Similar to ozone evaluations, it may be useful to prioritize examination of model
performance within and near the non-attainment area(s) and/or Class I areas of interest.
Additionally, priority may be placed on examination of the days that are potentially used in the
attainment test (20% best and worst days for visibility and the days > 65 ug/m3 for 24-hour
Statistics: Statistics for PM and regional haze performance are similar to those calculated
for ozone. We recommend calculating statistics for components of PM2.5, and PM precursors.
Useful metrics include mean fractional bias and mean fractional error, normalized mean bias, and
normalized mean error. Formulas for estimating these metrics at an individual monitoring site, j,
are shown in Equations (18.3) -(18.6). Other statistics such as mean bias, mean error, root mean
square error, correlation coefficients, etc. should also be calculated to the extent that they provide
meaningful information (see Appendix A for definitions).
50Predicting very low concentrations is not critical for modeling the PM2.5 NAAQS.
However, predicting low concentrations of PM species can be important for regional haze
modeling, where in the cleanest Class I areas, PM concentrations may be less than a few ug/m3
(especially on the 20% best days).
Mean Fractional Bias (percent): Normalized bias can become very large when a
minimum threshold is not used. Fractional bias is used as a substitute. The fractional bias
for cases with factors of 2 under- and over-prediction are -67 and + 67 percent,
respectively (as opposed to -50 and +100 percent, when using normalized bias).
Fractional bias is a useful indicator because it has the advantage of equally weighting
positive and negative bias estimates. The single largest disadvantage is that the predicted
concentration is found in both the numerator and denominator.
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