Development and initial application of a sub-grid scale plume treatment in a state-of-the-art online Multi-scale Air Quality and Weather Prediction Model

Abstract

Traditional Eulerian air quality models are unable to accurately simulate sub-grid scale processes, such as the near-source transport and chemistry of point source plumes, because they assume instantaneous mixing of the emitted pollutants within the grid cell containing the release, and neglect the turbulent segregation effects that limit the near-source mixing of emitted pollutants with the background atmosphere (e.g., Kramm and Meixner, 2000). Observations by Dlugi et al. (2010) show that the segregation of chemically reactive species can slow effective second-order reaction rates by as much as 15%, due to inhomogeneous mixing of the reactants. This limitation of traditional grid models applies to both “off-line” models, in which externally derived meteorology is used to drive the chemistry model, and newer “on-line” models, such as the Weather Research and Forecasting model with Chemistry (WRF/Chem), that simulate the emissions, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. While a number of approaches have been used in the past to address this limitation, the approach that has been most effectively used in operational models is the plume-in-grid (PinG) approach, in which a reactive plume model is embedded within the grid model to resolve sub-grid scale plumes. This paper describes the implementation of such a PinG treatment in WRF/Chem, based on a similar extension to the U.S. EPA Community Multi-scale Air Quality (CMAQ) model. The treatment, referred to as Advanced Plume Treatment, has been tested in CMAQ over more than a decade and has been used successfully in both episodic and long-term applications for assessing point source contributions to ozone and particulate matter. This paper presents the application of the PinG version of WRF/Chem for a three-day episode in July 2001, including a model performance evaluation and comparison of model results with and without PinG treatment. The results from the model application show that overall model performance is only slightly affected when the PinG treatment is used, although there are some generally small improvements, with the PinG treatment showing a 5% lower bias in predicting ozone concentrations, and 3% lower bias in sulfate predictions. However, the predicted spatial patterns of ozone and PM2.5 concentrations from the two simulations show both large decreases of up to 40 ppb ozone and 14 μg m−3 PM2.5, and increases of up to 80 ppb ozone and 33 μg m−3 PM2.5 as a result of using the PinG treatment. These differences are attributed to both direct effects of the PinG treatment (i.e., differences in dispersion, transport and chemistry of point source emissions) and indirect effects (i.e., impacts of air quality changes on meteorology).