Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. In this study, we estimate some of the model parameters using machine learning and statistics. Our findings identify the level of error and spatial coverage in the O₂ and CO data that are needed to achieve good parameter estimates. We also highlight the benefits of using multiple constraints to calibrate atmospheric chemistry models.