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.

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.

Abstract Concentrations of ambient particulate matter (such as PM 2.5 and PM 10 ) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM 2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM 2.5 prediction systems. The short-term predictability of PM 2.5 (and PM 10 ) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM 2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM 2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM 2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM 2.5 prediction systems using only LEO satellite-derived observations.

Abstract. Recent studies have revealed that halogen radicals (Cl, Br, and I) significantly influence atmospheric oxidation capacity, affecting O3 formation or destruction. However, understanding of halogen chemistries remains limited. To better investigate atmospheric halogen chemistries, 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. After developing the model, we examined its performance against observed data. The results demonstrated significant improvements in simulating observed nitryl chloride (ClNO2) mixing ratios at supersites. The index of agreement (IOA) improved from 0.41 to 0.66, and the mean bias (MB) decreased from -159.36 ppt to -25.07 ppt. These improvements were driven by four atmospheric key reactions: (i) ClO + ClO → Cl2; (ii) HOBr + Cl- → BrCl; (iii) different parameterization of γN2O5; and (iv) 2NO2 + Cl- → ClNO + NO3-. We then examined the net Ox production rate (P(Ox)), which increased from 3.08 ppb/h to 3.33 ppb/h on land and decreased from 0.21 ppb/h to 0.07 ppb/h over ocean in the presence of halogen radicals. Further analysis of the impacts of halogen processes on key atmospheric species revealed that levels of OH, HCHO, and NOx increased by ~0.007 ppt (5.5 %), ~0.03 ppb (1.6 %), and ~0.29 ppb (2.9 %), respectively, while levels of HO2 and VOCs decreased by ~0.45 ppt (5.3 %) and ~0.71 ppb (5.9 %), respectively.