Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method.

Chemical transport models (CTMs) are widely used for air pollution modeling, which suffer from significant biases due to uncertainties in simplified parameterization, meteorological fields, and emission inventories. Accurate diagnosis of simulation biases is critical for improvement of models, interpretation of results, and efficient air quality management, especially for the simulation of fine particulate matter (PM_2.5). In this study, an efficient method based on machine learning (ML) was designed to diagnose the drivers of the Community Multiscale Air Quality (CMAQ) model biases in simulating PM_2.5 concentrations from three perspectives of meteorology, chemical composition, and emission sources. The source-oriented CMAQ were used to diagnose influences of different emission sources on PM_2.5 biases. The ML models showed good fitting ability with small performance gap between training and validation. The CMAQ model underestimates PM_2.5 by -19.25 to -2.66 μg/m³ in 2019, especially in winter and spring and high PM_2.5 events. Secondary organic components showed the largest contribution to PM_2.5 simulation bias for different regions and seasons (13.8–22.6 %) among components. Relative humidity, cloud cover, and soil surface moisture were the main meteorological factors contributing to PM_2.5 bias in the North China Plain, Pearl River Delta, and northwestern, respectively. Both primary and secondary inorganic components from residential sources showed the largest contribution (12.05 % and 12.78 %), implying large uncertainties in this sector. The ML-based methods provide valuable complements to traditional mechanism-based methods for model improvement, with high efficiency and low reliance on prior information. [ABSTRACT FROM AUTHOR]