Optimization research on air quality numerical model forecasting effects based on deep learning methods

To improve the forecasting effectiveness of numerical air quality models, two deep learning models, DeepPM and APTR, were constructed and trained in this study using PM2.5 and O3 monitoring data, and WRF-Chem numerical forecasts in the south-central Beijing-Tianjin-Hebei region. The optimization effects were evaluated using test datasets and various evaluation metrics. The results show that the PM2.5 and O3 forecast results optimized by the DeepPM, and APTR models significantly outperform the WRF-Chem numerical model for both proximity forecasts over the next 24 h and short- to medium-term forecasts over the next 144 h. The APTR model achieves the best optimization results in proximity forecasting, whereas the DeepPM model has a better overall performance in optimizing the short- and medium-term forecasts. WRF-Chem is superior to other models in predicting high O3 concentration. DeepPM and APTR deep learning models are still significantly better than WRF-Chem for forecasting high concentration bands within the proximity forecast time period. For short- to medium-term forecasting, the DeepPM model outperforms WRF-Chem for forecasting high O3 concentrations. This paper provides a new method and idea for improving the forecasting performance of air quality numerical models.

Automated summary

In this study, two deep learning models, DeepPM and APTR, were constructed and trained to improve the forecasting effectiveness of numerical air quality models. The results showed that these models significantly outperformed the WRF-Chem numerical model in both short-term and medium-term forecasts.