You are here:
Predicting Future-Year Ozone Concentrations: Integrated Observational-Modeling Approach for Probabilistic Evaluation of the Efficacy of Emission Control strategies
Citation:
Astitha, M., H. Luo, C. Hogrefe, R. Mathur, AND S. Rao. Predicting Future-Year Ozone Concentrations: Integrated Observational-Modeling Approach for Probabilistic Evaluation of the Efficacy of Emission Control strategies. 16th CMAS Conference, Chapel Hill, North Carolina, October 23 - 25, 2017.
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. 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:
Regional-scale air quality models are being used to demonstrate attainment of the ozone air quality standard. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological conditions as those in the base year. Given the stochastic nature of the atmosphere, it is highly unlikely that the same meteorological conditions would prevail in the future year. Here, we use multi-decadal observations to develop a new method for estimating confidence limits for the future ozone design value (based on 4th highest daily maximum 8-hr ozone concentration) for each given emission loading scenario along with the probability of the design value exceeding a given ozone threshold concentration at each monitoring site in the Contiguous U.S. To this end, we spectrally decompose the observed daily maximum 8-hour (DM8HR) ozone time series data covering the period from 1981 to 2014 using the KZ filter to examine the variability in the relative strengths of the short-term variation (i.e., synoptic forcing induced by synoptic-scale weather fluctuations) and the magnitude of the long-term variation (i.e., baseline forcing induced by emissions, policy changes, background, and trends). Results indicate that combining the projected change in the ozone baseline levels with the synoptic forcing embedded in historical ozone observations would enable us to provide a probabilistic assessment of the efficacy of the selected emissions control strategy in complying with the ozone standard in future years.
