This paper describes a study to evaluate the capability of a photochemical grid modeling system to predict changes in ozone concentrations in response to emission changes over a period of several years. The development of regulatory emission control plans to meet air quality standards primarily relies on modeled projections of future-year air quality, although a weight of evidence approach (which takes into account a number of factors including modeling results, model evaluation and other pertinent information such as ambient trends) is recommended and is also typically used as part of the attainment demonstration. Thus, it is important to determine if the modeling system used to project future-year quality can correctly simulate ozone responses to the projected emissions reductions. Uncertainties and errors in modeled projections can lead to erroneous estimates of emissions controls required to attain the standards. We use two existing regulatory modeling databases, employed for forecasting future-year air quality in the South Coast Air Basin (SoCAB) of California, for a number of historical years to evaluate the ability of the system to accurately simulate the observed changes in air quality over a multi-year period. The evaluation results with the older (2012) database show that the modeling system consistently under-predicts the reductions in ozone in response to emission reductions over the years. Model response improves with the newer (2016) database with good agreement at some locations, but the system still tends to under-predict ozone responses by as much as a factor of 2 in recent years for the Basin maximum ozone design value. This suggests that future-year estimates of ozone design values may be overly conservative, resulting in emission controls that are technologically challenging or very expensive to implement. The development of better emission inventories and model inputs is recommended to develop a modeling system that more accurately responds to emission changes. Future regulatory planning should include dynamic evaluation in addition to the traditional operational evaluation of the model to provide more confidence to all stakeholders that the resulting policy decisions are necessary to attain the air quality standards and to protect public health.
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