component, elemental carbon (EC), and the general category of “other primary particulate
matter” (OPP). In some cases, directly emitted sulfate (and in rare cases nitrate) may also be a
significant component of the local primary PM2.5.
The dispersion modeling results should be evaluated to ensure adequate model
performance. Similar to grid modeling, the dispersion model results should be compared to
ambient data to ensure that the model is working well. Although section 18 of this guidance is
geared towards evaluating grid models, many of the same statistical calculations can be made for
primary PM2.5 and PM2.5 components predicted by a Gaussian dispersion model. Since
secondary PM2.5 is often a large component of total PM2.5 concentrations, it may be difficult to
separate the primary and secondary components of ambient PM2.5. EC and OPP should be
considered to be primary PM2.5. Much of the rest of the primary PM2.5 concentration will be
As part of the analysis, an estimated concentration of primary OC is needed. There are
several methods available for estimating the primary vs. secondary portion of ambient OC.
Among these are the EC tracer method and receptor modeling. The EC tracer method is the
most common method used to estimate secondary and primary OC concentrations (Turpin,
1995), (Strader, 1999) (Cabada, 2004), (Chu, 2005), (Saylor, 2006) . The method uses
measurements of OC and EC and calculated OC to EC ratios to identify periods when OC is
likely to be mostly primary. This information is then used to calculate the secondary
contribution to OC . Receptor models such as CMB and PMF have also been used to estimate
secondary organic concentrations (Na, 2004), (Yuan, 2006).
The following sections discuss a suggested methodology for examining decreases or
increases in PM concentrations due to local primary sources for the Annual and 24-hour PM2.5
NAAQS. Because each nonattainment area has unique emissions sources and source-receptor
relationships, States should work closely with their EPA Regional Office in developing local
area analysis applications.
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castellano: DISPER CUSTIC DESCAR RADIA italiano:
deutsch: DIS CUS DES RAD
castellano: DIS CUS DES RAD english: DIS CUS DES RAD
português: DIS CUS DES RAD italiano: DIS CUS DES RAD
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