strations, in which an air quality model is used to determine the amount of emission reductions needed to reach attainment by a specified date. In general, these attainment demonstrations have tended to be overly optimistic, and O3 concentrations in these areas have tended to exceed the values projected by the models (API 1989).

An Opportunity: Emerging Multipollutant Models

The growing development and use of multipollutant models suggests that a multipollutant approach to AQM is now viable. The current approach in the United States tends to ignore the interrelationships among pollutants. For the most part, planning and regulatory efforts have occurred without serious consideration of the linkages among pollutants and the commonality of their sources (the one exception being particles and visibility). At the very least, the effort to solve each air quality problem in isolation from the rest has probably resulted in missed opportunities to address different pollutants simultaneously. Future multipollutant modeling efforts should be enhanced to support strategies for simultaneous reduction of multiple pollutants.

Using the Weight-of-Evidence Approach in the Attainment Demonstration

In recognition of the uncertainties inherent in the use of the air quality models, EPA now encourages states to perform complementary analyses using available air quality, emission, and meteorological data along with outputs from alternative receptor- and observation-based models before arriving at a final target for the emission reductions that will be needed to meet attainment. These complimentary analyses can be used to assess the reasonableness of the results obtained from the air quality model simulations or as a weight-of-evidence determination that a given amount of emission reductions will be adequate to meet the NAAQS attainment standard.

The inclusion of a weight-of-evidence analysis in attainment-demonstration SIPs should be a positive development from a scientific and technical point of view. It implicitly acknowledges the limitations of air quality model simulations and allows planners to use information and insights from existing data and other analytical procedures to develop a more comprehensive and conceptual understanding of the relationship between pollutant emissions and the concentration of a criteria pollutant. In principle, such an understanding can make it possible for SIP developers to arrive at a more robust estimate of the emission reductions that will be needed to reach attainment. However, for this approach to work, the weight-of-

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