Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts

Abstract

Ambient ozone (O₃) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological conditions and regional transport can further complicate the O₃ formation processes. The emission-based chemical transport models (CTM) are broadly used to predict O₃ formation, but they may deviate from observations due to input uncertainties such as emissions and meteorological data, in addition to the treatment of O₃ nonlinear chemistry. In this study, an innovative recurrent spatiotemporal deep-learning (RSDL) method with model-monitor coupled convolutional recurrent neural networks (ConvRNN) has been developed to improve O₃ predictions of CTM. The RSDL method was first used to build the ConvRNN within a 24-h scale to characterize the spatiotemporal relationships between the monitored O₃ data and CTM simulations, and then incorporated the recurrent pattern to achieve 72-h multi-site forecasts based on a pilot study over the Pearl River Delta (PRD) region of China. The results showed that the RSDL method predicted O₃ with high accuracy over this case study, with an increase of 27.54% in the correlation coefficient (R) average for all sites as well as an increase in R of 0.14-0.21 for all cities compared to CTM. Moreover, the regional distribution of CTM was further improved by the RSDL predictions with the data fusion technique, which greatly reduced the underpredictions of O₃ concentrations, particularly in high O₃-level areas (concentrations >160 μg/m³), with a 33.55% reduction in the mean absolute error (MAE).

Keywords: Air quality modeling; Data fusion; Deep learning; Ozone photochemistry; Ozone predictions.