京津冀地区PM2.5/PM10浓度时空分布监测与分析
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摘要
针对京津冀地区多年来重工业较多、结构性污染突出等问题,该文充分利用多期扬尘地表和工业企业污染源、交通网络、地理国情地表覆盖数据、气象和地形数据,结合MODIS AOD产品和环境监测数据,采用主成分分析和最佳子集回归方法优选预测变量,构建估算PM2.5和PM10浓度的地理加权回归模型,实现京津冀地区2013、2015和2017年PM2.5/PM10年均浓度空间分布模拟制图,分析PM2.5/PM10年均浓度时空分布。实验结果表明,PM2.5和PM10浓度估算模型的决定系数R2分别为0.76和0.86,平均相对预测误差分别为10.87%和13.54%。
Aiming at the problem that Beijing-Tianjin-Hebei region had become one of the most serious area of air pollution in China,this paper made full use of multi-temporal dust surfaces data,industrial pollution sources data,transportation network,land cover,meteorological and topographic data,combined with MODIS AOD product and air quality monitoring data,principal component analysis and optimal subset regression were utilized to optimize the prediction variables to construct a geographically weighted regression model to estimate PM2.5 and PM10 concentration,the spatial distribution of PM2.5/PM10 was simulated,and the spatial-temporal change was analyzed.The experimental results showed that the determination coefficients R2 of PM2.5 and PM10 concentration estimation models are 0.76 and 0.86 respectively,and the average relative prediction errors are10.87% and 13.54%,respectively.
Aiming at the problem that Beijing-Tianjin-Hebei region had become one of the most serious area of air pollution in China,this paper made full use of multi-temporal dust surfaces data,industrial pollution sources data,transportation network,land cover,meteorological and topographic data,combined with MODIS AOD product and air quality monitoring data,principal component analysis and optimal subset regression were utilized to optimize the prediction variables to construct a geographically weighted regression model to estimate PM2.5 and PM10 concentration,the spatial distribution of PM2.5/PM10 was simulated,and the spatial-temporal change was analyzed.The experimental results showed that the determination coefficients R2 of PM2.5 and PM10 concentration estimation models are 0.76 and 0.86 respectively,and the average relative prediction errors are10.87% and 13.54%,respectively.
引文
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[12]杜强,贾丽艳.SPSS统计分析从入门到精通[M].北京:人民邮电出版社,2014:262-263.(DU Qiang,JIALiyan.SPSS statistical analysis from introduction to proficiency[M].Beijing:Posts and Telecom Press,2014:262-263.)
[13]林承勇.人工神经网络在预测PM2.5浓度中的研究[D].成都:电子科技大学,2016.(LIN Chengyong.Research of the prediction of PM2.5concentrations based on neural network[D].Chengdu:University of Electronic Science and Technology of China,2016.)
[14]OLVERA H A,GARCIA M,LI W W,et al.Principal component analysis optimization of a PM2.5land use regression model with small monitoring network[J].Science of the Total Environment,2012(425):27-34.
[15]PEREZ A,RODRIGUEZ J D,LOZANO J A.Sensitivity analysis of kappa-fold cross validation in prediction error estimation[J].IEEE Trans Pattern Anal Mach Intell,2010,32(3):569-575.
[16]KABACOFF R I.R in Action:data analysis and graphics with R[M].2nd ed.USA:Manning Publications,2015.
[17]MAATTA J S,SCHMIDT D F,TEEMU R.Subset selection in linear regression using sequentially normalized least squares:asymptotic theory[J].Scandinavian Journal of Statistics,2016(43):282-295.
[18]钱莉,杨晓玲,殷玉春,等.最优子集回归在武威市降水预报中的应用[J].干旱区研究,2009,26(6):895-900.(QIAN Li,YANG Xiaoling,YIN Yuchun,et al.Application of optimized subset regression in precipitation forecast in Wuwei city[J].Arid Zone Research,2009,26(6):895-900.)
[19]刘凡.空间视角下的PM2.5浓度精细化估算研究[D].兰州:兰州交通大学,2017.(LIU Fan.Study on fine estimation of PM2.5concentration from the view of spatial perspective[D].Landzhou:Lanzhou Jiaotong University,2017.)
[20]覃文忠.地理加权回归基本理论与研究应用[D].上海:同济大学,2007.(QIN Wenzhong.The basic theoretics and application research on geographically weighted regression[D].Shanghai:Tongji University,2007.)
[21]陈强,朱慧敏,何溶,等.基于地理加权回归模型评估土地利用对地表水质的影响[J].环境科学学报,2017,35(5):1571-1580.(CHEN Qiang,ZHU Huimin,HE Rong,et al.Evaluating the impacts of land use on surface water quality using geographically weighted regression[J].Acta Scientiae Circumstantiae,2017,35(5):1571-1580.)
[22]张国峰,杨立荣,瞿明凯,等.基于地理加权回归克里格的日平均气温插值[J].应用生态学报,2015,26(5):1531-1536.(ZHANG Guofeng,YANG Lirong,QU Mingkai,et al.Interpolation of daily mean temperature by using geographically weighted regression-Kriging[J].Chinese Journal of Applied Ecology,2015,26(5):1531-1536.)
[2]秦珊珊.悬浮颗粒物PM2.5和PM10的统计分析与预测[D].兰州:兰州大学,2014.(QIN Shanshan.Statistical analysis and forecasting of suspended particulate matters,PM10and PM2.5[D].Lanzhou:Lanzhou University,2014.)
[3]World Health Organization World Health Report.Reducing risks,promoting health life[R].Geneva:WHO Press,2002.
[4]王圣.京津冀地区城市环境效率评价及其影响因素分析[D].大连:东北财经大学,2016.(WANG Sheng.Urban environmental efficiency evaluation and its influential factors analysis in Beijing-Tianjin-Hebei Region[D].Dalian:Dongbei University of Fiance and Economics,2016.)
[5]环境保护部.2016年中国环境质量公报[R/OL].(2017-06-06).http:∥www.gov.cn/xinwen/2017-06/06/content_5200281.htm.(Ministry of Environment Protection.Report of environment quality of China in2016[R/OL].(2017-06-06).http:∥www.gov.cn/xinwen/2017-06/06/content_5200281.htm.)
[6]张晓雨.中国中东部地区典型城市大气细颗粒物中化学组成特征及来源解析研究[D].南京:南京大学,2017.(ZHANG Xiaoyu.Study on chemical characteristics and source apportionments of fine particulates in the typical cities over the central and eastern China[D].Nanjing:Nanjing University,2017.)
[7]张玉梅.北京市大气颗粒物污染防治技术和对策研究[D].北京:北京化工大学,2015.(ZHANG Yumei.Study on the prevention technologies and countermeasures for aerosol pollution in Beijing[D].Beijing:Beijing University of Chemical Technology,2015.)
[8]虢俊龙.石家庄霾污染过程大气颗粒物化学组分分析及来源解析[D].北京:北京化工大学,2015.(GUOJunlong.Study of chemical composition features and sources apportionment of atmospheric particulate matter during haze in Shijiazhuang[D].Beijing:Beijing University of Chemical Technology,2015.)
[9]蒋涛,白慧莉,孙宇.抑尘剂控制扬尘污染的研究:以西安市扬尘污染分析为例[J].绿色科技,2013(10):198-203.(JIANG Tao,BAI Huili,SUN Yu.Study on controlling fugitive dust pollution by dust suppressor:a case study of fugitive dust pollution in Xi’an city[J].Journal of Green Science and Technology,2013(10):198-203.)
[10]许珊,邹滨,蒲强,等.土地利用/覆盖的空气污染效应分析[J].地球信息科学学报,2015,17(3):290-299.(XU Shan,ZOU Bin,PU Qiang,et al.Impact analysis of land use/cover on air pollution[J].Journal of GeoInformation Science,2015,17(3):290-299.)
[11]牛琳琳,魏楠,孙琳琳,等.2014年盘锦市气象要素对大气污染物PM2.5的影响[J].现代农业科技,2015(20):180-182.(NIU Linlin,WEI Nan,SUN Linlin,et al.Impact of meteorological elements on air pollutant PM2.5in Panjin city in 2014[J].Modern Agricultural Science and Technology,2015(20):180-182.)
[12]杜强,贾丽艳.SPSS统计分析从入门到精通[M].北京:人民邮电出版社,2014:262-263.(DU Qiang,JIALiyan.SPSS statistical analysis from introduction to proficiency[M].Beijing:Posts and Telecom Press,2014:262-263.)
[13]林承勇.人工神经网络在预测PM2.5浓度中的研究[D].成都:电子科技大学,2016.(LIN Chengyong.Research of the prediction of PM2.5concentrations based on neural network[D].Chengdu:University of Electronic Science and Technology of China,2016.)
[14]OLVERA H A,GARCIA M,LI W W,et al.Principal component analysis optimization of a PM2.5land use regression model with small monitoring network[J].Science of the Total Environment,2012(425):27-34.
[15]PEREZ A,RODRIGUEZ J D,LOZANO J A.Sensitivity analysis of kappa-fold cross validation in prediction error estimation[J].IEEE Trans Pattern Anal Mach Intell,2010,32(3):569-575.
[16]KABACOFF R I.R in Action:data analysis and graphics with R[M].2nd ed.USA:Manning Publications,2015.
[17]MAATTA J S,SCHMIDT D F,TEEMU R.Subset selection in linear regression using sequentially normalized least squares:asymptotic theory[J].Scandinavian Journal of Statistics,2016(43):282-295.
[18]钱莉,杨晓玲,殷玉春,等.最优子集回归在武威市降水预报中的应用[J].干旱区研究,2009,26(6):895-900.(QIAN Li,YANG Xiaoling,YIN Yuchun,et al.Application of optimized subset regression in precipitation forecast in Wuwei city[J].Arid Zone Research,2009,26(6):895-900.)
[19]刘凡.空间视角下的PM2.5浓度精细化估算研究[D].兰州:兰州交通大学,2017.(LIU Fan.Study on fine estimation of PM2.5concentration from the view of spatial perspective[D].Landzhou:Lanzhou Jiaotong University,2017.)
[20]覃文忠.地理加权回归基本理论与研究应用[D].上海:同济大学,2007.(QIN Wenzhong.The basic theoretics and application research on geographically weighted regression[D].Shanghai:Tongji University,2007.)
[21]陈强,朱慧敏,何溶,等.基于地理加权回归模型评估土地利用对地表水质的影响[J].环境科学学报,2017,35(5):1571-1580.(CHEN Qiang,ZHU Huimin,HE Rong,et al.Evaluating the impacts of land use on surface water quality using geographically weighted regression[J].Acta Scientiae Circumstantiae,2017,35(5):1571-1580.)
[22]张国峰,杨立荣,瞿明凯,等.基于地理加权回归克里格的日平均气温插值[J].应用生态学报,2015,26(5):1531-1536.(ZHANG Guofeng,YANG Lirong,QU Mingkai,et al.Interpolation of daily mean temperature by using geographically weighted regression-Kriging[J].Chinese Journal of Applied Ecology,2015,26(5):1531-1536.)
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