A new technique has been developed to more efficiently examine a large range of

emissions control scenarios. Response Surface Modeling (RSM), has been developed by utilizing

advanced statistical techniques to characterize the relationship between model outputs and input

parameters in a highly economical manner (U.S. EPA, 2006c). The RSM is a metamodel of a

model (i.e., air quality model); it is a reduced-form prediction model using statistical correlation

structures to approximate model functions through the design of complex multi-dimension

experiments.

The RSM is based on a new approach known as air quality metamodeling that aggregates

numerous pre-specified air quality modeling simulations into a multi-dimensional air quality

“response surface”. Simply, this metamodeling technique is a “model of the model” and can be

shown to reproduce the results from an individual modeling simulation with little bias or error

(U.S. EPA, 2006c, Hubbell, 2005). The RSM is based on statistical relationships between model

inputs and outputs to provide real-time estimate of these air quality changes. The RSM provides

a wide breadth of model outputs, which can be used to develop emissions control scenarios. The

RSM approach can inform the selection and evaluation of various control scenarios. This

approach allows for the rapid assessment of air quality impacts and changes of different

combinations of emissions reductions. While the RSM may not provide a complete picture of all

changes necessary to reach various alternative standards nationwide, it is highly useful in the

 

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