基于WRF-Chem对成都地区一次重度雾霾过程的模拟研究
A WRF-Chem Simulation on a Severe Haze Process in Chengdu
DOI: 10.12677/AEP.2019.96104, PDF,    科研立项经费支持
作者: 韩沛沛:成都信息工程大学,大气科学学院,四川 成都
关键词: 重度雾霾WRF-Chem排放源成都Heavy Haze WRF-Chem Emission Source Chengdu
摘要: 本文使用ERA-I再分析数据驱动新一代大气预报模式Weather Research and Forecasting Model with Chemistry V3.9 (WRF-Chem V3.9)对2017年1月22~25日发生在成都的一次重度雾霾过程进行数值模拟研究,在使用观测数据检验模式性能后,对比了全球排放源和清华大学排放源等不同人为排放源对模拟结果的影响,并探讨了大气化学过程对雾霾过程的影响。论文得出以下结论:两种不同排放源的模拟结果对PM2.5、PM10、CO、SO2等大气污染物的模拟效果有待进一步改进,其中,采用清华大学源的模拟效果在模拟趋势和量级上优于全球排放源;两种不同排放源的敏感性试验输出的2 m气温模拟值与实测值的相关性较好,模拟误差在3℃~6℃以内,与控制性试验的模拟值相比,敏感性试验的模拟值偏低,体现了化学过程的降温效应;含有化学过程的模拟有使2 m气温,边界层高度,10 m风速,感热通量和潜热通量降低的趋势,表明大气化学过程使得风速减少,对流减弱,感热通量和潜热通量降低,边界层高度降低,从而使污染物浓度进一步增加,反映了大气化学过程与污染物浓度的正反馈关系。相较于全球源的结果,采用清华大学源的敏感性试验结果更明显。
Abstract: This paper uses ERA-I reanalysis data to drive a new generation of atmospheric forecasting model, Weather Research and Forecasting Model with Chemistry V3.9 (WRF-Chem V3.9). In a severe haze process in Chengdu on January 22~25, 2017, numerical simulation studies were carried out. After using the observation data to test the performance of the model, the effects of different anthropo-genic emission sources such as global emission sources and Tsinghua University emission sources on the simulation results were compared, and the effects of atmospheric chemical processes on the haze process were discussed. The paper draws the following conclusions: the simulation results of two different emission sources for the simulation of PM2.5, PM10, CO, SO2 and other atmospheric pollutants need to be further improved, in which the simulation effect of Tsinghua University source is used in the simulation trend and magnitude, it is superior to the global emission source; the 2 m temperature simulation value of the sensitivity test output of two different emission sources has a good correlation with the measured value, and the simulation error is within 3˚C - 6˚C. Compared with the simulated value of the control test, the simulation value of the sensitivity test is low, which reflects the cooling effect of the chemical process. The simulation with chemical process has the tendency of 2 m temperature, boundary layer height, 10 m wind speed, sensible heat flux and latent heat flux, indicating atmospheric chemical process. The wind speed is reduced, the convection is weakened, the sensible heat flux and the latent heat flux are reduced, and the boundary layer height is lowered, so that the pollutant concentration is further increased, reflecting the positive feedback relationship between the atmospheric chemical process and the pollutant concentration. Compared with the results of global sources, the sensitivity test results of Tsinghua University sources are more obvious.
文章引用:韩沛沛. 基于WRF-Chem对成都地区一次重度雾霾过程的模拟研究[J]. 环境保护前沿, 2019, 9(6): 794-803. https://doi.org/10.12677/AEP.2019.96104

参考文献

[1] 郭晓梅. 四川盆地空气质量气候特征及其大地形影响效应的观测模拟研究[D]: [硕士学位论文]. 南京: 南京信息工程大学, 2016.
[2] Dalia, G. and Zhang, J. (2014) ‘Effortless Perfection:’ Do Chinese Cities Manipulate Air Pollu-tion Data? Journal of Environmental Economics and Management, 68, 203-205. [Google Scholar] [CrossRef
[3] 徐莉莉. 北京市秋冬季雾霾污染特征及来源研究[D]: [硕士学位论文]. 北京: 清华大学, 2017.
[4] 刘馨语. 四川盆地雾霾特征及光学特征[D]: [硕士学位论文]. 成都: 成都信息工程大学, 2018.
[5] 张颖, 刘志红, 吕晓彤, 钱骏, 向卫国. 四川盆地一次污染过程的WRF模式参数化方案最优配置[J]. 环境科学学报, 2016, 36(8): 2819-2826.
[6] 祖繁. 大气污染过程中气溶胶辐射反馈效应的数值模拟研究[D]: [硕士学位论文]. 南京: 南京信息工程大学, 2013.
[7] 周广强, 谢英, 吴剑斌, 余钟奇, 常炉予, 高伟. 基于WRF-Chem模式的华东区域PM2.5预报及偏差原因[J]. 中国环境科学, 2016, 36(8): 2251-2259.
[8] 徐敬, 陈丹, 赵秀娟, 陈敏, 崔应杰, 张方健. RMAPS Chem V1.0系统SO2排放清单优化效果评估[J]. 应用气象学报, 2019, 30(2): 164-176.
[9] 宁浩翔, 张少波, 韩沛沛. 成都地区一次重度雾霾过程的数值模拟研究[J]. 气候变化研究快报, 2019, 8(3): 337-349.
[10] 徐敬, 张小玲, 蔡旭晖, 赵秀娟, 苏捷, 张自银, 温维. 基于敏感源分析的动态大气污染排放方案模拟[J]. 应用气象学报, 2016, 27(6): 654-665.
[11] Małgorzata, W., Maciej, K., Mariusz, P. and Jakub, G. (2019) Assimilation of PM2.5 Ground Base Observations to Two Chemical Schemes in WRF-Chem—The Results for the Winter and Summer Period. Atmospheric Environment, 200, 178-189. [Google Scholar] [CrossRef
[12] 马欣, 陈东升, 温维, 盛黎, 胡江凯, 佟华, 尉鹏. 应用WRF-Chem探究气溶胶污染对区域气象要素的影响[J]. 北京工业大学学报, 2016, 42(2): 285-295.
[13] 谭敏, 谢晨波, 王邦新, 吴德成, 马晖, 刘东, 王英俭. 北京2014年冬季边界层高度与颗粒物浓度的相关性研究[J]. 红外与激光工程, 2018, 47(7): 187-194.
[14] 杜川利, 唐晓, 李星敏, 陈闯, 彭燕, 董研, 董自鹏. 城市边界层高度变化特征与颗粒物浓度影响分析[J]. 高原气象, 2014, 33(5): 1383-1392.
[15] 贺园园, 胡非, 刘郁珏, 刘磊. 北京地区一次PM2.5重污染过程的边界层特征分析[J]. 气候与环境研究, 2019, 24(1): 61-72.