Date of Completion
air quality modeling, ozone, PM2.5, Kolmogorov-Zurbenko (KZ) filter, Model evaluation, coupled WRF-CMAQ, improved Complete Ensemble Empirical Mode Decomposition (EMD) with Adaptive Noise, trend
S. Trivikrama Rao
Field of Study
Doctor of Philosophy
Regional-scale air quality models are being used to perform research, forecasting, regulatory assessments and planning. Thus, it is important to critically assess the models’ capability in reproducing the spatio-temporal features seen in observations. The main scope of this work is to develop new methods for assessment and evaluation of EPA’s Community Multiscale Air Quality Modeling System (CMAQ) in the context of building confidence when regional-scale air quality models like CMAQ are used for policy support. The research is divided into three parts. In the first part, features embedded in two decades of ozone and PM2.5 observations and coupled WRF-CMAQ model outputs are analyzed and dynamic evaluation of model simulations is conducted. In the second part, a probabilistic framework is developed based on 34 years of ozone observations to examine the efficacy of emission reduction policies on ozone concentrations using regional-scale air quality models. In the third part, the contributions of anthropogenic emissions to ozone concentrations are assessed towards defining the manageable portion of ozone concentration with the aid of specialized CMAQ model simulations.
Luo, Huiying, "Interpreting the Information Embedded in Observed and Modeled Air Quality Time Series and Using Regional Air Quality Models for Regulatory Policies" (2019). Doctoral Dissertations. 2371.