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The Community Multiscale Air Quality Modeling System (CMAQ)

Description

EPA’s Community Multiscale Air Quality (CMAQ) modeling system is an open-source, regional air quality model used to simulate how pollutants such as ozone, PM2.5, and air toxics form and move in the atmosphere. It combines detailed atmospheric chemistry, emissions, and meteorology to estimate pollutant concentrations and deposition over time and space. The CMAQ website provides the model code, documentation, tutorials, input datasets, and example applications, supporting researchers and agencies in evaluating air quality policies, health and ecosystem impacts, and interactions between air quality and climate.

Questions this resource can help answer

How will specific emission control strategies affect regional ozone and PM?.? levels?
What are the contributions of different sectors or regions to pollution in a given area?
How might future climate or land-use changes influence air quality?
What are the spatial and temporal patterns of pollutant concentrations and deposition across a region?

How do I use this resource?

To use CMAQ, download the modeling system and documentation from EPA’s site, set up the required computing environment (Linux, compilers, libraries), and obtain or generate input fields for meteorology, emissions, and boundary conditions (often using tools like WRF and SMOKE or prebuilt datasets). Configure a modeling domain (time period, grid, vertical layers), compile CMAQ, and run a base-case simulation to reproduce observed air quality. Then create alternative scenarios (e.g., emission controls, climate conditions) and rerun CMAQ, comparing outputs—such as ozone and PM2.5 concentrations and deposition—to observations and between scenarios to evaluate impacts of policies or processes.

Pro tips

Start with an official benchmark case to verify your installation and build familiarity.
Use existing, well-documented input datasets before attempting to create your own.
Keep domains modest at first (smaller region, shorter time) to reduce runtime and debug faster.
Rigorously document your configuration (versions, domain, emissions, met) for reproducibility.
Always perform basic model evaluation (against monitors) before interpreting scenario differences.

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