Ph.D. Candidate, Environmental Engineering Program, University of Connecticut
Abstract: In the United States (U.S.), regional scale air quality models are being used to determine the emission reductions needed to comply with the national ambient air quality standards (NAAQS). The uncertainties associated with deterministic air quality model predictions have been researched by many scientists in the past, yet the biases and errors in the predicted air pollutant concentrations are still not negligible. The different timescales captured by modeled values and observations (intraday, synoptic, baseline) is one manifestation of the problem that could result in an uncertain estimate of the efficiency of the envisioned emission control strategies. In this work, we use air quality model simulations and observations from the Environmental Protection Agency (EPA) that span a 21-year long period (1990-2010) to assess the distinguishable scales of variations in pollutant concentration time series for both model and observed ozone concentrations. The Kolmogorov-Zurbenko (KZ) filtering technique is used to separate different scales imbedded in time series of observed and simulated ozone concentrations. Preliminary results for the NE U.S. have shown that the model is more skillful in representing the changes in the concentrations rather than the absolute values (exceedances). In this presentation, we will present the applied technique and the first results for NE U.S.