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Air Quality Model Evaluation through the Analysis and Modelling of Ozone Features

Tuesday, April 21, 2015 - 11:00
Tianji Shi PhD Candidate, UBC Department of Statistics
Seminar
Room 4192, Earth Science Buildling, 2207 Main Mall

Legislative actions regarding ozone pollution use air quality models (AQMs) such as Community Multiscale Air Quality (CMAQ) model for scientific guidance, hence the evaluation of AQM such as CMAQ is an important subject. Traditional point-to-point comparisons between AQM outputs and ozone observations can be uninformative or even misleading since the AQM modelled ozone process and physical observations are governed by different 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 and observation. To successfully implement feature-based AQM evaluation, I further developed statistical framework of analyzing and modelling space-time ozone fields 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 Lower Fraser Valley, BC. I found that the feature-based ozone model is an efficient way of modelling 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 CMAQ inputs on ozone precursor emissions, and the CMAQ-observation differences are focused on regions where the pollution process transitions from NOx-sensitive to VOC-sensitive. Through the comparison of dynamic ozone features, I found that CMAQ's over-prediction is also connect to the model producing higher than observed ozone plume in daytime. However, CMAQ model did capture the observed space-time pattern of diurnal ozone advection. 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 feature-based AQM evaluation methods developed in this research are able to provide ``big picture'' process-level understandings of AQM deficiency.