| 大气自净容量的算法及关键物理因子分析 |
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| 引用本文： | 许云凡,向伟玲,王自发.大气自净容量的算法及关键物理因子分析[J].气候与环境研究,2022,27(4):458-468. |
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| 作者姓名： | 许云凡 向伟玲 王自发 |
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| 作者单位： | 1.中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室，北京1000292.中国科学院大学，北京1000493.中国科学院城市环境研究所区域大气环境研究卓越创新中心，福建厦门361021 |
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| 基金项目： | 国家重点研发计划2017YFC0213004 |
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| 摘 要： | 为更科学地量化大气对污染物的清除能力，使用WRF-NAQPMS模式对2017年12月进行模拟，对比分析影响大气清除能力的主要关键物理因子，修正A值法和大气自净容量算法的差异，进一步计算大气自净容量余量及各关键物理化学过程的贡献量。结果表明，边界层高度、风廓线、湿清除系数等3个关键物理参数较混合层高度、10 m高度风速、雨洗强度等更适用于量化清除过程；修正A值法和大气自净容量算法虽均能表征大气清除能力的强弱，但前者受目标城市面积影响较大，结果远高于大气自净容量算法；大气自净容量余量与细颗粒物（PM2.5）浓度变化趋势呈负相关，污染越重，大气自净容量亏空越多，其中平流扩散对大气自净容量贡献最大，化学转化过程次之，湿沉降等过程也不可忽视。
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| 关 键 词： | 京津冀地区 大气自净容量 边界层高度 风廓线 湿清除系数 |
| 收稿时间： | 2021-02-08 |
Algorithm of Atmospheric Self-Purification Capacity and Its Critical Physical Factors |
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| Yunfan XU,Weiling XIANG,Zifa WANG.Algorithm of Atmospheric Self-Purification Capacity and Its Critical Physical Factors[J].Climatic and Environmental Research,2022,27(4):458-468. |
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| Authors: | Yunfan XU Weiling XIANG Zifa WANG |
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| Affiliation: | 1.State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 1000292.University of Chinese Academy of Sciences, Beijing 1000493.Center for Excellence in Urban Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian Province 361021 |
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| Abstract: | The air’s capacity in removing air pollutants during December 2017 was scientifically quantified by using NAQPMS (Nested Air Quality Prediction Modeling System). Moreover, critical influential factors were compared to analyze the difference of the atmosphere’s self-cleaning ability between using the Modified A-value Algorithm (MAA) and the Atmospheric Self-Purification Capacity Algorithm (ASPCA). The air self-purification capacity margin and its contribution were also evaluated for the air pollution process. The results show that the planetary boundary layer height, wind profile, and wet scavenging coefficient could describe the atmosphere’s self-cleaning ability more properly than the mixing layer height, wind speed at the height of 10 m, and rain-washing intensity. Both the MAA and ASPCA can represent the atmospheric removal capacity, but the former is highly affected by the city area, whose result is much higher than that of the latter. The atmospheric self-purification capacity margin is negatively correlated with the trend of the PM2.5 concentration, where advection-diffusion contributes the most, followed by the chemical transformation process and finally by the wet deposition processes. |
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