引用本文:张章,孙峰,李倩,姚欢,董欣,刘保献等.2013—2018年北京市PM2.5污染波动特征研究[J].环境监控与预警,2021,13(4):33-39
ZHANG Zhang,SUN Feng,LI Qian,YAO Huan,DONG Xin,LIU Bao-xianet,al.Analysis of Characteristics of PM2.5 Fluctuation in Beijing From 2013 to 2018[J].Environmental Monitoring and Forewarning,2021,13(4):33-39
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2013—2018年北京市PM2.5污染波动特征研究
张章,孙峰,李倩,姚欢,董欣,刘保献等
作者单位
张章1,孙峰1 *,李倩1,姚欢1,董欣1,刘保献1,2 1.北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室北京 1000482.清华大学环境学院北京 100084 
摘要:
利用2013—2018年北京市大气污染物监测数据及气象条件等资料,分析了北京市PM2.5污染波动变化趋势及其影响因素。结果表明,2013—2018年北京市空气质量呈现整体改善趋势,优良天数由2013年的176 d增加至2018年的227 d;重污染天数显著减少,由2013年的58 d逐年递减至2018年的14 d。受污染源排放、特殊气候现象、气象条件等多因素影响,近6年北京市ρ(PM2.5)月均值呈现波动下降趋势,其中秋、冬季波动性更加突显。2014—2016年北京市秋冬季PM2.5污染突出,其中2014年10月、2015年11—12月、2016年12月ρ(PM2.5)月均值均达到中度污染级别;而2017—2018年北京市秋、冬季ρ(PM2.5)月均值均处于优良水平。相关性分析结果显示,地面相对湿度、中层温度与大气污染物呈现较强的正相关性,中层北风频率、地面风速则呈现负相关性。基于上述气象条件及CO、SO2和NO2等气态污染物共同构建的拟合方程对ρ(PM2.5)估算结果较好,多站点的拟合值与实际值的相关系数为0.900~0.947,进一步说明气象条件及相关污染源排放对PM2.5污染具有显著影响。
关键词:  细颗粒物  气象条件  相关性分析  多元线性回归模型  北京
DOI:
分类号:X51
基金项目:大气重污染成因与治理攻关基金资助项目(DQGG0303-01);首都蓝天行动培育科技计划基金资助项目(Z181100005418003,Z191100009119004)
Analysis of Characteristics of PM2.5 Fluctuation in Beijing From 2013 to 2018
ZHANG Zhang,SUN Feng,LI Qian,YAO Huan,DONG Xin,LIU Bao-xianet,al
Abstract:
This study investigated the variation trend of air quality in Beijing and its causes by using the data of air pollutants and meteorological conditions from 2013 to 2018. The results showed that the air quality of Beijing improved obviously in the past six years, there were 227 days reached primary and secondary standards in 2018 compared with 176 days in 2013. Heavy polluted days decreased year by year, from 58 days in 2013 to 14 days in 2018. Due to the influence of pollution source emissions, climate phenomenon and meteorological conditions, the average PM2.5 concentration in Beijing has shown a trend of decrease with fluctuation in the past six years, which is more significantly in autumn and winter. The PM2.5 pollution was significant in the fall and winter of 2014—2016, the average concentration of PM2.5 reached the level of middle pollution in October 2014, November—December 2015 and December 2016. However, the average monthly concentration of PM2.5 did not reached the polluted level in autumn and winter of 2017—2018. In addition, the results of correlation analysis showed that the surface relative humidity and intermediate temperature have a strong positive correlation with air pollutants, while the north wind frequency and surface wind speed have a negative correlation. Based on the meteorological conditions and gaseous pollutants such as CO, SO2 and NO2, the PM2.5 concentration was well reproduced by the multiple fitting model. Correlation coefficients between the calculated values and observed values were ranging from 0.900 to 0.947, which further confirmed that meteorological conditions and pollution source emission have a significant influence on PM2.5 pollution.
Key words:  PM2.5  Meteorological conditions  Correlation analysis  Multivariate regression model  Beijing