Machine Learning-Based Bias Correction for Improving PM2.5 Prediction Performance Using the Community Multiscale Air Quality (CMAQ) Model

This study aims to enhance the prediction performance of PM2.5 concentrations by applying post-processing bias correction to the forecast outputs of the Community Multiscale Air Quality (CMAQ) model, which utilizes meteorological data. The CMAQ model uses meteorological data and various environmental information to generate air quality predictions, but due to the complexity of atmospheric processes, the model often contains systematic errors. To evaluate and improve the accuracy of these predictions, PM2.5 forecasts from CMAQ were compared with ground-based observations from air quality monitoring stations across South Korea.

Bias correction was performed using Integrated Process Rate (IPR) data, which represents the physical and chemical processes that influence air quality within the CMAQ model. This correction was conducted using linear regression and Deep Neural Network (DNN) methods, both of which have shown promise in improving model predictions in other atmospheric studies.

The training data used for the correction covered air quality data from December 2020 to February 2021, a period representing typical wintertime conditions in South Korea. The bias correction was then applied to data from March 2021. This period is particularly significant as it coincides with South Korea's ‘Seasonal Fine Dust Management System,’ which is designed to address high levels of PM2.5 pollution during the winter months. The analysis used these three months of wintertime data to assess how well the bias correction techniques can improve the CMAQ model's accuracy during this critical pollution season.

The results demonstrate that combining the CMAQ model's predictions with IPR data and machine learning-based bias correction techniques significantly improves the prediction accuracy of PM2.5 concentrations. This study illustrates the potential of post-processing the CMAQ model's forecasts with bias correction methods to refine air quality predictions, especially during periods of elevated pollution. The use of advanced AI techniques such as DNN in this context offers a promising tool for improving the reliability and precision of air quality predictions. These improvements are essential for informing public health strategies, air quality management policies, and pollution control measures, particularly during times of high pollution. By improving the accuracy of PM2.5 predictions, this research contributes to more reliable forecasting systems, supporting better decision-making, policy development, and pollution management, which ultimately improves public health outcomes during critical periods of air pollution.

 

key word : CMAQ, Bias Correction, Machine Learning, PM2.5