CMAQ

CMAQ
Name	CMAQ
Developer	United States Environmental Protection Agency
Initial release	1998
Programming language	Fortran, C, Python
Operating system	Unix, Linux, macOS
License	Open-source (EPA)
Website	EPA CMAQ

CMAQ CMAQ is a regional air quality modeling system developed to simulate atmospheric chemistry, transport, and deposition for tropospheric pollutants. The system is used by researchers and agencies to support air quality management, policy assessment, and regulatory modeling tied to emissions inventories, meteorological predictions, and observational networks. CMAQ integrations commonly interact with atmospheric models, emission processors, and observational datasets to produce concentrations, deposition, and source-attribution metrics.

Overview

CMAQ originated from collaborative efforts involving the United States Environmental Protection Agency, National Oceanic and Atmospheric Administration, University of Iowa, Georgia Institute of Technology, Carnegie Mellon University, and other academic partners. It addresses ozone, particulate matter, acid deposition, and photochemical oxidants through a three-dimensional Eulerian framework tied to gridded meteorological fields from models such as Weather Research and Forecasting Model, MM5, and hindcast products from National Centers for Environmental Prediction. The system supports regulatory applications in jurisdictions including United States, European Union, China, India, and regional programs like AirNow and regional planning commissions. CMAQ outputs are commonly used in conjunction with observational platforms including the Aerosol Robotic Network, AirNow, European Monitoring and Evaluation Programme, and satellite missions like MODIS and TROPOMI.

Model Components and Processes

CMAQ couples modules for advection, diffusion, gas-phase chemistry, aerosol dynamics, aqueous-phase chemistry, and dry and wet deposition. Chemical mechanisms implemented include versions resembling mechanisms developed by groups at California Institute of Technology and Brookhaven National Laboratory, with transition schemes akin to Carbon Bond and SAPRC families. Aerosol treatments draw on approaches from Mie theory implementations used by researchers at Scripps Institution of Oceanography and size-resolved schemes inspired by work at University of California, Davis. Boundary layer and vertical mixing parameterizations align with planetary boundary layer formulations used in National Center for Atmospheric Research modeling efforts. Deposition routines reflect parameterizations utilized in studies by National Atmospheric Deposition Program and acidification assessments seen in contexts like the Clean Air Act amendments.

Input Data and Configuration

CMAQ requires meteorological inputs, emissions inventories, boundary conditions, and chemical initializations. Meteorological preprocessing often employs tools from WRF Preprocessing System and assimilation products from Global Forecast System, while emissions are prepared using processors such as SMOKE (software), national inventories like the National Emissions Inventory, and sectoral datasets from agencies like Environmental Defense Fund studies and industry reports. Boundary conditions may incorporate global chemistry model outputs from GEOS-Chem, TM5, or reanalysis fields from ERA5. Configuration choices include grid resolution, vertical layering comparable to configurations used in European Centre for Medium-Range Weather Forecasts studies, chemical mechanism selection, and aerosol mode definitions similar to those evaluated by Aerosol Research Group teams.

Applications and Use Cases

CMAQ is applied in regulatory attainment demonstrations under standards promulgated by the United States Environmental Protection Agency, policy impact assessments linked to amendments of the Clean Air Act, health risk studies often coordinated with Centers for Disease Control and Prevention analyses, and ecosystem impact work related to National Park Service and United Nations Environment Programme interests. Researchers use CMAQ for source apportionment studies comparable to source-oriented work by Argonne National Laboratory and Lawrence Berkeley National Laboratory, for scenario testing of emission control strategies evaluated in Intergovernmental Panel on Climate Change assessments, and for urban-scale exposure modeling similar to projects by Harvard School of Public Health and Columbia University. Case studies have linked CMAQ outputs to epidemiological analyses conducted by teams at Johns Hopkins University and exposure mapping used by city planning offices in locales such as Los Angeles, Beijing, and London.

Evaluation and Performance

Model evaluation commonly follows protocols developed by groups at Atmospheric Model Evaluation Tool initiatives and intercomparison projects like those organized by the Aerosol Comparisons between Observations and Models (AeroCom). Performance metrics include bias, root-mean-square error, and correlation versus observations from networks such as AirNow, CASTNET, IMPROVE, and measurement campaigns run by NOAA and NASA. Sensitivity analyses often reference benchmarking studies conducted at institutions like Purdue University and University of California, Irvine. Computational performance and scalability are assessed on high-performance computing resources provided by centers such as XSEDE and Argonne Leadership Computing Facility, with parallelization strategies influenced by work at Oak Ridge National Laboratory.

Development, Versions, and Software Integration

CMAQ is maintained through collaborative development involving the United States Environmental Protection Agency, academic partners, and contributors from national laboratories including Pacific Northwest National Laboratory and National Renewable Energy Laboratory. Major releases have integrated updated chemical mechanisms, aerosol modules, and coupling interfaces to models like WRF, GOCART, and global chemistry models such as GEOS-Chem. Software integration leverages toolchains and languages practiced at institutions like Lawrence Livermore National Laboratory (Fortran, C), workflow managers and visualization packages used by NCAR and ESRI, and data formats consistent with NetCDF conventions adopted by World Meteorological Organization-linked projects. Ongoing community development is supported through workshops comparable to meetings at AGU and AMS conferences and through collaborative repositories modeled after practices at GitHub and Bitbucket.

Category:Air quality models