Bias Correction of Copernicus Atmosphere Monitoring Service (CAMS) Forecasts via Machine and Deep Learning-based Multi-target Regression

Abstract

The significant impact of air pollution on human health and climate change underscores the critical importance of atmospheric chemistry transport modeling. However, the accuracy of the Copernicus Atmosphere Monitoring Service (CAMS) forecasts remains a substantial challenge, particularly evidenced by a pronounced overestimation of SO2 concentrations in the Republic of Korea. While O3 is relatively well-simulated and CAMS undergoes continuous model updates, there is still a clear necessity for accuracy improvements for PM2.5, PM10, NO2, and CO, similar to SO2. Historically, artificial intelligence algorithms have predominantly focused on single-target prediction problems. Nevertheless, recent research has increasingly explored multi-target regression (MTR), an approach that simultaneously considers multiple variables, leading to enhanced generalization performance. This study, therefore, aimed to perform bias correction for CAMS forecasts of PM2.5, PM10, O3, NO2, SO2, and CO in the Korean region using AI-based MTR algorithms. CAMS data from 2016 to 2018 served as the training dataset, with AirKorea ground observations utilized as the target data for model validation. The models were evaluated using 2019 data, assessing performance through metrics including Index of Agreement (IOA), Pearson Correlation Coefficient (R), Root Mean Square Error (RMSE), and Mean Bias (MB). Results demonstrated that both XGBoost and LSTM-based MTR models significantly enhanced forecasting performance across all pollutants compared to CAMS data. XGBoost generally outperformed LSTM in terms of IOA and R across most pollutants, showing improvements of up to 408% and 76% for IOAs of SO2 and PM2.5, respectively compared to CAMS. Both models exhibited substantial reductions in RMSE and MB. While CAMS itself performed better for O3, the MTR models effectively corrected the overestimation of SO2, CO, NO2, and particulate matter by CAMS.