This session brings together advances in artificial intelligence, Earth observation, and atmospheric modelling to improve air quality monitoring and emission inversion. We welcome studies on the synergistic uses of satellite-based monitoring and deep learning—ranging from data-driven surrogates and emulators to hybrid physical-AI integration within chemistry transport models, alongside synthetic data approaches. While advances for well-monitored regions are of interest, we particularly encourage applications in resource-constrained, data-scarce, and under-monitored environments where traditional infrastructure is lacking