Abstract. Halogen radicals (Cl, Br, and I) significantly influence atmospheric oxidation capacity, affecting both O3 formation and destruction. However, understanding of halogen chemistry remains limited. To better investigate comprehensive atmospheric halogen chemistry, we incorporated halogen processes into the Community Multi-scale Air Quality (CMAQ) model: (i) emissions of Cl2, HCl, Br2, and HBr from anthropogenic sources and Br2, I2, HOI, and halocarbons from natural sources and (ii) 177 multi-phase halogen reactions. Model performance was evaluated against observed ClNO2 levels and by comparison with reported ranges of BrO and IO levels. The updated model showed significant improvements in simulating ClNO2 mixing ratios, with the index of agreement (IOA) increasing from 0.41 to 0.66 and mean bias (MB) decreasing from −159.36 to −25.07 ppt at supersites. Furthermore, simulated BrO and IO levels fell within the ranges reported in previous studies. We found that these improvements were driven by four key reactions: (i) ClO self-reaction, (ii) heterogeneous HOBr chemistry, (iii) NO2 uptake, and (iv) revised N2O5 parameterization. Based on our modeling system, we found that the presence of halogen radicals led to changes in the net Ox production rate (P(Ox)), which increased from 3.08 to 3.33 ppb h−1 on land and decreased from 0.21 to 0.07 ppb h−1 over ocean. It was noted that levels of OH, HCHO, and NOx also increased by ∼0.007 ppt (5.5 %), ∼0.03 ppb (1.6 %), and ∼0.29 ppb (2.9 %), respectively, while levels of HO2 and volatile organic compounds (VOCs) decreased by ∼0.45 ppt (5.3 %) and ∼0.71 ppb (5.9 %). These results highlight the importance of accurately representing halogen processes in regional air quality models.

To improve the predictability of concentrations of atmospheric particulate matter, a data assimilation (DA) system using ensemble square root filter (EnSRF) has been developed for the Community Multiscale Air Quality (CMAQ) model. The EnSRF DA method is a deterministic variant of the ensemble Kalman filter (EnKF) method, which means that unlike the EnKF method, it does not add random noise to the observations. To compare the performances of the EnSRF with those of other DA methods, such as EnKF and 3DVAR (three-dimensional variational), these three methods were applied to the same CMAQ model simulations with identical experimental settings. This is the first attempt in the field of chemical DA to compare the EnKF and EnSRF methods. An identical set of surface fine particulate matter (PM_2.5) were assimilated every 6 h by all the DA methods over a CMAQ domain of East Asia, during the period from 01 May to 11 June 2016. In parallel with 'reanalysis experiments', we also carried out '48 h prediction experiments' using the optimized initial conditions produced by the three DA methods. Detailed analyses among the three DA methods were then carried out by comparing both the reanalysis and the prediction outputs with the observed surface PM_2.5 over four regions (i.e., South Korea, the Beijing-Tianjin-Hebei (BTH) region, Shandong province, and Liaoning province). The comparison results revealed that the EnSRF produced the best reanalysis and prediction fields in terms of several statistical metrics. For example, when the 3DVAR, EnKF, and EnSRF methods were used, averaged normalized mean biases (NMBs) decreased by (57.6, 85.6, and 91.8) % in reanalyses and (39.7, 87.6, and 91.5) % in first-day predictions, compared to the CMAQ control experiment (i.e., without DA) over South Korea, respectively. Also, over the three Chinese regions, the EnSRF method outperformed the EnKF and 3DVAR methods.