I would like to thank both the authors and the reviewers for their collaborative efforts in improving this manuscript. By implementing a leave-one-month-out cross-validation strategy, the revised manuscript successfully addresses previous concerns regarding temporal overfitting. Furthermore, the inclusion of sensitivity tests to validate critical regime thresholds (e.g., temperature and N/S ratio) has solidified the findings on non-linear atmospheric interactions.
The authors have effectively reframed their machine learning approach as a robust statistical diagnostic tool, explicitly grounding the XGBoost-SHAP outputs in established atmospheric chemistry principles. They have also clarified the site-specific nature of their dataset, appropriately positioning their methodology as a scalable framework for future global research. Overall, through rigorous computational validation and careful textual revisions, the authors have fully resolved the reviewers' concerns. The manuscript is scientifically sound, and I am happy to accept it for publication in Atmospheric Chemistry and Physics.