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Welcome to the GENOA documentation

GENOA (GENerator of Optimized Atmospheric chemical mechanisms) is an open-source Python framework for automated reduction, analysis, and exploration of atmospheric chemical mechanisms.

It enables users to reduce complex, fully explicit mechanisms, such as those generated by GECKO-A, to compact yet chemically interpretable schemes while preserving key atmospheric processes including secondary organic aerosol (SOA) formation and oxidant chemistry (Ozone and NOx).

The resulting reduced mechanisms can be evaluated using GENOA with coupled 0-D box models and are significantly more computationally efficient for 3-D atmospheric simulations.

Current version

GENOA v3.0

The latest v3 series introduces a scalable and flexible framework with:

  • Compatibility with fully explicit GECKO-A mechanisms
  • Integrated threshold-based and simulation-based reduction workflows
  • Configurable input files for user-defined accuracy, size, and performance
  • Built-in postprocessing and visualization tools for mechanism evaluation

For earlier releases (GENOA v1 and v2) and version history, see the Updates page.

Quick start

  1. Overview – Learn how GENOA works and what it can do.
  2. Installation – Set up GENOA on your system.
  3. Basic Workflow – Walk through GENOA work philosophy.
  4. Test Cases– Explore sample configurations and outputs.
  5. Configuration Reference – Review configurable parameters and file structures.
  6. Postprocessing Gallery – Browse example outputs and visualization options.
  7. External Resources – Learn about compatible models and supporting utilities.

Citation

If you use GENOA v3 in your work, please cite:

Wang, Z., P. L. Carter, W., Lee-Taylor, J., Orlando, J., Ye, Q., Valorso, R., Camredon, M., Aumont, B., and Barsanti, K. (in preparation): GENOA v3: A flexible framework for reduction and exploration of highly detailed chemical mechanisms Geoscientific Model Development (under preparation).

Contact and support

For any questions, bug reports, or suggestions, users are welcome to contact zhizhaow@ucr.edu or use the GitHub Issues and Discussions pages.