way (e.g., different secondary components). Thus, if one only considers PM2.5 as a single entity,

rather than as the sum of its major components, there is a greater risk of choosing an ineffective

control strategy. This follows, because models may not perform equally well in predicting major

components of PM2.5. Nevertheless, balancing errors could (erroneously) indicate good model

performance predicting PM2.5. If a control strategy focused on reducing a component of PM2.5

which was overestimated by the model, the subsequently observed impact on PM2.5 could be less

than expected.

Characteristics of PM2.5 as a mixture and the possibility that models perform unevenly in

predicting the major components of the mixture have two important implications for our

guidance. First, the modeling should divide PM 2.5 into a half a dozen or so major components

and note the effects of a strategy on each. The effect on PM2.5 should be estimated as a sum of

the effects on individual components. Second, to reduce the effects of uneven performance and

possible major bias in predicting absolute concentrations of one or more components, models are

best used in a “relative” sense in concert with measured PM2.5 and estimated composition of the

measured PM2.5 derived from speciated measurements. That is, responses predicted by models

should be applied to observed component concentrations derived from PM2.5 measurements and

composition of PM2.5 estimated from measurements of ambient species.

 

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