The grid resolution of the photochemical model and the number of flagged sources in the

dispersion model will determine the magnitude of the potential double counting. The higher the

resolution of the grid model, the more double counting will be a problem (because the local

sources will be more finely resolved). And the more sources that are flagged, the more double

counting will be a problem. Therefore, the nature of the issue should be evaluated on a case by

case basis. The simplest way to evaluate the magnitude of the problem is to run the

photochemical model with and without the flagged sources from the dispersion model. This will

indicate the maximum impact the sources can have on the photochemical modeling results. A

very small impact means that double counting is not a problem. A large impact indicates that

double counting needs to be explicitly accounted for.

Quality Assurance

As with any modeling exercise, it is important to quality assure the model inputs and

outputs. In particular, we recommend a focused quality assurance check on emissions from

sources flagged for a dispersion modeling analysis. Prior to applying a model, States should

review available information to ensure that there are no major discrepancies between modeled

estimates and nearby monitored data for particulate matter. The emission factors, activity data,

and speciation profiles of the PM2.5 emissions should also be analyzed for accuracy. If a

speciation monitor exists, the speciated data from the monitor can be compared to the speciation

profiles of the flagged sources. Receptor models can also be used as a QA tool. Discrepancies

between receptor modeling results (which are based on analyzing ambient data) and speciated


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