emissions as well as a report which examined trends in PM2.5 concentrations and precursors
(U.S. EPA, 2004b).
Weight given to trend analyses depends on several factors. Analyses that use more air
quality data and apply a greater variety of trend parameters provide more credible results. More
weight can be placed on the results if the procedure used to normalize the trend for
meteorological differences explains much of the variability attributable to these differences. In
addition, trend analysis is more believable if the extrapolation does not extend very far into the
future. Finally, trend analysis is most credible if the contemplated strategy is similar to a past
strategy (e.g., both strategies focus on reducing sulfates for PM or NOx for ozone). For
example, if a past strategy focused on reducing sulfates, but a future one envisions controlling
OC, there is no guarantee that ambient OC will respond similarly to changes in past emissions.
Observational Models In some cases ambient data can be used to corroborate the effects of a
control strategy (e.g., Blanchard et al, 1999; Croes et al, 2003; Koerber and Kenski, 2005).
Observational models take advantage of monitored data to draw conclusions about the relative
importance of different types of emissions and precursors as factors contributing to observed
PM2.5 and ozone, as well as inferences which might be drawn about the effectiveness of various
strategies to reduce concentrations. Observational models can be used to examine days which
have not been modeled with an air quality model, as well as days which have been modeled.
The resulting information may be useful for drawing conclusions about the general
representativeness of the responses simulated with the air quality model for a limited sample of
days. However, their ability to estimatehow much control is needed is limited. Thus,
observational approaches are suitable to corroborate results from more quantitative techniques,
like air quality models. Additionally, observational models are limited to analyses based on
current or past ambient conditions. A change in the relative mix of precursor emissions in the
future (compared to current conditions) may lead to a different set of future control strategies
compared to what the observational model may indicate based on current data. There are at least
two types of observational models: source apportionment (i.e., “receptor”) models and indicator
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