Meteorology | Emissions | Chemistry

To provide extensive air quality modeling capabilities, we have incorporated a modeling paradigm that incorporates different meteorological models, two emission inventory models, and two chemical transport models were used, along with assorted supporting pre- and inter-processing programs. The primary analysis tools are based on the Community Multiscale Air Quality (CMAQ) modeling system (Byun and Ching, 1999), which is the latest Eulerian air quality model made available by the U.S. EPA. CMAQ employs the best available techniques for advection, diffusion, and complex chemical transformation of a variety of pollutants. The system consists of three primary components (meteorology, emissions, and a chemical transport model) and several interface processors. Figure 1 illustrates the relationship between the CMAQ processors and the requisite interfaces with the chemical transport model. With this structure, CMAQ retains the flexibility to substitute other emissions modeling systems and meteorological models. In the current release, the Sparse Matrix Operator Kernel Emission (SMOKE; Coats, 1996; Houyoux et al., 2000) model is used to produce the model-ready emissions data and the Fifth Generation Penn State University/National Center for Atmospheric Research Mesoscale Model (MM5) (Grell et al., 1994) provides the meteorological fields needed for the CMAQ Chemical Transport Model (hereafter, CTM). Within the CMAQ paradigm, the emission processing and meteorological modeling systems can be substituted with alternative processors. The CMAQ CTM (CCTM) is designed for multiscale air quality modeling, from very small scales (such as urban areas) to very large scales (such as the US continent). CMAQ uses a generalized coordinate system, which makes it flexible to work with most known Eulerian systems as long as the Jacobian of transformation is available. Mass consistency is one of the most important features for transport and transformation of air pollutants. For that reason mass consistency is constantly monitored in the model during calculations. In cases of inconsistencies in the input, different algorithms for mass adjustment are available.

A set of preprocessors provides linkage mechanisms among the meteorology, emissions, and chemistry transport modeling components. These processors include: the Emission-Chemistry Interface Processor (ECIP) that translates data from the SMOKE emission model for use in the CCTM; the Plume Dynamics Model that computes geometry of subgrid scale Lagrangian plumes for large elevated emitters; and the Meteorology-Chemistry Interface Processor (MCIP) that translates and processes outputs from the meteorology model for the CCTM. Initial condition and boundary condition processors (IC/BC Proc) provide concentration fields for individual chemical species for the beginning of a simulation and for the grids surrounding the modeling domain, respectively, and the photolysis processor (JPROC) calculates temporally varying photolysis rates. The arrows in Figure 1 show the flow of data through the modeling system.

Figure 1. The air quality information infrastructure includes extensive modeling and analysis systems and environmental input data and user interface. Solid lines represent primary data flows and dashed lines are for alternative processing sequences. Oval symbols are for the science models, small rectangles are for the interface processors, and circles represent analysis and visualization systems (GRADS, GIS, DX, Vis5D, and PAVE). Solid lines represent for the essential data linkage and arrows represent directions of the flows of information. Dashed lines represent optional data flow. Many of linkages among the processors and data are built at University of Houston with the existing software modified. A web-based user interface is under development for public access for generating reports from the model output and measurement data.

In addition to the CMAQ modeling system, other meteorological models—such as Weather Research and Forecasting (WRF) model (Klemp et al., 2000) might also be applicable in this framework, which is a next-generation mesoscale modeling system to replace MM5; and the CALMET model (Scire et al., 2000), which provides a mechanism further downscaling meteorology beyond the current limit of mesoscale models. Also, we include an alternative emissions processor--Emissions Processing System version 2 (EPS2); and a regulatory model for Texas, Comprehensive Air Quality Model with Extensions (CAMx) (Environ, 2000) in this framework, to facilitate comparative evaluation of different modeling systems. MetProc in Figure 1 transforms the MM5 output to provide meteorological data for CAMx. CMAQ and CAMx differ in many science areas such as in their dynamics descriptions, vertical and horizontal grid structures, detailed physics and numerical algorithms used. Such differences are bound to predict different air quality simulation results. A powerful method for improving our understanding of the atmospheric processes involved with air quality problems is the comparative evaluation of results gained from different modeling systems when compared against available observations, such as the TexAQS 2000 experiment data. Together with the input database and analysis tools described below, the combination of multiple meteorological, emissions, and air quality modeling tools and associated processors form a powerful information infrastructure that allows processing of measurement and modeled air quality data.