Yin Xiao-Mei, Li Zi-Ming, Xiong Ya-Jun, Qiao Lin, Qiu Yu-Lu, Sun Zhao-Bin, Kou Xing-Xia
Institute of Urban Meteorology, Chinese Meteorological Administration, Beijing 100089, China.
Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, Beijing 100089, China.
Huan Jing Ke Xue. 2019 Mar 8;40(3):1011-1023. doi: 10.13227/j.hjkx.201807067.
During 2014-2017, the number of haze days and air pollution days declined year by year obviously in Beijing. The average mass concentrations of PM, PM, SO, and NO also decreased with the alleviated pollution level. These decreases were more obvious during the heating period, especially in November and December. In order to analyze the reasons for the improvement of air quality, changes of the meteorological factors and emission-reduction have been discussed and quantified in this study. This work was based on the numerical simulation model WRF-CHEM and the large data mining technologies of k-nearest neighbor (KNN) and support vector machines (SVM). Meteorological observations indicated that the mean wind speed of 2017 increased by 7.9% compared with the last three years. The frequency of hourly wind speed higher than 3.4 m·s was the highest (10.6%), and frequency of daily relative humidity higher than 70% was lowest (25.1%), in 2017. Meanwhile, the number of low wind days (daily wind speed lower than 2 m·s), environmental capacity, ventilation index, and height of the boundary layer showed that the diffusion conditions were better in the heating period of 2017 than those of 20142016, especially in November and December. The accumulated precipitation during the non-heating period was 558.3 mm in 2017, which is conducive to pollutant removal and wet deposition. Inter-annual changes of meteorological conditions are important to the air quality. A simulation for December 119 by WRF-CHEM during 2014-2017 was performed, and the results demonstrated that changes of meteorological conditions led to a reduction of the PM concentration of 2017 by 5%, 38%, and 25% compared with that of 2014-2016, respectively. However, it was not possible to quantify the specific contributions of meteorology conditions because of the lack of real emission reduction options. The KNN and SVM models are applied in this study based on the observed meteorology factors, haze days, and pollution days, and it was found that for the reduced haze days and heavy pollution days in 2017, 65.0% could be attributed to emission reduction and 35.0% was caused by improvement of the meteorological conditions.
2014 - 2017年期间，北京的雾霾天数和空气污染天数明显逐年下降。随着污染程度的减轻，PM、PM、SO和NO的平均质量浓度也有所降低。在供暖期，尤其是11月和12月，这些下降更为明显。为了分析空气质量改善的原因，本研究对气象因素的变化和减排情况进行了讨论和量化。这项工作基于数值模拟模型WRF - CHEM以及k近邻（KNN）和支持向量机（SVM）的大数据挖掘技术。气象观测表明，2017年的平均风速比过去三年增加了7.9%。2017年，每小时风速高于3.4 m·s的频率最高（10.6%），日相对湿度高于70%的频率最低（25.1%）。同时，小风天数（日风速低于2 m·s）、环境容量、通风指数和边界层高度表明，2017年供暖期的扩散条件比2014 - 2016年更好，尤其是在11月和12月。2017年非供暖期的累计降水量为558.3毫米，这有利于污染物的去除和湿沉降。气象条件的年际变化对空气质量很重要。利用WRF - CHEM对2014 - 2017年12月1日至19日进行了模拟，结果表明，与2014 - 2016年相比，气象条件的变化导致2017年PM浓度分别降低了5%、38%和25%。然而，由于缺乏实际的减排方案，无法量化气象条件的具体贡献。本研究基于观测到的气象因素、雾霾天数和污染天数应用了KNN和SVM模型，发现2017年雾霾天数和重度污染天数的减少，65.0%可归因于减排，35.0%是由气象条件的改善所致。
