石家庄市空气污染物波动相关性时变特征研究
|
摘要
基于石家庄市2014年—2016年空气污染物观测数据,建立时间序列滑动窗,对窗体内主要空气污染物浓度的波动计算皮尔逊相关系数,根据其相关性进行粗粒化处理并得到相关关系模态,再以模态为节点,以窗体滑动次序连边,构建石家庄市空气污染物波动相关性时变网络,通过研究网络拓扑性质可得到以下结论:石家庄市PM2.5、CO、NO2、SO2的浓度波动互为正相关出现频次较高;空气污染物浓度波动相关性转化往往以全局相关为媒介,个别指标浓度与其他指标浓度的关联性是从不相关过渡到正相关;主要空气污染物浓度的当天波动相关性倾向于维持前一天波动相关性状态;空气污染物浓度波动相关性的转化会随季节变化而不同。
Based on the observation data of air pollutants in Shijiazhuang city from 2014 to 2016, the time series sliding window is established first, then the Pearson correlation coefficient is calculated for the fluctuation of the main air pollutant concentration in the window, and the coarse graining treatment is carried out according to the correlation. Then the modality is used as a node, and the sliding order of the form is connected, to construct a timevarying network of air pollutant fluctuation correlation in Shijiazhuang city. By studying the network topology properties, the following conclusions can be drawn: The concentration fluctuations of PM2.5, CO, NO2 and SO2 in Shijiazhuang are positive relevant, and its frequency of occurrence is higher. For a long time, the correlation conversion of the main air pollutant concentration fluctuations is often based on the global correlation. In a short time, the correlation between the concentration of individual indicators and the concentration of other indicators is from irrelevant to positive correlation. The day-to-day fluctuation correlation of the main air pollutant concentration tends to maintain the fluctuation correlation state of the previous day. The conversion of the correlation of fluctuations in air pollutant concentration will vary with the seasons.
Based on the observation data of air pollutants in Shijiazhuang city from 2014 to 2016, the time series sliding window is established first, then the Pearson correlation coefficient is calculated for the fluctuation of the main air pollutant concentration in the window, and the coarse graining treatment is carried out according to the correlation. Then the modality is used as a node, and the sliding order of the form is connected, to construct a timevarying network of air pollutant fluctuation correlation in Shijiazhuang city. By studying the network topology properties, the following conclusions can be drawn: The concentration fluctuations of PM2.5, CO, NO2 and SO2 in Shijiazhuang are positive relevant, and its frequency of occurrence is higher. For a long time, the correlation conversion of the main air pollutant concentration fluctuations is often based on the global correlation. In a short time, the correlation between the concentration of individual indicators and the concentration of other indicators is from irrelevant to positive correlation. The day-to-day fluctuation correlation of the main air pollutant concentration tends to maintain the fluctuation correlation state of the previous day. The conversion of the correlation of fluctuations in air pollutant concentration will vary with the seasons.
引文
[1]单文坡,殷永泉,杜世勇,等.夏季城市大气O3浓度影响因素及其相关关系[J].环境科学,2006,27(7):1276-1281.
[2]赵辉,郑有飞,徐静馨,等.乌鲁木齐市大气污染物浓度的变化特征[J].环境化学,2015,34(11):2118-2126.
[3]郭伟,王雁,张怀德,等.山西省太原市6种主要大气污染物变化特征及与气象因子的关系[J].科技与创新,2017,2017(2):34-36.
[4]谢志英,刘浩,唐新明,等.北京市近12年空气污染变化特征及其与气象要素的相关性分析[J].环境工程学报,2015,9(9):4471-4478.
[5]高湘昀,安海忠,方伟.基于复杂网络的时间序列双变量相关性波动研究[J].物理学报,2012,61(9):533-541.
[6]王涛.乌鲁木齐城市群大气质量及其与气象条件的相关性分析[D].乌鲁木齐:新疆大学,2015.
[7]HUANG S,AN H,GAO X,et al.The Multiscale Conformation Evolution of the Financial Time Series[J].Mathematical Problems in Engineering,2015,2015(6):1-9.
[8]周磊,龚志强,支蓉,等.利用复杂网络研究中国温度序列的拓扑性质[J].物理学报,2008,57(11):7380-7389.
[9]蒋晨光,时间序列多标度时间不可逆性及其在金融市场上的应用[D].北京:北京交通大学,2016.
[10]SHI K,LIU C Q,AI N S,et al.Using three methods to investigate time-scaling properties in air pollution indexes time series[J].Nonlinear Analysis Real World Applications,2008,9(2):693-707.
[11]WAHYUNI D A S,WAGE S,HARTONO A.Volatility in GARCHModels of Business Tendency Index[Z].IOP Conference Series:Materials Science and Engineering.300.012033.10.1088/1757-899X/300/1/012033.2018:12-33.
[12]LI H,AN H,LIU X,et al.Price fluctuation in the energy stock market based on fluctuation and co-fluctuation matrix transmission networks[J].Energy,2016,117:73-83.
[13]LAIB M,TELESCA L,KANEVSKI M,et al.Periodic fluctuations in correlation-based connectivity density time series:Application to wind speed-monitoring network in Switzerland[J].Physica A Statistical Mechanics&Its Applications,2018,492:1555-1569.
[14]郭璋,司刚全,黄越辉,等.考虑波动趋势和相关性的多风电场模拟出力数据生成方法[J].西安交通大学学报,2018,52(4):7-14.
[15]邓创,滕立威,徐曼.中国金融状况的波动特征及其宏观经济效应分析[J].国际金融研究,2016,347(3):17-27.
[16]ZHANG H,ZHANG S,WANG P,et al.Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan,China[J].Journal of the Air&Waste Management Association,2017,67(7):776-788.
[17]BAI Y,LI Y,WANG X,et al.Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions[J].Atmospheric Pollution Research,2016,7(3):557-566.
[18]刘朋.灰色理论在西北地区城市大气污染物浓度预测及质量评价中的应用[D].兰州:兰州大学,2010.
[19]陈静,郭卫红,王素丽.石家庄市空气污染预警天气背景分析及模型的建立[J].气象与环境学报,2007,23(6):62-67.
[20]冯刚,孜比布拉·司马义,何芯,等.乌鲁木齐城市群内部大气污染特征及趋势对比[J].环境工程,2016,34(8):79-83.
[21]翟红兵,魏鹏冉,张小林.石家庄市大气污染现状及污染特征分析[J].河北工业科技,2013,30(4):301-305.
[2]赵辉,郑有飞,徐静馨,等.乌鲁木齐市大气污染物浓度的变化特征[J].环境化学,2015,34(11):2118-2126.
[3]郭伟,王雁,张怀德,等.山西省太原市6种主要大气污染物变化特征及与气象因子的关系[J].科技与创新,2017,2017(2):34-36.
[4]谢志英,刘浩,唐新明,等.北京市近12年空气污染变化特征及其与气象要素的相关性分析[J].环境工程学报,2015,9(9):4471-4478.
[5]高湘昀,安海忠,方伟.基于复杂网络的时间序列双变量相关性波动研究[J].物理学报,2012,61(9):533-541.
[6]王涛.乌鲁木齐城市群大气质量及其与气象条件的相关性分析[D].乌鲁木齐:新疆大学,2015.
[7]HUANG S,AN H,GAO X,et al.The Multiscale Conformation Evolution of the Financial Time Series[J].Mathematical Problems in Engineering,2015,2015(6):1-9.
[8]周磊,龚志强,支蓉,等.利用复杂网络研究中国温度序列的拓扑性质[J].物理学报,2008,57(11):7380-7389.
[9]蒋晨光,时间序列多标度时间不可逆性及其在金融市场上的应用[D].北京:北京交通大学,2016.
[10]SHI K,LIU C Q,AI N S,et al.Using three methods to investigate time-scaling properties in air pollution indexes time series[J].Nonlinear Analysis Real World Applications,2008,9(2):693-707.
[11]WAHYUNI D A S,WAGE S,HARTONO A.Volatility in GARCHModels of Business Tendency Index[Z].IOP Conference Series:Materials Science and Engineering.300.012033.10.1088/1757-899X/300/1/012033.2018:12-33.
[12]LI H,AN H,LIU X,et al.Price fluctuation in the energy stock market based on fluctuation and co-fluctuation matrix transmission networks[J].Energy,2016,117:73-83.
[13]LAIB M,TELESCA L,KANEVSKI M,et al.Periodic fluctuations in correlation-based connectivity density time series:Application to wind speed-monitoring network in Switzerland[J].Physica A Statistical Mechanics&Its Applications,2018,492:1555-1569.
[14]郭璋,司刚全,黄越辉,等.考虑波动趋势和相关性的多风电场模拟出力数据生成方法[J].西安交通大学学报,2018,52(4):7-14.
[15]邓创,滕立威,徐曼.中国金融状况的波动特征及其宏观经济效应分析[J].国际金融研究,2016,347(3):17-27.
[16]ZHANG H,ZHANG S,WANG P,et al.Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan,China[J].Journal of the Air&Waste Management Association,2017,67(7):776-788.
[17]BAI Y,LI Y,WANG X,et al.Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions[J].Atmospheric Pollution Research,2016,7(3):557-566.
[18]刘朋.灰色理论在西北地区城市大气污染物浓度预测及质量评价中的应用[D].兰州:兰州大学,2010.
[19]陈静,郭卫红,王素丽.石家庄市空气污染预警天气背景分析及模型的建立[J].气象与环境学报,2007,23(6):62-67.
[20]冯刚,孜比布拉·司马义,何芯,等.乌鲁木齐城市群内部大气污染特征及趋势对比[J].环境工程,2016,34(8):79-83.
[21]翟红兵,魏鹏冉,张小林.石家庄市大气污染现状及污染特征分析[J].河北工业科技,2013,30(4):301-305.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |