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High concentrations of PM 2.5 in China have caused severe visibility degradation and health problems. However, it is still challenging to accurately predict PM 2.5 and its chemical components in numerical models. In this study, we compared the inorganic aerosol components of PM 2.5 (sulfate, nitrate, and ammonium (SNA)) simulated by the Weather Research and Forecasting model fully coupled with chemistry (WRF-Chem) model with in-situ data in a heavy haze-fog event during November 2018 in Nanjing, China. Comparisons show that the model underestimates sulfate concentrations by 81% and fails to reproduce the significant increase of sulfate from early morning to noon, which corresponds to the timing of fog dissipation that suggests the model underestimates the aqueous-phase formation of sulfate in clouds. In addition, the model overestimates both nitrate and ammonium concentrations by 184% and 57%, respectively. These overestimates contribute to the simulated SNA being 77.2% higher than observed. However, cloud water content is also underestimated which is a pathway for important aqueous-phase reactions. Therefore, we constrained the simulated cloud water content based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Liquid Water Path observations. Results show that the simulation with MODIS-corrected cloud water content increases the sulfate by a factor of 3, decreases the Normalized Mean Bias (NMB) by 53.5%, and reproduces its diurnal cycle with the peak concentration occurring at noon. The improved sulfate simulation also improves the simulation of nitrate, which decreases the simulated nitrate bias by 134%. Although the simulated ammonium is still higher than the observations, corrected cloud water content leads to a decrease of the modelled bias in SNA from 77.2% to 14.1%. The strong sensitivity of simulated SNA concentration to the cloud water content provides an explanation for the simulated SNA bias. Hence, uncertainties in cloud water content can contribute to model biases in simulating SNA which are less frequently explored from a process-level perspective and can be reduced by constraining the model with satellite observations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Ammonia (NH 3 ), as a dominant alkaline gas in the atmosphere, plays a vital role in Chinese urban haze formation process, but its source in urban areas of China is controversial. To identify the sources of urban NH 3 in the semi-arid region of East Asia, real-time measurements of NH 3 and NH 4 + of PM 2.5 in the urban atmosphere of Xi'an, inland China during the winter and summer of 2017 were performed and their stable nitrogen isotope composition were analyzed. NH 3 was 38.0 ± 9.4 μg/m 3 in the summer, which is 1.5 times higher than that in the winter. Concentration of NH 3 in both seasons well correlated with that of PAHs in PM 2.5 and the mass ratio of (BbF + BeP + IP + BghiP) to the total PAHs, suggesting that fossil fuel combustion is an important source of NH 3 in Xi'an. Moreover, diurnal variation pattern of NH 3 was consistent with that of CO in the summer, peaking in the morning and evening rush hours, respectively, further indicating an importance of the contribution of traffic emissions to NH 3 in the city. Based on the source apportionment by using isotope mixing model, we found that 66.4% and 62.5% of NH 3 in the urban atmosphere were contributed by non-agricultural sources in the summer and winter, respectively. Our work revealed that non-agricultural sources dominate the atmospheric NH 3 of Xi'an, where haze pollution is still severe, and suggested that emission controls of non-agricultural NH 3 could be an effective way to mitigate the air pollution problem in the semi-arid region of East Asia., Competing Interests: Declaration of competing interest We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)