| 基于数值模拟和统计拟合分析华北冬季一次大范围重污染过程的形成机理 |
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| 引用本文： | 秦楚菲,孙家仁,张文君,廖志恒,滕宇威,陈朋龙,陈静华.基于数值模拟和统计拟合分析华北冬季一次大范围重污染过程的形成机理[J].气候与环境研究,2020,25(2):185-198. |
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| 作者姓名： | 秦楚菲 孙家仁 张文君 廖志恒 滕宇威 陈朋龙 陈静华 |
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| 作者单位： | 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室气象灾害预报预警与评估协同创新中心,南京,210044;生态环境部华南环境科学研究所国家环境保护城市生态环境模拟与保护重点实验室,广州 510655;中山大学大气科学学院,广州 510275;中山大学大气科学学院,广州,510275;生态环境部华南环境科学研究所国家环境保护城市生态环境模拟与保护重点实验室,广州,510655 |
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| 基金项目： | 国家自然科学基金项目41475140、41675073 |
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| 摘 要： | 基于WRF/Chem（Weather Research Forecasting/Chemistry）模式对2015年11月25日至12月2日我国北方一次大范围PM2.5（空气动力学当量直径小于等于2.5 μm的颗粒物，即细颗粒物）重污染过程进行了模拟。与观测资料对比表明，模式能够较好地模拟出PM2.5浓度及气象因素的变化趋势，结果适用于此次污染事件的机理分析。动力、热力条件及化学转化等因素对此次强污染事件形成的机理分析表明，动力因子主要通过表面风和垂直风切变的减弱对此次污染事件造成影响，边界层逆温等热力因子促进了大气稳定性的增强，不利于污染物扩散。依据PM2.5组成成分变化分析可知，硝酸盐、硫酸盐和有机碳在此次事件中含量增加，说明机动车汽车尾气和燃煤排放所致的二次气溶胶生成对PM2.5污染加剧起重要贡献。多元线性回归分析和多因子相对贡献率量化解析结果表明，热力因子在此次污染过程中起主要作用，方差贡献率为52%，动力因子次之，方差贡献率为34%，而化学转化方差贡献率约为14%，说明气象条件，尤其是热力条件是引起此次污染事件的主要原因。
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| 关 键 词： | WRF/Chem模式 重污染 动力因子 热力因子 化学因子 |
| 收稿时间： | 2019/1/11 0:00:00 |
Formation Mechanism of a Large-Scale Heavy Pollution Process in North China in Winter Based on Numerical Simulation and Statistical Fitting |
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| QIN Chufei,SUN Jiaren,ZHANG Wenjun,LIAO Zhiheng,TENG Yuwei,CHEN Penglong and CHEN Jinghua.Formation Mechanism of a Large-Scale Heavy Pollution Process in North China in Winter Based on Numerical Simulation and Statistical Fitting[J].Climatic and Environmental Research,2020,25(2):185-198. |
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| Authors: | QIN Chufei SUN Jiaren ZHANG Wenjun LIAO Zhiheng TENG Yuwei CHEN Penglong and CHEN Jinghua |
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| Abstract: | Using the WRF/Chem (Weather Research Forecasting/Chemistry) model, a large-scale PM2.5 heavy pollution process in northern China from 25 November to 2 December 2015 was simulated. Comparisons to observations show that the model can realistically capture the magnitude and variation of PM2.5 and meteorological factors, and can be used for the mechanism analysis of this pollution event. This paper further analyzed the mechanism of the strong pollution event from the aspects of dynamics, thermo-meteorological conditions, and chemical transformation. The results show that the dynamic factors mainly affect the pollution event through weakening of the surface wind and vertical wind shear. Thermal factors, such as a boundary layer inversion, promote the enhancement of the atmospheric stability, which is not conducive to pollutant diffusion. Based on the analysis of the PM2.5 composition, the nitrate, sulfate, and organic carbon content increased in this event, indicating that the secondary aerosol formation caused by vehicle exhaust and coal combustion contributes greatly to the PM2.5 pollution. To identify the main factors causing this pollution event, we used multiple linear regression and relative contribution rate accounting methods to quantify the multi-factor analysis. The results show that the thermal factors play a major role in the pollution process, with a variance contribution of 52%, dynamic factor of 34%, and a chemical transformation variance contribution of 14%, indicating that adverse meteorological conditions, especially thermal conditions, are the main causes of the pollution event. |
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| Keywords: | WRF-Chem model Heavy pollution Dynamic factors Thermal factors Chemical factors |
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