AI-Augmented High-Frequency Reconstruction of Online VOC Observations and Implications for Atmospheric Chemistry Mechanism Modeling

The growing integration of artificial intelligence (AI) and atmospheric observations is opening new opportunities to resolve fast, nonlinear processes in atmospheric chemistry. A key bottleneck is the limited temporal resolution of routine volatile organic compound (VOC) monitoring, which weakens observational constraints on rapid chemical evolution and can bias process-based simulations of secondary pollution. Current VOC measurements rely primarily on gas chromatography–mass spectrometry (GC–MS) and proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). GC–MS is favored for accurate compound identification but is limited by relatively low temporal resolution. Conversely, PTR-ToF-MS can achieve minute-scale resolution by directly ionizing samples, yet it struggles to detect compounds with low proton affinity. Here, based on five years of long-term online monitoring data, we propose an Adaptive Convolutional Tree Ensemble (ACTE) model to overcome the limitations of current instruments and reconstruct VOC concentrations at 5-minute resolution. Our results indicate that ACTE consistently achieves robust predictive accuracy across major chemical classes, with R2 values of 0.92 and 0.89 for alkanes and alkenes, respectively, many of which have relatively low proton affinity. Furthermore, using ozone photochemical simulations driven by VOC inputs at different temporal resolutions, we find that higher-resolution inputs more accurately capture rapidly evolving photochemical reactions, whereas hourly inputs tend to overlook short-term variability, potentially biasing mechanistic interpretation. Our findings demonstrate how machine learning (ML)-enabled temporal super-resolution can bridge routine monitoring and mechanism-based modeling, improving process-level diagnosis of atmospheric chemical evolution.