Enhancing Time Resolution of Ambient VOC Measurement Data by Machine Learning: From One‐Hour to Five Minutes

Abstract Atmospheric volatile organic compounds (VOCs) significantly impact the environment and public health, necessitating precise, continuous online monitoring. Currently, VOCs monitoring primarily uses Gas Chromatography‐Mass Spectrometry (GC‐MS) and Proton Transfer Reaction‐Time of Flight Mass Spectrometry (PTR‐ToF‐MS). GC‐MS is favored for its accurate compound identification capabilities but is limited by its lower temporal resolution. Conversely, PTR‐ToF‐MS, while achieving minute‐scale resolution by directly ionizing samples, struggles to detect low‐proton‐affinity compounds. Here, based on 5 years of long‐term online monitoring data, we propose the Adaptive Convolutional Tree Ensemble (ACTE) model to surpass current instruments limitations and accurately obtain high‐resolution (5‐min) VOCs. Our results indicate that this model consistently achieves robust predictive accuracy across different major species categories, notably achieving R 2 values of 0.92 and 0.89 for alkanes and alkenes, respectively, which mostly have low‐proton‐affinity. Furthermore, by comparing simulations using different VOCs temporal resolutions in ozone mechanism modeling, we found that models with higher temporal resolution more accurately and comprehensively capture the rapidly occurring photochemical reactions, whereas hourly models tend to overlook many details, potentially leading to inaccuracies in understanding the related mechanisms. This study underscores the potential of machine learning to improve monitoring of atmospheric pollutants and enhance our understanding of atmospheric chemical processes.