The National Ambient Air Quality Standards (NAAQS) were established by the U.S. Environmental Protection Agency (USEPA) under the authority of the Clean Air Act (U.S. EPA, 2004). These standards are designed to protect human health and the environment from high levels of criteria pollutants, such as ozone and particulate matter. The complex nature of air pollution scenarios requires control strategies to be effective for a variety of air pollutants, regions, and scales. In an effort to decrease ambient levels of criteria pollutants and to better understand the transport of chemicals, emission control strategies, and air quality, models have been approved by the USEPA for use at regional, state, and local scales within the United States. The models have been used to estimate the contribution of different control strategies to improve air quality and ensure cost effective results.
Historically models were designed and developed for specific air quality issues, such as acid deposition or ozone. Depending on the release characteristics, the pollutants in question, and the surrounding meteorological conditions at the time of pollutant release, modeling scenarios can range from a localized, short-term phenomenon to a long-term regional event. Because some emission sources contribute to the ambient levels of more than one pollutant and can affect an entire regional area on different time scales, an integrated modeling approach capable of handling multiple air pollutants and spatiotemporal scales was needed to isolate control strategies that improve overall air quality in a cost-effective manner. New air quality issues identified by the Clean Air Act Amendments of 1990 (such as visibility, fine and coarse particles, and indirect exposure to toxic pollutants) make an integrated modeling approach that can address multiple pollutants even more essential.
CMAQ is a multi-pollutant, multi-scale air quality modeling system that can simulate the transport and chemistry of ozone, particulate matter, toxic airborne pollutants, and acidic and nutrient pollutant species. CMAQ uses state-of-the-science techniques and has many new and important features that are not available in previous modeling systems. CMAQ is capable of modeling complex atmospheric processes affecting transformation, transport, and deposition of air pollutants using a system architecture that is designed for fast and efficient computing.
CMAQ allows users to easily construct models with different characteristics, such as different chemical mechanisms or alternative cloud treatments, in order to address a specific air quality issue (Figure 2-1). This modeling configuration will allow CMAQ to retain its state-of-the-science status over time with future implementations of new science modules as appropriate. At the same time, CMAQ can be employed for regulatory applications by using approved standard configurations of the modeling platform that represent the best available modeling technology at a given time. CMAQ has been developed to meet the needs of both the research and application communities.
The CMAQ modeling system is also a useful tool for the model developer. It is unique in that its components are designed in a modular fashion with an interface that allow a model developer to create complex modeling situations and scenarios, or develop entirely new models using a standardized coding framework. Model developers can also perform sensitivity analyses on newly developed modules and perform comparisons with existing systems. Brief descriptions of the key features in CMAQ are discussed in this chapter. More detailed discussions on these features can be found in Byun and Ching (1999) and Byun and Schere (2006).
The CMAQ modeling system has a flexible modeling structure. Historically air quality model development resulted in separate air quality models that addressed specific issues. For instance, independent models were developed for ozone modeling and for acid deposition modeling. These models had little or no flexibility to be updated with advances in science or to accommodate new regulations. With this in mind, CMAQ was designed to have more adaptability and flexibility for different applications and for changing or improving methodology. The following subsections provide examples of how this is accomplished in CMAQ.
CMAQ approaches air quality as a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including tropospheric ozone, fine particles, toxins, acid deposition, and visibility degradation. The development of CMAQ includes the scientific expertise from each of these areas and combines the capabilities to enable a community modeling practice. CMAQ has multi-scale capabilities so that separate models are not needed for urban and regional scale air quality modeling. The target grid resolutions and domain sizes for CMAQ range spatially and temporally over several orders of magnitude. With the temporal flexibility of the model, simulations can be performed to evaluate longer term pollutant climatologies, as well as short-term transport from localized sources. By making CMAQ a modeling system that addresses multiple pollutants and different spatial scales, CMAQ has a “one atmosphere” perspective that combines the efforts of the scientific community. Developments and improvements to the CMAQ modeling system can continue as the state-of-the-science evolves. For example, linkages of CMAQ with exposure modeling, hydrological modeling, climate modeling, and multimedia modeling are currently under development.
To implement multi-scale capabilities in CMAQ, several different issues, such as scalable atmospheric dynamics and generalized coordinates that depend on the desired model resolution are addressed. Meteorological models may assume hydrostatic conditions for large regional scales, where the atmosphere is assumed to have a balance of vertical pressure and gravitational forces with no net vertical acceleration on larger scales. However, on smaller scales such as urban scales, the hydrostatic assumption cannot be made. A set of governing equations for compressible non-hydrostatic atmospheres is available to better resolve atmospheric dynamics at smaller scales, and they are more appropriate for finer regional scale and urban scale meteorology. CMAQ's generalized coordinate system is used so that meteorological fields on different vertical coordinates can easily be accommodated and multiple scales can be simulated with the same CTM. The Jacobian used by the generalized coordinate system controls the necessary grid and coordinate transformations (cf., Byun 1999).
CMAQ's current coding structure is based on a modularity level that distinguishes its main driver, science modules, data estimation modules, and control/utility subroutines in the CCTM. The distinction remains at a division between the science models (including submodels for meteorology, emissions, chemistry-transport models), and analysis and visualization subsystems. In the CCTM, the process modules that affect the pollutant concentration fields are classified as follows:
Science Modules:
- Horizontal advection (hadv).
- Vertical advection (vadv).
- Mass conservation adjustments for advection processes (adjc).
- Horizontal diffusion (hdiff).
- Vertical diffusion (vdiff).
- Gas-phase chemical reaction solver (chem).
- Aqueous-phase reactions and cloud mixing (cloud).
- Aerosol dynamics and size distributions (aero).
- Plume chemistry effects (ping).
Control/Utility Modules:
- Model data flow and synchronizing of fractional time steps (ctm).
- Unit conversion (gencoor).
- Initialization (init).
- Process analysis (pa).
Data Estimation Modules:
- Aerosol deposition velocity estimation (aero_depv).
- Photolytic rate computation (phot).
This modularity makes it easier to modify or introduce a specific scientific process in the CCTM. For example, the chem module contains several options for different gas-phase chemistry solvers that can be used to optimize model performance. (More discussion will be provided about these modules later in this chapter.) Without the modular structure, such changes could entail coding modifications throughout the CCTM, thereby increasing the risk of human error. In that case, failing to make the correct modification at even a single location in the source code could lead to erroneous results or program execution failure. With the modularity featured in CMAQ, designing or modifying a model simulation is much more user-friendly, requiring less or no reprogramming.
CMAQ was designed to minimize the potential for model simulation error in several ways. For example, the generalized coordinate system helps to ensure that mass is conserved when transferring data from a non-hydrostatic meteorological model to the CMAQ modeling system. Otherwise, errors in air density and wind fields could induce large errors in the pollutant concentrations predicted by the air quality model. Also, the modularity of the scientific processes in CMAQ makes modifications and adaptations to the user's model more straightforward. The potential for error is minimized because the user is not required to change code or declare variables within the program modules. A common error in previous model formulations overlooked necessary program coding changes when making model modifications. Another feature of CMAQ that supports quality control is process analysis, which is discussed in Section 2.2.6.