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, CB6r3_ae7 and Saprc07tic_ae7i, for China. Sensitivity simulations related to precursor reactive organic carbon (ROC) emissions were conducted to investigate the key driving factors of PM_2.5 formation. The results show slight differences in the correlation coefficient (R), mean bias (MB), and normalized mean bias (NMB) values for the three chemical mechanisms when using the traditional primary organic aerosol (POA) inventory. However, when using the full volatility emission inventory, CRACMM shows improvements in R, MB, and NMB values in some regions. CRACMM predicts higher PM_2.5 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 PM_2.5 formation in CRACMM. The SOA from BTX emissions accounts for nearly 50 % of the PM_2.5 changes, while intermediate-volatility organic compounds (IVOC) and SVOCs emissions mainly affect PM_2.5 concentrations through SOA formation. These results indicate that CRACMM, when using the full volatile inventory, can effectively compensate for the underestimation of PM_2.5 mass that may occur with traditional POA treatment, particularly in regions with high photochemical activity and abundant S/IVOC precursors.