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Interpretation of PM2.5 Model Performance Metrics “paralysis by analysis”

Interpretation of PM2.5 Model Performance Metrics “paralysis by analysis”. Kirk Baker Lake Michigan Air Directors Consortium Midwest Regional Planning Organization February 2004. PM2.5 Specie Scatter-plot. PSO4 PNO3 SOIL COARSE OC EC. Mean (Gross) Bias. PSO4 PNO3 SOIL COARSE OC

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Interpretation of PM2.5 Model Performance Metrics “paralysis by analysis”

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  1. Interpretation of PM2.5 Model Performance Metrics“paralysis by analysis” Kirk Baker Lake Michigan Air Directors Consortium Midwest Regional Planning Organization February 2004

  2. PM2.5 Specie Scatter-plot PSO4 PNO3 SOIL COARSE OC EC

  3. Mean (Gross) Bias PSO4 PNO3 SOIL COARSE OC EC

  4. Mean (Gross) Bias

  5. Mean Absolute (Gross Error) PSO4 PNO3 SOIL COARSE OC EC

  6. Mean Absolute (Gross Error)

  7. Normalized Bias PSO4 PNO3 SOIL COARSE OC EC

  8. Normalized Bias

  9. Normalized Gross Error PSO4 PNO3 SOIL COARSE OC EC

  10. Normalized Gross Error

  11. Fractional Error PSO4 PNO3 SOIL COARSE OC EC

  12. Fractional Error

  13. Fractional Bias PSO4 PNO3 SOIL COARSE OC EC

  14. Fractional Bias

  15. Modified Index of Agreement

  16. Coefficient of Determination

  17. Mean Fractional Bias PSO4 PNO3 SOIL COARSE OC EC

  18. Relative Response Evaluation • Used CART Analysis to find similar meteorological “episode” to June 2002 • Model June 2002 and past year using 2002 meteorological inputs • Construct an approximation of the past episode emission inventory • Examine model response in past episode and current episode for O3 and SO2/PSO4 where available

  19. Multiple Model Diagnostics

  20. Performance driven by standard averaging time Attainment tests use relative factor approach by specie Metric guidelines by specie Monitor error Chemistry uncertainty Gross emissions uncertainty Regulatory importance Mindset of trying to get the right answer everywhere, all the time, for all PM2.5 species and precursor species Middle Ground? Regulatory v. Scientific Evaluation

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