Knowledge based convolutional transformer for joint estimation of PM2.5 and O3 concentrations

Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM_2.5 and O₃ by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM_2.5-O₃ interaction module, and the weighted loss function designed with the correlation between PM_2.5 and O₃ concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM_2.5:RMSE = 6.136 µg/m³, O₃:RMSE = 8.250 µg/m³) and the highest prediction accuracy (PM_2.5:R² = 0.923, O₃:R² = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.