Other observational methodologies exist and can be used in a similar manner. The Smog

Production (SP) algorithm is another means by which ambient data can be used to assess areas

that are NOx or VOC-limited (Blanchard, 1999). Additionally, it has been postulated that

differences in weekend-weekday ozone and PM2.5 patterns may also provide real-world

information on which precursors are most responsible for ozone and PM2.5 formation in any given

area (Pun, 2001 and 2003; Heuss, 2003; Fujita, 2003; Blanchard and Tanenbaum, 2003 and 2005;

Chinkin, 2003; Yarwood, 2003; Qin, 2004). For example, activity levels and patterns, leading to

PM2.5 and precursor emissions from mobile, area and some point sources, may differ on weekends

vs. week days. In areas where there are large differences between average weekend and weekday

ambient ozone and PM2.5 concentrations over the span of several seasons, it would be useful to

compare statistical model performance for weekends versus weekdays (e.g., does the model

accurately reflect observed differences in component concentrations in summer vs. winter?). This

would allow one to assess whether the model is capturing the effect of the emissions differences

which are presumably driving the real-world concentration differences.

18.5.2 Probing Tools

Recently, techniques have been developed to embed procedures within the code of an air

quality model which enable users to assess the contributions of specific source categories or of

specific geographic regions to predicted model concentrations at specified sites (e.g. for ozone

predictions, Zhang, 2003). Various techniques have been implemented into various air quality

models, but three of the most commonly used probing tools are photochemical source

apportionment (Dunker, 2002a; ENVIRON, 2006a; Tonnesen and Wang, 2004; SAI, 2005;

Yarwood, 2005; Douglas, 2006), the decoupled direct method (DDM) (Dunker, 1980 1981, and

1984; Yang, 1997a and 1997b; Dunker, 2002b; ENVIRON, 2006a; Cohan, 2002, 2004, and 2005;

Hakami, 2003 and 2004), process analysis (Jeffries, 1994 and 1997; Jeffries, 1996; Jang, 1995;

Lo, 1997; Byun and Ching, 1999; Morris, 2001 and 2003; ENVIRON, 2006a; Henderson, 2006).

In the context of model performance evaluation, these attribution procedures are useful in that

they allow one to "track" the importance of various emissions categories or phenomena

contributing to predicted ozone and particulate matter at a given location. This can provide

 

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