The U.S. Environmental Protection Agencys Community Multiscale Air Quality (CMAQ) model is a three-dimensional Eulerian (i.e., gridded) atmospheric chemistry and transport modeling system that simulates ozone, acid deposition, visibility, and fine particulate matter throughout the troposphere. Designed as a one-atmosphere model, CMAQ can address the complex couplings among several air quality issues simultaneously across spatial scales ranging from local to hemispheric. The CMAQ source code is highly transparent and modular to facilitate extensibility through community development.
CMAQ is a third-generation air quality model that is designed for applications ranging from regulatory and policy analysis to understanding the complex interactions of atmospheric chemistry and physics. First-generation air quality models simulated air quality using simple chemistry at local scales, and Gaussian plume formulation was the basis for prediction. Second-generation models covered a broader range of scales (local, urban, regional) and pollutants, addressing each scale with a separate model that often focused on a single pollutant (e.g., ozone). Third-generation models, like CMAQ, treat multiple pollutants simultaneously up to continental or larger scales, often incorporating feedback between chemical and meteorological components. Future efforts toward fourth-generation systems will extend linkages and process feedback to include air, water, land, and biota to provide a more holistic approach to simulating transport and fate of chemicals and nutrients throughout an ecosystem.
Air quality models integrate our understandings of the complex processes that affect the concentrations of pollutants in the atmosphere. Establishing the relationships among meteorology, chemical transformations, emissions, and removal processes in the context of atmospheric pollutants is the fundamental goal of an air quality model (Seinfeld and Pandis, 1998). In contrast to statistical air quality models that use historical trends in observed atmospheric conditions to predict air pollution, CMAQ uses coupled mathematical representations of actual chemical and physical process to simulate air quality. Based upon the underlying concept of preserving mass through a series of contiguous three-dimensional grid cells covering a fixed model grid, CMAQ belongs to the Eulerian class of mathematical models that calculate a mass balance within each grid cell by solving the transport across each cell boundary and chemical transformations within each cell during a given time period. As a framework for simulating the interactions of multiple complex atmospheric processes, CMAQ thus requires two major types of inputs: meteorological information and source emissions rates.
With weather conditions contributing the primary physical driving forces in the atmosphere (such as the changes in temperature, winds, cloud formation, and precipitation rates), representative gridded meteorology forms the basis of all 3-D air quality model simulations. The Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5) (Grell et al., 1994) and the Weather Research and Forecasting (WRF) model Advanced Research WRF (WRF-ARW) (Skamrock et al., 2005) are two meteorological models that are compatible with CMAQ. The meteorology inputs dictate the following CMAQ configuration parameters:
For inputs on emissions, CMAQ uses an emissions model to estimate the magnitude, location, and temporal variability of pollution sources. Open-source models such as the Sparse Matrix Operator Kernel Emissions (SMOKE) model (CEP, 2006) and the Consolidated Community Emissions Processing Tool (CONCEPT) (http://www.conceptmodel.org/) are available for computing emissions inputs to CMAQ from annual, county-level emissions inventories. CMAQ emissions inputs must be on the same horizontal and vertical spatial scales and cover the same time period used in the air quality model simulation. The emissions inputs to CMAQ must also represent volatile organic compound (VOC) emissions using a chemical parameterization supported by CMAQ; currently supported photochemical mechanisms are the Carbon Bond IV (CB IV) mechanism (Gery et al., 1989; Dodge, 1989; Carter, 1996), the 2005 update to the Carbon Bond mechanism (CB05) (Yarwood et al., 2005), and the Statewide Air Pollution Research Center (SAPRC-99) mechanism (Carter, 1990, 2000). Additional details about the gas-phase chemistry in CMAQ are provided in Chapter 2 below and in Byun and Ching (1999). Those two sources also describe the primary aerosol emissions species that are supported by CMAQ. It is possible to add new emissions species to CMAQ that are not supported in the distributed version of the software using the chemical mechanism compiler (CHEMMECH) that is part of the CMAQ utility programs (see Chapter 5).
As a community model, the modular design of CMAQ facilitates customization and open-source development. Using the Input/Output Applications Programming Interface (I/O API) library (http://www.baronams.com/products/ioapi/AA.html) to control the internal and external data flows to the model, and the network Common Data Form (netCDF) library (http://www.unidata.ucar.edu/software/netcdf) to control the input and output file formats, CMAQ is based around a transparent and platform-independent code infrastructure that promotes extensibility by a broad community of developers. The modularity of CMAQ also leads to multiple science configuration options that model users can choose from when setting up new simulations. The trade-off for this flexibility is complexity in the model configuration; the model user is faced with hundreds of different configuration combinations when designing new simulations. To avoid confusion for new CMAQ users, this document provides guidance on a basic configuration to use for getting started with the model. For experienced air quality model users, the multiple configuration combinations available provide an unprecedented level of flexibility for optimizing model performance for different air quality model applications.
CMAQ is designed to meet the needs of the multiple groups contained within the air quality modeling community: research and regulatory modelers, algorithm and science module developers, air quality forecasters, and planners and policy makers. While each of these groups has distinct individual requirements of CMAQ, they also share a common need for an accurate, efficient, transparent, and economical tool that represents the state-of-the-science to simulate air pollution formation and transport. To address these individual and common needs, CMAQ has the following goals: