中文关键词空气污染气象条件排放K近邻算法(KNN)支持向量机(SVM)贡献率 英文关键词air pollutionmeteorological conditionsemissionK-nearest neighbor(KNN)support vector machines(SVM)contribution

作者	单位	E-mail
尹晓梅	中国气象局北京城市气象研究所, 北京 100089 京津冀环境气象预报预警中心, 北京 100089	yinxiaomei@lasg.iap.ac.cn
李梓铭	京津冀环境气象预报预警中心, 北京 100089
熊亚军	京津冀环境气象预报预警中心, 北京 100089	xyajun78@163.com
乔林	京津冀环境气象预报预警中心, 北京 100089
邱雨露	京津冀环境气象预报预警中心, 北京 100089 中国气象局大气化学重点开放实验室, 北京 100081
孙兆彬	中国气象局北京城市气象研究所, 北京 100089
寇星霞	中国气象局北京城市气象研究所, 北京 100089

中文摘要 2014~2017年北京地区霾日数和污染日数逐年减少，PM_2.5、PM₁₀、SO₂和NO₂年平均质量浓度下降，污染程度缓解，采暖期中的11~12月尤为明显.针对空气质量的显著改善，从气象条件的改善和减排措施两方面进行探讨分析，并结合数值模式和大数据挖掘技术实现气象和排放对大气污染贡献率的定量化研究.结果表明，2017年与过去3 a相比，平均风速增加7.9%，≥3.4 m·s^-1的风速频次最高（10.6%），≥70%湿度日占比最小（25.1%）；其中，采暖期与过去3 a同期相比，小风日数减少8.6%、大气环境容量指数和通风指数平均增加约11%，边界层高度以3.2%·a^-1的速率升高，尤其11~12月各要素改善更显著，且该时段内2014年各因子变化与2017年相似.非采暖期（4~10月）累积降水量558.3 mm，仅次于2016年，有利于污染物的清除和湿沉降.利用WRF-CHEM对霾和污染频发的12月进行模拟发现，气象要素的改变导致2017年12月北京PM_2.5质量浓度较2014~2016年同期分别降低5%、38%和25%.因缺少政府实际施行的减排方案，无法利用WRF-CHEM量化气象和减排的具体贡献率，因此借助大数据挖掘算法，基于K近邻算法（KNN）和支持向量机（SVM）模型对气象和减排对空气质量改善的贡献进行评估，结果显示2017年减少的霾日和重污染日，65.0%归因于减排的贡献，35.0%归因为气象条件的改善.可见，气象与生态环境部门应继续加强数据开放共享，科学开展气象条件预报与减排评估. 英文摘要 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_2.5, PM₁₀, SO₂, and NO₂ 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^-1 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^-1), 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 2014~2016, 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 1~19 by WRF-CHEM during 2014-2017 was performed, and the results demonstrated that changes of meteorological conditions led to a reduction of the PM_2.5 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.

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