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谷阳阳, 苏贵金, 柴涛, 高丽荣, 刘雅露, 李倩倩, 魏大. 北京地区PM2.5浓度影响因素及估算模型[J]. 环境化学, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
引用本文: 谷阳阳, 苏贵金, 柴涛, 高丽荣, 刘雅露, 李倩倩, 魏大. 北京地区PM2.5浓度影响因素及估算模型[J]. 环境化学, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
shu
GU Yangyang, SU Guijin, CHAI Tao, GAO Lirong, LIU Yalu, LI Qianqian, WEI Da. Influencing factors and prediction model of PM2.5 concentration in Beijing area[J]. Environmental Chemistry, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
Citation: GU Yangyang, SU Guijin, CHAI Tao, GAO Lirong, LIU Yalu, LI Qianqian, WEI Da. Influencing factors and prediction model of PM2.5 concentration in Beijing area[J]. Environmental Chemistry, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
shu

北京地区PM2.5浓度影响因素及估算模型

  • 1.

    中北大学环境与安全工程学院, 太原, 030051;

  • 2.

    中国科学院生态环境研究中心环境化学与 生态毒理学国家重点实验室, 北京, 100085;

  • 3.

    中国科学院大学, 北京, 100049

  • 基金项目:

    国家重点研发计划（2016YFC0202500）和中国科学院青年创新促进会项目资助.

Influencing factors and prediction model of PM2.5 concentration in Beijing area

  • 1.

    School of Environment and Safety Engineering, North University of China, Taiyuan, 030051, China;

  • 2.

    Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, State Key Laboratory of Environmental Chemistry and Ecotoxicology, Beijing, 100085, China;

  • 3.

    University of Chinese Academy of Science, Beijing, 100049, China

  • Fund Project: Supported by the National Key R & D program of China (2016YFC0202500) and Youth Innovation Promotion Association CAS.

  • 摘要: 为研究PM2.5与大气污染物浓度之间的关系以及气象条件对PM2.5浓度的影响，本文运用数学统计方法，对北京顺义区2016年1月—12月PM2.5及大气污染物和气象要素的数据资料进行分析并建立了北京顺义区PM2.5浓度的估算模型.双变量相关性分析的结果表明，PM2.5浓度与PM10、SO2、NO2、O3以及CO等大气污染物浓度与温度、湿度、压强和风速风向等气象条件间呈现强的相关性.建立了PM2.5与单因素拟合模型，其中PM10、NO2和CO与PM2.5浓度拟合模型的R2均大于0.6.识别了对PM2.5浓度有显著影响的二阶、三阶交互作用的因素交叉项.综合考虑单个影响因素与影响因素间交互作用的对PM2.5浓度的影响，采用因子分析方法并对提取的主成分进行回归分析，建立了拟合度R2为0.887的PM2.5浓度估算模型.
    Abstract: This study aims to investigate the relationship between the concentration of PM2.5 and atmospheric pollutants and the influence of meteorological conditions on PM2.5 concentration. Mathematical statistics methods were employed to analyze the data of PM2.5, atmospheric pollutants and meteorology in Shunyi District of Beijing, from January to December of 2016. The bivariate correlation analysis showed that the PM2.5 concentration had a strong correlation with the concentrations of atmospheric pollutants such as PM10, SO2, NO2, O3, CO and the meteorological conditions such as temperature, humidity, pressure, wind speed and wind direction. The fitting models between PM2.5 and single influence factor were established in which R2 related to respective PM10, NO2 and CO was all greater than 0.6. The second and third order of interaction factor cross terms that show significant influence on PM2.5 concentration have been identified. Considering the effect of the single influencing factors and their mutual interactions to the concentration of PM2.5, the factor analysis method was carried out to analyze the extracted principal components. The R2 of PM2.5 concentration estimation model, which was further simulated under considering both the single influencing factors and their mutual interaction, was 0.887.
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  • 收稿日期:  2017-09-14
  • 刊出日期:  2018-03-15
谷阳阳, 苏贵金, 柴涛, 高丽荣, 刘雅露, 李倩倩, 魏大. 北京地区PM2.5浓度影响因素及估算模型[J]. 环境化学, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
引用本文: 谷阳阳, 苏贵金, 柴涛, 高丽荣, 刘雅露, 李倩倩, 魏大. 北京地区PM2.5浓度影响因素及估算模型[J]. 环境化学, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
shu
GU Yangyang, SU Guijin, CHAI Tao, GAO Lirong, LIU Yalu, LI Qianqian, WEI Da. Influencing factors and prediction model of PM2.5 concentration in Beijing area[J]. Environmental Chemistry, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
Citation: GU Yangyang, SU Guijin, CHAI Tao, GAO Lirong, LIU Yalu, LI Qianqian, WEI Da. Influencing factors and prediction model of PM2.5 concentration in Beijing area[J]. Environmental Chemistry, 2018, 37(3): 397-409. doi: 10.7524/j.issn.0254-6108.2017091403
shu

北京地区PM2.5浓度影响因素及估算模型

  • 1.  中北大学环境与安全工程学院, 太原, 030051;
  • 2.  中国科学院生态环境研究中心环境化学与 生态毒理学国家重点实验室, 北京, 100085;
  • 3.  中国科学院大学, 北京, 100049
  • 收稿日期:  2017-09-14
基金项目:

国家重点研发计划（2016YFC0202500）和中国科学院青年创新促进会项目资助.

摘要: 为研究PM2.5与大气污染物浓度之间的关系以及气象条件对PM2.5浓度的影响，本文运用数学统计方法，对北京顺义区2016年1月—12月PM2.5及大气污染物和气象要素的数据资料进行分析并建立了北京顺义区PM2.5浓度的估算模型.双变量相关性分析的结果表明，PM2.5浓度与PM10、SO2、NO2、O3以及CO等大气污染物浓度与温度、湿度、压强和风速风向等气象条件间呈现强的相关性.建立了PM2.5与单因素拟合模型，其中PM10、NO2和CO与PM2.5浓度拟合模型的R2均大于0.6.识别了对PM2.5浓度有显著影响的二阶、三阶交互作用的因素交叉项.综合考虑单个影响因素与影响因素间交互作用的对PM2.5浓度的影响，采用因子分析方法并对提取的主成分进行回归分析，建立了拟合度R2为0.887的PM2.5浓度估算模型.

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