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Using CMAQ to provide probabilistic assessment of emission control scenarios in meeting the ozone standard
Citation:
Luo, H., M. Astitha, C. Hogrefe, R. Mathur, AND S. Rao. Using CMAQ to provide probabilistic assessment of emission control scenarios in meeting the ozone standard. ITM 2018 - 36th International Technical Meeting on Air Pollution Modelling and its Application, Ottawa, Ontario, CANADA, May 14 - 18, 2018.
Impact/Purpose:
The novel concept in this work is the determination of confidence intervals for the ozone exceedances in the future year and probability of achieving ozone compliance for future emission scenarios. We utilize ozone observations over a 30-yr timeframe to demonstrate the proposed approach for examining expected changes in extreme ozone concentration levels for the future target year. We also use data from long-term CMAQ simulations to assess how well changes in baseline concentrations are captured by the model and how simulated baseline changes can be integrated with observations into a probabilistic framework. A probabilistic assessment is used to investigate how the base year non-attainment situation would change with given emission control options in the future and obtain confidence limits associated with such projections. Results analyzed and presented in this probabilistic manner would enable more explicit consideration of the ever-present uncertainty in projected changes in air quality needed to comply with the ozone standard in the future.
Description:
In the United States, regional-scale air quality models are being used to identify emissions reductions needed to comply with the ozone National Ambient Air Quality Standard. Previous work has demonstrated that ozone extreme values (i.e., 4th highest ozone or Design Value) are controlled by the level of the ozone baseline component (i.e., the longer-term forcing, which is primarily influenced by emissions loading, embedded in time series of the daily maximum 8-hrozone). In this paper, we evaluate whether the changes in Community Multiscale Air Quality (CMAQ) model-simulated ozone baselines are consistent with those seen in observations and illustrate a method to help build confidence in using regional models for policy-making. To this end, we propose a probabilistic assessment method by utilizing model-simulated changes in the baseline concentration level stemming from envisioned emission control strategies together with the historical observed meteorological forcing embedded in daily maximum 8-hr ozone time series (>30 years) to account for the synoptic-scale weather-induced fluctuations. Results indicate that this new approach would enable us to evaluate the efficacy of the selected emissions control strategy in achieving ozone compliance in the future year with increased confidence.
