valuable insight into whether the model is adequately representing the conceptual description of

ozone and PM patterns in the nonattainment area. In cases where model performance is subpar,

these analyses can be useful for indicating where model input or model algorithm improvements

are most needed.

Caution should be applied to the tools and techniques mentioned here as they are limited

by the accuracy of the underlying air quality photochemical model, when the tools utilize the

same algorithms/modules and assumptions. An overview of these aforementioned probing tools

are provided below.

Photochemical source apportionment: A photochemical source apportionment tool tags

and tracks the release, transport, chemical transformation, and deposition of precursor species

from primary emission sources, source categories, source regions, initial conditions and/or

boundary conditions from a photochemical grid model. Therefore, the contribution of tagged

sources to simulated concentrations (including secondary pollutants) and deposition can be

measured in a single model run. This source apportionment tool can be used to estimate how

emissions from individual source areas and regions affect modeled ozone and PM concentrations

over space and time. For example, this is achieved by using multiple tracer species to track the

fate of ozone precursor emissions (VOC and NOx) and the ozone formation caused by these

emissions within an air quality model simulation. There are challenges to consider during

implementation of this methodology, i.e., tracking the spatial and temporal relationships between

separate groups of emission sources and ozone and particulate matter formation (Douglass, 2006),

(Environ, 2006a), (Environ, 2006b). Among them are insuring compatibility with the underlying

air quality model formulation so that derived source-receptor relationships will be consistent with

model response to emission changes.


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