High-resolution mapping of VOCs using the fast space-time Light Gradient Boosting Machine (LightGBM)

Volatile organic compounds (VOCs) play a crucial role in atmospheric chemistry, influencing global climate and posing potential health risks to humans. Accurate spatiotemporal estimation of VOCs is vital for establishing advanced early warning systems and controlling air pollution. However, research on high-resolution spatiotemporal prediction of VOCs concentrations using machine learning is still limited. This study conducted an extensive VOCs observational campaign in Shanghai, improving upon the LightGBM model with the integration of spatiotemporal information, satellite data, meteorological data, emission inventories, and geographical data for VOCs estimation. We achieved a high-precision distribution map of VOCs concentrations in Shanghai (1 km, 1 hour resolution), demonstrating the model’s excellent hourly VOCs estimation performance (R^2 = 0.92). Further analysis with SHapley Additive exPlanations (SHAP) regression revealed the significant contributions of each input feature to VOCs estimation. Compared to many traditional machine learning models, this approach offers lower computational demands in terms of speed and memory. Moreover, the model maintained good hourly spatiotemporal VOCs prediction performance during the COVID-19 lockdown. This research analyzed the spatiotemporal variations of VOCs concentrations in Shanghai, providing a scientific basis for future control of VOCs levels in the city and offering algorithmic support for comprehensive VOCs prediction in other regions.