基于三维数值模型的中国非甲烷烃(NMHCs)和含氧VOCs模拟性能综合评价

Yibo Zhang, Dejia Yin, Shuxiao Wang, Shengyue Li, Bin Yuan, Min Shao, Hong Li, Qinwen Tan, Qing Li, Yanlin Zhang, Guiqian Tang, Chun Zhao, Qiuyan Du, Yun Zhu, Jie Li, Fenfen Zhang and Bin Zhao*, 
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引用次数: 0

摘要

挥发性有机化合物(VOCs),特别是含氧voc (OVOCs),通过自由基的产生严重影响臭氧(O3)的形成。然而,对模拟非甲烷烃(NMHCs)和OVOCs的三维模型的综合评价仍然很少,这阻碍了O3的控制策略。本研究利用2017年至2021年中国12个监测点的每小时观测数据,系统评估了社区多尺度空气质量模型(CMAQv5.3.3)。结果表明,该模型平均低估了NMHCs 22.0%和OVOCs 43.7%。在14种丰富的VOCs组分中,甲醛(HCHO)和酮(PRD2)预测过高,而其他VOCs预测偏低(4.7% ~ 94.2%),模拟OVOC对总VOCs的贡献(8.8 ~ 36.7%)显著低于观测值(16.6 ~ 60.8%)。该模式在不同季节中持续低估了白天的浓度,并显示与观测值相比,夜间峰值的白天浓度下降幅度更大,这表明高估了大气物理扩散或垂直混合过程。在减去NMHCs的模拟偏差(主要归因于排放和大气物理过程的偏差)后的残差分析表明,二次化学形成不足是OVOCs的主要偏差源。灵敏度实验表明,对边界层和地表参数化方案、最小湍流扩散系数(Kzmin)或一次排放的常规调整无法解决白天OVOCs的低估问题。相反,调整白天的涡旋扩散系数值、不稳定大气状态下的局地垂直梯度和OVOCs化学形成的屈服系数可以有效地增强模拟的白天OVOCs浓度,减小模式偏差。该研究强调,为了提高NMHCs和OVOCs模型的性能,应优先考虑更好地描述白天大气扩散或垂直混合过程以及OVOCs的二次化学形成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comprehensive Evaluation of Simulation Performance of Nonmethane Hydrocarbons (NMHCs) and Oxygenated VOCs in China Using a Three-Dimensional Numerical Model

Comprehensive Evaluation of Simulation Performance of Nonmethane Hydrocarbons (NMHCs) and Oxygenated VOCs in China Using a Three-Dimensional Numerical Model

Volatile organic compounds (VOCs), particularly oxygenated VOCs (OVOCs), critically influence ozone (O3) formation through free radical production. However, comprehensive evaluations of three-dimensional models in simulating both nonmethane hydrocarbons (NMHCs) and OVOCs remain scarce, hindering O3 control strategies. This study systematically evaluates the Community Multiscale Air Quality model (CMAQv5.3.3) using hourly observations from 12 monitoring sites across China from 2017 to 2021. Results reveal that the model underestimates NMHCs by 22.0% and OVOCs by 43.7%, on average. Within the 14 abundant OVOC components, formaldehyde (HCHO) and ketones (PRD2) show overpredictions, while other OVOCs are underpredicted (4.7–94.2%), with simulated OVOC contributions to total VOCs (8.8–36.7%) being substantially lower than observations (16.6–60.8%). The model exhibits persistent underestimation in daytime concentrations across seasons and shows stronger declines in daytime concentrations from nighttime peaks compared to observations, which suggests overestimated atmospheric physical diffusion or vertical mixing processes. Residual error analysis after subtracting the simulation bias of NMHCs (primarily attributed to biases in emissions and atmospheric physical processes) highlights inadequate secondary chemical formation as a major bias source for OVOCs. Sensitivity experiments demonstrate that conventional adjustments to boundary layer and surface parametrization schemes, minimum turbulent diffusivity (Kzmin), or primary emissions failed to resolve daytime OVOCs underestimation. On the contrary, adjusting eddy diffusivity values during the day, the local vertical gradient in an unstable atmospheric state, and the yield coefficient for OVOCs chemical formation effectively enhance the simulated daytime concentrations of OVOCs and reduce the model bias. This study highlights that better descriptions of the atmospheric diffusion or vertical mixing processes during the daytime and the secondary chemical formations of OVOCs should be prioritized to improve the performance of NMHCs and OVOCs modeling.

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