h i g h l i g h t sNH 4 NO 3 , NH 4 Cl and (NH 4 ) 2 SO 4 were measured in Beijing atmosphere. Sulfate, nitrate and chloride associated with crustal ions were important. ISORROPIA II was used to investigate the gas-aerosol equilibrium characteristics. Crustal species should be carefully considered to improve model prediction. In the winter and summer of 2013e2014, we used a sampling system, which consists of annular denuder, back-up filter and thermal desorption set-up, to measure the speciation of major inorganic salts in aerosols and the associated trace gases in Beijing. This sampling system can separate volatile ammonium salts (NH 4 NO 3 and NH 4 Cl) from non-volatile ammonium salts ((NH 4 ) 2 SO 4 ), as well as the non-volatile nitrate and chloride. The measurement data was used as input of a thermodynamic equilibrium model (ISORROPIA II) to investigate the gaseaerosol equilibrium characteristics. Results show that (NH 4 ) 2 SO 4 , NH 4 NO 3 and NH 4 Cl were the major inorganic salts in aerosols and mainly existed in the fine particles. The sulfate, nitrate and chloride associated with crustal ions were also important in Beijing where mineral dust concentrations were high. About 19% of sulfate in winter and 11% of sulfate in summer were associated with crustal ions and originated from heterogeneous reactions or direct emissions. The nonvolatile nitrate contributed about 33% and 15% of nitrate in winter and summer, respectively. Theoretical thermodynamic equilibrium calculations for NH 4 NO 3 and NH 4 Cl suggest that the gaseous precursors were sufficient to form stable volatile ammonium salts in winter, whereas the internal mixing with sulfate and crustal species were important for the formation of volatile ammonium salts in summer. The results of the thermodynamic equilibrium model reasonably agreed with the measurements of aerosols and gases, but large discrepancy existed in predicting the speciation of inorganic ammonium salts. This indicates that the assumption on crustal species in the model was important for obtaining better understanding on gaseaerosol partitioning and improving the model prediction.
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