Because the uniform rate of progress analysis relies on mean visibility values on 20 or

more “worst” visibility days and most of these days are unlikely to change, we would expect

little difference in the outcome of the analysis. Further, because of the shape of the distribution

of extinction coefficients, the mean of the worst days is more heavily influenced by extreme days

rather than those on the borderline between “worst” and more moderate light extinction. There

could be differences in some “best” visibility days corresponding with pre- and post-control

emissions. However, because the differences in concentrations of particulate matter on such

 

83This is only recommended if the model performance for all species is good (at sites with

data). While it is expected that the air quality models will do an adequate job of predicting the

PM2.5 components which are responsible for the largest visibility degradation, the models may

not perform as well in estimating total visibility impairment. Accurately estimating the 20% best

and worst days depends on accurately estimating all of the PM components. In particular,

overprediction of certain components (e.g nitrates in the winter) could lead to identification of

worst or best days in the wrong season and/or for the wrong reasons (e.g. wrong mix of species

on the best or worst days).

 

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