Community Multiscale Air Quality Modeling System

Community Multiscale Air Quality Modeling System
Name	Community Multiscale Air Quality Modeling System
Acronym	CMAQ
Developed by	United States Environmental Protection Agency
Initial release	2000
Latest release	ongoing
Operating system	Cross-platform
Programming language	Fortran, C++
License	Open-source

Community Multiscale Air Quality Modeling System

The Community Multiscale Air Quality Modeling System is a comprehensive atmospheric chemistry and transport modeling suite designed for simulation of air quality, deposition, and chemical transformation across scales from local to continental. It supports policy assessment, scientific research, and regulatory analysis by linking meteorological, emissions, and chemical mechanisms to produce gridded estimates of atmospheric composition. CMAQ has been integrated into workflows used by environmental agencies, research institutions, and international organizations to inform decisions on air pollution, public health, and ecosystem impacts.

Overview

CMAQ is a three-dimensional Eulerian photochemical grid model developed to simulate concentrations of ozone and particulate matter along with gas-phase and aqueous chemistry, aerosol dynamics, and dry/wet deposition. The system couples to meteorological models like Weather Research and Forecasting Model and Community Multiscale Air Quality Modeling System-adjacent preprocessors to ingest meteorology, land use, and emissions, producing outputs used by stakeholders including the United States Environmental Protection Agency, National Oceanic and Atmospheric Administration, European Environment Agency, and academic groups at Massachusetts Institute of Technology, University of California, Berkeley, Colorado State University, and Carnegie Mellon University.

History and development

Development began in the late 1990s within the United States Environmental Protection Agency as part of efforts linked to regulatory programs under statutes such as the Clean Air Act. Early collaborative efforts included partnerships with the National Aeronautics and Space Administration and research centers such as Oak Ridge National Laboratory and Pacific Northwest National Laboratory. Major milestones paralleled advances in computational capability at institutions like Argonne National Laboratory and adoption by international agencies including the World Health Organization for exposure assessment studies. Academic consortia at University of North Carolina at Chapel Hill and University of Iowa contributed modules for aerosol thermodynamics and multiphase chemistry, while software engineering practices drew on methods from projects at Lawrence Livermore National Laboratory.

Model components and architecture

CMAQ architecture comprises modular components: meteorological input preprocessing, emissions processors, chemical mechanism solvers, aerosol dynamics, and deposition modules. The system interfaces with preprocessors such as Meteorology-Chemistry Interface Processor and emissions tools like Sparse Matrix Operator Kernel Emissions and regional inventories from agencies such as the European Monitoring and Evaluation Programme and National Emissions Inventory (United States). Core solvers implement algorithms similar to those used in atmospheric models developed at National Center for Atmospheric Research and numerical libraries employed by Los Alamos National Laboratory. The codebase is maintained with version control and community contribution workflows modeled after practices at GitHub and scientific software initiatives at Princeton University.

Emissions and input data

CMAQ requires detailed gridded emissions data including anthropogenic, biogenic, wildfire, and marine sources. Inputs commonly derive from inventories and datasets produced by Environmental Protection Agency (United States), European Monitoring and Evaluation Programme, Global Fire Emissions Database, and satellite products from Moderate Resolution Imaging Spectroradiometer and Geostationary Operational Environmental Satellite. Emissions processors handle speciation, temporal allocation, and spatial surrogates referencing land cover datasets from National Land Cover Database and transport networks cataloged by agencies like Federal Highway Administration. Boundary conditions are often supplied from global chemistry models maintained at institutions such as Max Planck Institute for Chemistry and National Center for Atmospheric Research.

Chemical and physical processes

The system implements gas-phase mechanisms (including mechanisms developed in collaboration with groups at University of California, Riverside and Yale University), multiphase aqueous chemistry informed by research at Georgia Institute of Technology, and detailed aerosol modules incorporating thermodynamics from University of East Anglia and kinetic treatments used by Scripps Institution of Oceanography. Processes simulated include photochemistry driven by solar radiative transfer algorithms similar to those at NASA Goddard Space Flight Center, heterogeneous reactions on particle surfaces, nucleation, coagulation, condensation/evaporation, and deposition to terrestrial and aquatic surfaces. Parameterizations reflect experimental findings from field campaigns such as Intercontinental Chemical Transport Experiment and ACE (Aerosol Characterization Experiment).

Applications and use cases

CMAQ has been applied to regulatory attainment demonstrations under Clean Air Act planning, public health exposure assessments supported by Centers for Disease Control and Prevention, and epidemiological studies coordinated with researchers at Harvard University and Johns Hopkins University. Other uses include assessment of wildland fire impacts alongside teams at US Forest Service and National Interagency Fire Center, evaluation of shipping emission controls in studies involving the International Maritime Organization, and climate–air quality interactions investigated by Intergovernmental Panel on Climate Change contributors. Urban air quality planning efforts in cities like Los Angeles, Beijing, London, and Delhi have used CMAQ outputs to guide mitigation.

Validation and performance evaluation

Model evaluation employs observational networks including the Air Quality System (AQS), European Air Quality Index monitoring sites, and research measurements from campaigns such as DISCOVER-AQ and Southeast Atmosphere Study. Statistical performance metrics benchmarked against guidelines from the Environmental Protection Agency (United States) and frameworks used by World Meteorological Organization and GEOS-Chem model intercomparisons assess biases, root-mean-square error, and temporal correlations. Computational performance studies compare CMAQ scaling on high-performance computing platforms at XSEDE centers and national supercomputing facilities like Oak Ridge Leadership Computing Facility.

Limitations and future directions

Limitations include uncertainties in emissions inventories noted by analysts at International Energy Agency, representation of sub-grid urban processes studied at Massachusetts Institute of Technology, and computational expense for high-resolution or ensemble applications addressed by efforts at Lawrence Berkeley National Laboratory and cloud computing initiatives at Amazon Web Services. Future directions emphasize coupling with global models such as GEOS-Chem, enhanced treatment of secondary organic aerosol informed by work at Purdue University, integration with exposure and health impact models developed at Imperial College London, and use of data assimilation techniques promoted by European Centre for Medium-Range Weather Forecasts to reduce uncertainties.

Category:Air pollution models