Development of an automated photolysis rates prediction system based on machine learning

Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of O1D, NO2, HONO, H2O2, HCHO, and NO3, which are the crucial values for the prediction of the atmospheric oxidation capacity (AOC) and secondary pollutant concentrations such as ozone (O3), secondary organic aerosols (SOA). The J-ML can self-select the optimal “Model + Hyperparameters” without human interference. The evaluated results showed that the J-ML had a good performance to reproduce the J-values where most of the correlation (R) coefficients exceed 0.93 and the accuracy (P) values are in the range of 0.68–0.83, comparing with the J-values from observations and from the tropospheric ultraviolet and visible (TUV) radiation model in Beijing, Chengdu, Guangzhou and Shanghai. The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days, respectively. Compared with O3 concentrations by using J-values from the TUV model, an emission-driven observation-based model (e-OBM) by using the J-values from the J-ML showed a 4 %-12 % increase in R and 4 %–30 % decrease in ME, indicating that the J-ML could be used as an excellent supplement to traditional numerical models. The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values, and the other dominant factors for all J-values were 2-m mean temperature, O3, total cloud cover, boundary layer height, relative humidity and surface pressure.