Application and evaluation of CRACMM v1.0 mechanism in PM _2.5 simulation over China

Abstract. Chemical mechanisms are one of the major sources of bias in chemical transport model simulations, making their improvement a critical step towards enhancing model performance and supporting air quality management and research. In this study, a newly developed chemical mechanism, the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM), integrated into the Community Multiscale Air Quality (CMAQ) modeling system, was evaluated through comparison with two traditional chemical mechanisms, Carbon Bond 6 version r3 with aero7 treatment of SOA (CB6r3_ae7) and State Air Pollution Research Center version 07tc with extended isoprene chemistry and aero7i treatment of SOA (Saprc07tic_ae7i), for China. Sensitivity simulations related to precursor reactive organic carbon (ROC) emissions were conducted to investigate the key driving factors of PM2.5 formation. The results indicate that, when using the traditional primary organic aerosol (POA) inventory, the differences among the three chemical mechanisms are within 0–0.14 for the R, 0–10 µg m−3 for the MB, and within 10 % for the NMB values. However, when the full-volatility emission inventory is applied in January, CRACMM exhibits improved performance in the Pearl River Delta (PRD) region. The MB is reduced by 3.0–7.8 µg m−3. In addition, the NMB decreases by 17 %–23 %, and the root mean square error (RMSE) is reduced by 1–6 µg m−3 compared with simulations using the traditional POA inventory across the four months. CRACMM predicts higher PM2.5 concentrations during spring, summer and autumn, mainly due to enhanced secondary organic aerosol (SOA) formation driven by increased precursor emissions. Benzene–toluene–xylene (BTX) species and semi-volatile organic compound (SVOC) emissions significantly contributed to PM2.5 formation in CRACMM. The SOA from BTX emissions accounts for nearly 50 % of the PM2.5 changes, while intermediate-volatility organic compounds (IVOC) and SVOC emissions mainly affect PM2.5 concentrations through SOA formation. These results indicate that CRACMM, when using the full-volatility inventory, can effectively compensate for the underestimation of PM2.5 mass that may occur with traditional POA treatment, particularly in regions with high photochemical activity and abundant S/IVOC precursors.