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Aerosol chemical component: Simulations with WRF-Chem and comparison with observations in Nanjing

机译：气溶胶化学成分：使用WRF-Chem进行的模拟以及与南京观测值的比较

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Secondary inorganic aerosols, including sulfate, nitrate, and ammonium (SNA), are the predominant components of fine particles (PM2.5). Reasonable representations of SNA formation in numerical models can largely improve the predictions of PM2.5 concentrations and effectively help implement emission control strategies. Despite the Atmospheric Pollution Prevention and Control Action Plan has been implemented since 2013, PM2.5 concentration during 2017 in Nanjing, one of the megacities in China, still exceeded the World Health Organization-recommended safe level (35 m(-3)). In this study, WRF-Chem model was applied to simulate aerosol chemical components in PM2.5 during April 2016 and January 2017 in Nanjing, and the simulations are evaluated with in-situ observations. Our results show that the model can reasonably reproduce the temporal variability of PM2.5 in two seasons, but significantly underestimate the sulfate concentrations by 71% (84%) in April (January), and overestimate the nitrate concentrations by 67% (45%) in April (January). The simulated ammonium is overall consistent with the observations. Meanwhile, the model tends to overestimate SO2 concentrations by 20% (74%) in April (January). Several sensitivity studies are conducted to explore the mechanisms for underestimation of sulfate and overestimation of nitrate, and found that the conversion rate of SO2 to sulfate is significantly underestimated in the model. Tripling the gas-phase oxidation rate of SO2 by OH only enhances sulfate by 67% (72%) in April (January), indicating gas-phase oxidation is not the main causes for the underestimations in the model. However, inclusion of SO2 heterogeneous oxidation in aerosol water can largely increase the simulated sulfate by 84% (196%) in April (January), and also better reproduce the diurnal variations of sulfate compared to the reference run. It should be noted that the simulated sulfate is still 47% (53%) lower than the observations in April (January), though inclusion of heterogeneous reaction can substantially improve the simulation performance of SNA.
机译：次级无机气溶胶，包括硫酸盐，硝酸盐和铵盐（SNA），是微粒（PM2.5）的主要成分。数值模型中SNA形成的合理表示可以大大改善PM2.5浓度的预测，并有效地帮助实施排放控制策略。尽管自2013年以来实施了《大气污染预防和控制行动计划》，但2017年南京作为中国特大城市之一的PM2.5浓度仍然超过了世界卫生组织建议的安全水平（35 m（-3））。在这项研究中，WRF-Chem模型被用于模拟2016年4月和2017年1月在南京的PM2.5中的气溶胶化学成分，并通过现场观察评估了模拟结果。我们的结果表明，该模型可以合理地再现两个季节中PM2.5的时间变异性，但显着低估了4月（一月）的硫酸盐浓度71％（84％），而高估了硝酸盐浓度67％（45％）。 ）在四月（一月）。模拟铵与观察值总体一致。同时，该模型倾向于高估4月（1月）的SO2浓度20％（74％）。进行了几项敏感性研究，探讨了低估硫酸盐和高估硝酸盐的机制，发现模型中SO2转化为硫酸盐的转化率大大低估了。用OH将SO2的气相氧化速率提高三倍，仅在4月（1月）使硫酸盐提高67％（72％），这表明气相氧化不是模型中低估的主要原因。但是，在4月（1月）将气溶胶水中的SO2异质氧化包括在内可以使模拟硫酸盐的含量大幅增加84％（196％），并且与参考试验相比，还可以更好地再现硫酸盐的昼夜变化。应该注意的是，尽管包含异质反应可以大大改善SNA的模拟性能，但模拟的硫酸盐仍比4月（1月）的观测值低47％（53％）。

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