刘兆东,王宏,沈新勇,彭玥,施义舍. 2020. 京津冀及周边地区冬季能见度与PM2.5和环境湿度的多元回归分析[J]. 气象学报, (0):-, doi:10.11676/qxxb2020.036
京津冀及周边地区冬季能见度与PM2.5和环境湿度的多元回归分析
Multiple regression Analysis of winter Visibility, PM2.5 and Humidity in Beijing-Tianjin-Hebei and its surrounding regions
投稿时间:2019-10-23  修订日期:2020-03-04
DOI:10.11676/qxxb2020.036
中文关键词:  京津冀  PM2.5浓度  相对湿度  大气能见度  多元非线性回归
英文关键词:Beijing-Tianjin-Hebei  PM2.5 concentration  relative humidity  visibility  multiple nonlinear regression
基金项目:国家重点研发计划(2019YFC0214601, 2016YFC0203300),国家自然科学基金(41590874, 41790471, 41530427),中国科学院战略性先导科技专项(XDA20100304)
作者单位E-mail
刘兆东 南京信息工程大学 liuzhaodongkk@163.com 
王宏 中国气象科学研究院 wangh@cma.gov.cn 
沈新勇 南京信息工程大学 shenxy@nuist.edu.cn 
彭玥 中国气象科学研究院 nuist_py@163.com 
施义舍 南京信息工程大学 sys1056002212@163.com 
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中文摘要:
      2013年至今,我国冬季与雾霾相伴的低能见度事件频发,京津冀及周边地区尤为严重。PM2.5浓度与环境湿度是导致低能见度的最关键影响因素。[目的]为了深入研究PM2.5浓度与环境湿度对大气能见度的影响,[资料和方法]利用2017年1月京津冀及周边地区MICAPS气象数据与PM2.5观测数据,运用天气学诊断分析方法讨论了不同相对湿度下PM2.5浓度、环境湿度对冬季能见度变化的相对贡献,按照地理环境与污染程度差异将京津冀及周边地区划分为北京-天津地区与河北-山东地区,建立了PM2.5浓度、露点温度、温度对能见度的多元回归方程,并对2015、2016、2018、2019年冬季能见度进行了回算检验。[结果和结论]研究结果显示:相对湿度低于70%、PM2.5浓度低于75μg/m3时,北京-天津地区与河北-山东地区能见度多高于10km,PM2.5浓度增加是此时能见度迅速降低的主导因素;相对湿度上升(70-85%)和PM2.5浓度增加(75-200μg/m3)的共同作用导致了能见度降低至10-5km;能见度进一步下降至5-2km则更多依赖于相对湿度的进一步升高(85-95%),PM2.5浓度与此时能见度相关性减弱;能见度降低至2km甚至更低主要是由于水汽近饱和状态下(相对湿度为95%以上)的雾滴消光,与PM2.5浓度的变化关系不大。与不分组直接拟合相比,以相对湿度85%为限,分别拟合能见度能够很大程度优化多元回归模型,相对湿度高于85%时能见度拟合值的RMSE从9.2、5.2下降至0.5、0.7,5km以下拟合能见度的误差大幅度减小。按相对湿度85%将数据分组所得的拟合方程对2015至2019年1月能见度估算结果较好,观测值与拟合值相关系数均高于0.91,为雾-霾数值预报系统提供了新的能见度参数化算法。
英文摘要:
      Since 2013, low visibility events have been repeatedly observed in Beijing-Tianjin-Hebei and its surrounding regions. PM2.5 concentration and humidity are considered key factors leading to low visibility. Using the surface meteorological data from MICAPS and PM2.5 concentration observation data from the China Environmental Monitoring Center, the influences of PM2.5 and humidity on visibility under different relative humidity (RH) and pollution levels were investigated. According to the difference in geographical and pollution degree, study region was divided into Beijing-Tianjin and Hebei-Shandong region. The multiple regression equations of visibility, PM2.5 concentration temperature and dew point temperature were established based on data of January 2017, and these equations were tested on the data of January 2015, 2016, 2018 and 2019. Results show that, when RH<70% and PM2.5 concentration<75μg/m3, visibility in Beijing-Tianjin region and Hebei-Shandong region was usually higher than 10km. The increase in PM2.5 concentration was the dominant factor for the rapid decrease in visibility. The combination of the increase in RH (70-85%) and the increase in PM2.5 concentration (75-200μg/m3) resulted in the further decrease of visibility (10-5km). The decrease in visibility (5-2km) mostly depended on the further increase of RH (85-95%). The correlation between PM2.5 concentration and visibility was weakened. The decrease in visibility to 2km or even lower was mainly due to the extinction of droplets under the near saturation of water vapor (RH>95%), and had little relation with changing of PM2.5 concentration. Compared with establishing the visibility fitting equation directly without grouping, establishing the visibility fitting equation according to the RH above or below 85% respectively can greatly optimize the multivariate regression models. RMSE for visibility fittings with RH >85% decreased from 9.2 and 5.2 to 0.5 and 0.7. Visibility in January of 2015, 2016, 2018 and 2019 were well reproduced by these fitting models. Correlation coefficients between the observed visibility and the calculated visibility were all higher than 0.91. It provides new visibility parameterization for the Haze-Fog numerical prediction system.
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