account the horizontal/vertical model resolution of the model configuration and the size of the

area to be modeled. In short, treatment of emissions is a central and complex one which, itself,

involves several steps. These include deriving emission inventories, quality assuring results,

applying results in an emission model(s), and (again) quality assuring results. Emission inputs

may be needed for number of scenarios including; (1) a base case corresponding to that of the

selected meteorological time periods, (2) a baseline corresponding to that represented by the

baseline monitored design value, (3) a future base case when attainment of the NAAQS or

regional haze progress needs to be demonstrated, and (4) control scenarios in which additional

emissions controls are applied to emissions in the future base case.

8. Evaluate performance of the air quality simulation model and perform diagnostic tests.

The credibility of a modeled attainment test and other model outputs is affected by how well the

model replicates observed historical air quality. Evaluating model performance and conducting

diagnostic tests depend on the prior definition of the modeling exercise and specification of

model inputs. Hence, this is generally the last step prior to using the model to support an

attainment demonstration or glidepath assessment. In the past, the performance evaluation for

ozone has relied almost exclusively on numerical tests comparing predicted and observed ozone,

or visual inspection of predictions and observations. These are still important tools. However,

photochemical grid models have many inputs, and it is possible to get similar predicted ozone

and/or PM2.5 concentrations with different combinations of inputs. There is no guarantee that

ozone and/or secondary PM2.5 will respond the same way to controls with these different

combinations of inputs. Thus, we place greater emphasis on additional kinds of tests than was

the case in past guidance. These include use of precursor observations, indicator species, and

corroborative analyses with observational models. Diagnostic tests are separate simulations

which are performed to determine the sensitivity of a model’s predictions to various inputs to the


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