By integrating field measurements with an interpretable ensemble machine learning framework, we comprehensively identified key driving factors of nitro-aromatic compounds (NACs), demonstrated complex interrelationships, and quantified their contributions across different locations. This work provides a reliable modeling approach for recognizing causes of NAC pollution, enhances our understanding of variations of atmospheric NACs, and highlights the necessity of strengthening emission controls.