Air-quality model evaluation through the analysis of spatial-temporal ozone features

• Main Author: Shi, Tianji
• Language: English
• Published: University of British Columbia 2015
• Online Access: http://hdl.handle.net/2429/54707

Legislative actions regarding ozone pollution use air quality models (AQMs) such as the Community Multiscale Air Quality (CMAQ) model for scientific guidance, hence the evaluation of AQM is an important subject. Traditional point-to-point comparisons between AQM outputs and physical observations can be uninformative or even misleading since the two datasets are generated by discrepant stochastic spatial processes. I propose an alternative model evaluation approach that is based on the comparison of spatial-temporal ozone features, where I compare the dominant space-time structures between AQM ozone and observations. To successfully implement feature-based AQM evaluation, I further developed a statistical framework of analyzing and modelling space-time ozone using ozone features. Rather than working directly with raw data, I analyze the spatial-temporal variability of ozone fields by extracting data features using Principal Component Analysis (PCA). These features are then modelled as Gaussian Processes (GPs) driven by various atmospheric conditions and chemical precursor pollution. My method is implemented on CMAQ outputs during several ozone episodes in the Lower Fraser Valley (LFV), BC. I found that the feature-based ozone model is an efficient way of emulating and forecasting a complex space-time ozone field. The framework of ozone feature analysis is then applied to evaluate CMAQ outputs against the observations. Here, I found that CMAQ persistently over-estimates the observed spatial ozone pollution. Through the modelling of feature differences, I identified their associations with the computer model's estimates of ozone precursor emissions, and this CMAQ deficiency is focused on LFV regions where the pollution process transitions from NOx-sensitive to VOC-sensitive. Through the comparison of dynamic ozone features, I found that the CMAQ's over-prediction is also connect to the model producing higher than observed ozone plume in daytime. However, the computer model did capture the observed pattern of diurnal ozone advection across LFV. Lastly, individual modelling of CMAQ and observed ozone features revealed that even under the same atmospheric conditions, CMAQ tends to significantly over-estimate the ozone pollution during the early morning. In the end, I demonstrated that the AQM evaluation methods developed in this thesis can provide informative assessments of an AQM's capability. === Science, Faculty of === Statistics, Department of === Graduate