • On-site (site specific data) Air Pollution Research Group

  7. Boundary Layer File sensible heat flux surface friction velocity convective velocity scale potential temp. gradient above mixing height convectively-driven mixing height mechanically-driven mixing height Monin-Obukhov length surface roughness length Bowen ratio albedo Profile File Measurement height WD, WS Temperature Standard Dev. of Lateral WD Standard Dev. of Vertical WS AERMET - Output Air Pollution Research Group

  8. Atmospheric Stability • AERMOD uses Monin-Obukhov length as the stability parameter • You will need friction velocity u*and the flux of sensible heat H to compute L • L is defined to be negative in convective conditions and positive in stable Air Pollution Research Group

  9. AERMAP • Input data needs for AERMAP: • DEM formatted terrain data • User provided receptors and terrain • Design of receptor grid: AERMAP accepts either polar, cartesian or discrete receptors Air Pollution Research Group

  10. Pathways Used in AERMOD Input Runstream • Control • Source • Receptor • Meteorology • Output Air Pollution Research Group

  11. Statistical Evaluation Methods • Fractional Bias (FB) • Normalized Mean Square Error (NMSE) Air Pollution Research Group

  12. Statistical Evaluation Methods • Coefficient of Correlation (COR) • Factor of Two(Fa2) Fraction of data for which 0.5<Cp/Co<2 Air Pollution Research Group

  13. Statistical Evaluation Methods • Confidence Limits Confidence limits are the lower and upper boundaries / values of a confidence interval, that is, the values which define the range of a confidence interval. The upper and lower bounds of a 95% confidence interval are the 95% confidence limits. • Q-Q Plots The quantile-quantile (Q-Q) plot is a graphical technique for determining if two data sets come from populations with a common distribution. Air Pollution Research Group

  14. Results & Discussion • Model is evaluated in the following ways: • Performance measures and confidence limits for 3-hr average for SO2 • Stable Condition • Convective Condition 24-hr average for SO2 • Plots of NMSE vs.FB • Q-Q plots for 3-hr average for S02 • Q-Q plots for 24-hr average for S02 Air Pollution Research Group

  15. NMSE vs FB plots (3-hr average) Air Pollution Research Group

  16. NMSE vs. FB plots(3-hr average) Air Pollution Research Group

  17. NMSE vs. FB plots (3-hr average) Air Pollution Research Group

  18. NMSE vs. FB plots (3-hr average) Air Pollution Research Group

  19. NMSE vs. FB plots (24-hr average) Air Pollution Research Group

  20. Q-Q plots (3-hr average) Air Pollution Research Group

  21. Q-Q plots (3-hr average) Air Pollution Research Group

  22. Q-Q plots (3-hr average) Air Pollution Research Group

  23. Q-Q plots (3-hr average) Air Pollution Research Group

  24. Q-Q plots (3-hr average) • Observed Concentrations < 20µg/m3 Air Pollution Research Group

  25. Q-Q plots (3-hr average) • Observed Concentrations < 20µg/m3 Air Pollution Research Group

  26. Q-Q plots (3-hr average) • Observed Concentrations < 20µg/m3 Air Pollution Research Group

  27. Q-Q plots (3-hr average) • Observed Concentrations < 20µg/m3 Air Pollution Research Group

  28. Q-Q plots (24-hr average) Air Pollution Research Group

  29. Q-Q plots (24-hr average) Air Pollution Research Group

  30. Confidence Limits (3-hr average) • The values of NMSE and FB were significantly different from zero in the stable case. COR was not significantly different from zero. • The values of NMSE, FB, and COR were not significantly different from zero for the convective case. Air Pollution Research Group

  31. Confidence Limits (24-hr average) • The values of NMSE and FB were significantly different from zero. COR, was not significantly different from zero. • Note: 24-hr data were not divided according to stability classes. Air Pollution Research Group

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