Enhancing real-time PM2.5 forecasts: A hybrid approach of WRF-CMAQ model and CNN algorithm

Abstract

As fine particulate matter (PM_2.5) poses significant environmental and human health risks, there is an urgent need for accurate forecasting systems. In Taiwan, the current air quality forecasting (AQF) system based on the Weather Research and Forecasting meteorological model and Community Multiscale Air Quality model provides essential predictions but is limited by biases and computational complexities. This study introduces a convolutional neural network (CNN)-based PM_2.5 forecasting model to enhance prediction accuracy. The CNN model incorporates hourly PM_2.5 concentrations from surface observations and the AQF system, along with synoptic weather patterns (SWPs), to predict PM_2.5 levels up to 72 h in advance. Three CNN models were developed: CNN-BASE (without SWPs), CNN-SWP (with SWPs), and CNN-SWPW (with SWPs and a weighted loss function). Performance assessment reveals a significant reduction in the mean RMSE of 72-h PM_2.5 prediction, from 10.48 μg/m³ with the AQF system to 6.88 μg/m³ with the CNN-BASE model. However, CNN-BASE showed the lowest prediction accuracy for high PM_2.5 concentrations (only 26.2%) due to a small subset of samples. Including SWPs improves the model's ability to capture meteorological influences, enhancing predictions of high PM_2.5 concentrations. Furthermore, CNN-SWPW incorporates a weighted loss function to address imbalanced sample size distributions, further enhancing the accuracy of high PM_2.5 predictions. This study demonstrates the potential of CNNs in operational air quality forecasting.