京津冀及周边地区PM_(2.5)时空变化特征遥感监测分析
|
摘要
为分析京津冀及周边地区的PM_(2.5)时空变化特征,先利用MODIS数据反演1 km分辨率的AOT产品,采用地理加权回归模型实现京津冀及周边地区2016~2017年逐日PM_(2.5)浓度的遥感反演,并在此基础上对多种时间尺度PM_(2.5)浓度合成结果进行验证分析,最后从不同时间尺度对2016年和2017年PM_(2.5)时空变化特征进行了对比分析.结果表明本研究反演的日均、月均和年均这3种时间尺度的PM_(2.5)浓度结果总体上效果较为理想,时间尺度越大,遥感估算的PM_(2.5)效果越好,年均PM_(2.5)结果相对精度达80%以上,并且2016年和2017年同一时间尺度的PM_(2.5)遥感结果精度较为接近.京津冀及周边地区PM_(2.5)分布总体均呈现"冬季>秋季≈春季>夏季"和"南高北低"的季节变化和空间分布趋势.与2016年相比,2017年京津冀及周边地区PM_(2.5)浓度平均下降约9.2%,且高值区范围明显减小,PM_(2.5)浓度高值一般发生在11月和12月,而低值则一般发生在8月.2017年与2016年PM_(2.5)浓度时空变化与2017年的大气污染综合治理攻坚行动巡查和空气质量专项督查活动密切相关,这也能间接说明大气污染减排的成效.
To analyze the spatial and temporal variation characteristics of PM_(2.5) in Beijing-Tianjin-Hebei and its surrounding regions,a1 km resolution AOT product was retrieved from MODIS data and the remote sensing inversion of the PM_(2.5) concentration in BeijingTianjin-Hebei and its surrounding regions was realized using the geographically weighted regression model.On this basis,the synthesis results of multi-timescale PM_(2.5) concentrations were verified and analyzed.Finally,the spatial and temporal variation characteristics of PM_(2.5) in Beijing-Tianjin-Hebei and its surrounding regions between 2016 and 2017 were compared and analyzed using different time scales.The results show that the verification of the PM_(2.5) concentration products of the average daily,monthly,and annual averages are in general good.The larger the time scale is,the better is the PM_(2.5) effect of the remote sensing estimation.The relative accuracy of the annual average PM_(2.5) products is higher than 80%.However,the precision of the PM_(2.5) remote sensing results for 2016 and 2017 is relatively close(at the same time scales).The PM_(2.5) distribution in Beijing-Tianjin-Hebei and its surrounding regions shows a seasonal variation(winter > autum ≈ spring > summer).The spatial distribution is high in the southern but low in the northern part.Compared with 2016,the average PM_(2.5) concentration decreased by ~ 9.2% in 2017.The area with high values was significantly reduced.High PM_(2.5) concentrations occurred in November and December and low concentrations were observed in August.The PM_(2.5) concentration change between 2017 and 2016 is closely related to the comprehensive control crucial action and specific inspection activities of air pollution in 2017,which indirectly account for the effect of the reduction of the atmospheric pollution.
To analyze the spatial and temporal variation characteristics of PM_(2.5) in Beijing-Tianjin-Hebei and its surrounding regions,a1 km resolution AOT product was retrieved from MODIS data and the remote sensing inversion of the PM_(2.5) concentration in BeijingTianjin-Hebei and its surrounding regions was realized using the geographically weighted regression model.On this basis,the synthesis results of multi-timescale PM_(2.5) concentrations were verified and analyzed.Finally,the spatial and temporal variation characteristics of PM_(2.5) in Beijing-Tianjin-Hebei and its surrounding regions between 2016 and 2017 were compared and analyzed using different time scales.The results show that the verification of the PM_(2.5) concentration products of the average daily,monthly,and annual averages are in general good.The larger the time scale is,the better is the PM_(2.5) effect of the remote sensing estimation.The relative accuracy of the annual average PM_(2.5) products is higher than 80%.However,the precision of the PM_(2.5) remote sensing results for 2016 and 2017 is relatively close(at the same time scales).The PM_(2.5) distribution in Beijing-Tianjin-Hebei and its surrounding regions shows a seasonal variation(winter > autum ≈ spring > summer).The spatial distribution is high in the southern but low in the northern part.Compared with 2016,the average PM_(2.5) concentration decreased by ~ 9.2% in 2017.The area with high values was significantly reduced.High PM_(2.5) concentrations occurred in November and December and low concentrations were observed in August.The PM_(2.5) concentration change between 2017 and 2016 is closely related to the comprehensive control crucial action and specific inspection activities of air pollution in 2017,which indirectly account for the effect of the reduction of the atmospheric pollution.
引文
[1]肖璐,郎艺超,夏浪,等.基于多源数据的PM2.5浓度时空分布预测与制图[J].环境科学,2017,38(12):4913-4923.Xiao L,Lang Y C,Xia L,et al.Space-time estimations and mapping of PM2.5fine particulates based on multi-source data[J].Environmental Science,2017,38(12):4913-4923.
[2]郝静,孙成,郭兴宇,等.京津冀内陆平原区PM2.5浓度时空变化定量模拟[J].环境科学,2018,39(4):1455-1465.Hao J,Sun C,Guo X Y,et al.Simulation of the spatiotemporally resolved PM2.5aerosol mass concentration over the inland plain of the Beijing-Tianjin-Hebei region[J].Environmental Science,2018,39(4):1455-1465.
[3]Wang Z F,Chen L F,Tao J H,et al.Satellite-based estimation of regional particulate matter(PM)in Beijing using Vertical-andRH correcting method[J].Remote Sensing of Environment,2010,114(1):50-63.
[4]陈辉,厉青,张玉环,等.基于地理加权模型的我国冬季PM2.5遥感估算方法研究[J].环境科学学报,2016,36(6):2142-2151.Chen H,Li Q,Zhang Y H,et al.Estimations of PM2.5concentrations based on the method of geographically weighted regression[J].Acta Scientiae Circumstantiae,2016,36(6):2142-2151.
[5]薛文博,武卫玲,许艳玲,等.基于WRF模型与气溶胶光学厚度的PM2.5近地面浓度卫星反演[J].环境科学研究,2016,29(12):1751-1758.Xue W B,Wu W L,Xu Y L,et al.Satellite retrieval of nearsurface PM2.5based on WRF model and aerosol optical depth[J].Research of Environmental Sciences,2016,29(12):1751-1758.
[6]徐建辉,江洪.长江三角洲PM2.5质量浓度遥感估算与时空分布特征[J].环境科学,2015,36(9):3119-3127.Xu J H,Jiang H.Estimation of PM2.5concentration over the Yangtze Delta using remote sensing:analysis of spatial and temporal variations[J].Environmental Science,2015,36(9):3119-3127.
[7]Xiao L,Lang Y C,Christakos G.High-resolution spatiotemporal mapping of PM2.5concentrations at Mainland China using a combined BME-GWR technique[J].Atmospheric Environment,2018,173:295-305.
[8]Wu J S,Yao F,Li W F,et al.ⅦRS-based remote sensing estimation of ground-level PM2.5concentrations in BeijingTianjin-Hebei:a spatiotemporal statistical model[J].Remote Sensing of Environment,2016,184:316-328.
[9]马宗伟.基于卫星遥感的我国PM2.5时空分布研究[D].南京:南京大学,2015.Ma Z W.Study on spatiotemporal distributions of PM2.5in China using satellite remote sensing[D].Nanjing:Nanjing University,2015.
[10]谢杨,戴瀚程,花岡達也,等.PM2.5污染对京津冀地区人群健康影响和经济影响[J].中国人口·资源与环境,2016,26(11):19-27.Xie Y,Dai H C,Hanaoka T,et al.Health and economic impacts of PM2.5pollution in Beijing-Tianjin-Hebei area[J].China Population Resources and Environment,2016,26(11):19-27.
[11]环境保护部,国家发展和改革委员会,工业和信息化部,等.关于印发《京津冀及周边地区落实大气污染防治行动计划实施细则》的通知[EB/OL].http://www.zhb.gov.cn/g kml/hbb/bwj/201309/t20130918_260414.htm,2013-09-17.
[12]环境保护部.环境保护部组织开展2017年第一季度空气质量专项督查[EB/OL].http://www.mep.gov.cn/g kml/hbb/qt/201702/t20170214_395837.htm,2017-02-14.
[13]环境保护部,北京市人民政府,天津市人民政府,等.关于开展京津冀及周边地区2017-2018年秋冬季大气污染综合治理攻坚行动巡查工作的通知[EB/OL].http://www.zhb.gov.cn/g kml/hbb/bwj/201708/t20170831_420710.htm,2017-08-28.
[14]Vermote E F,Tanre D,Deuze J L,et al.Second simulation of the satellite signal in the solar spectrum,6S:an overview[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(3):675-686.
[15]刘佳,王利民,杨玲波,等.基于6S模型的GF-1卫星影像大气校正及效果[J].农业工程学报,2015,31(19):159-168.Liu J,Wang L M,Yang L B,et al.GF-1 satellite image atmospheric correction based on 6S model and its effect[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(19):159-168.
[16]陈健,周杰,李雅雯.基于静止卫星GOCI数据的陆地上空气溶胶光学厚度遥感反演[J].遥感技术与应用,2017,32(6):1040-1047.Chen J,Zhou J,Li Y W.Retrieving aerosol optical depth over land based on GOCI data onboard geostationary satellite[J].Remote Sensing Technology and Application,2017,32(6):1040-1047.
[17]王中挺,辛金元,贾松林,等.利用暗目标法从高分一号卫星16 m相机数据反演气溶胶光学厚度[J].遥感学报,2015,19(3):530-538.Wang Z T,Xin J Y,Jia S L,et al.Retrieval of AOD from GF-1 16 m camera via DDV algorithm[J].Journal of Remote Sensing,2015,19(3):530-538.
[18]Levy R C,Mattoo S,Munchak L A,et al.The Collection 6MODIS aerosol products over land and ocean[J].Atmospheric Measurement Techniques,2013,6(11):2989-3034.
[19]Levy R C,Remer L A,Mattoo S,et al.Second-generation operational algorithm:retrieval of aerosol properties over land from inversion of moderate resolution imaging spectroradiometer spectral reflectance[J].Journal of Geophysical Research,2007,112(13):D13211.
[20]Kaufman Y J,TanréD,Remer L A,et al.Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer[J].Journal of Geophysical Research,1997,102(D14):17051-17067.
[21]Hus C N,Jeong M J,Bettenhausen C,et al.Enhanced Deep Blue aerosol retrieval algorithm:the second generation[J].Journal of Geophysical Research,2013,118(16):9296-9315.
[22]Hsu C N,Tsay S C,King M D,et al.Aerosol properties over bright-reflecting source regions[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(3):557-569.
[23]Hsu C N,Tsay S C,King M D,et al.Deep blue retrievals of Asian aerosol properties during ACE-Asia[J].IEEETransactions on Geoscience and Remote Sensing,2006,44(11):3180-3195.
[24]Bilal M,Nichol J E,Nazeer M.Validation of Aqua-MODISC051 and C006 operational aerosol products using AERONETmeasurements over Pakistan[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(5):2074-2080.
[25]王晓君,马浩.新一代中尺度预报模式(WRF)国内应用进展[J].地球科学进展,2011,26(11):1191-1199.Wang X J,Ma H.Progress of application of the weather research and forecast(WRF)model in China[J].Advances in Earth Science,2011,26(11):1191-1199.
[26]石春娥,李耀孙,杨军,等.MM5和WRF对中国东部地区冬季边界层模拟效果比较[J].高原气象,2015,34(2):389-400.Shi C E,Li Y S,Yang J,et al.Comparison of simulations on winter sounding profiles in PBL in East China between WRF and MM5[J].Plateau Meteorology,2015,34(2):389-400.
[27]王丽霞,王颖,赖锡柳,等.WRF模式不同边界层参数化方案模拟兰州冬季边界层高度的研究[J].高原气象,2017,36(1):162-172.Wang L X,Wang Y,Lai X L,et al.Study on the simulation of boundary layer height in Lanzhou in winter using WRF model with different boundary layer parameterization schemes[J].Plateau Meteorology,2017,36(1):162-172.
[28]赵世强,张镭,王治厅,等.利用激光雷达结合数值模式估算兰州远郊榆中地区夏季边界层高度[J].气候与环境研究,2012,17(5):523-531.Zhao S Q,Zhang L,Wang Z T,et al.Boundary layer height estimate in summer over the Lanzhou suburb in the Yuzhong area using lidar measurement and numerical model[J].Climatic and Environmental Research,2012,17(5):523-531.
[29]汪明军.基于WRF模式的长三角地区风能资源评估[D].杭州:浙江大学,2017.
[30]Hu X F,Waller L A,Al-Hamdan M Z,et al.Estimating ground-level PM2.5concentrations in the southeastern U.S.using geographically weighted regression[J].Environmental Research,2013,121:1-10.
[31]Ma Z W,Hu X F,Huang L,et al.Estimating ground-level PM2.5in China using satellite remote sensing[J].Environmental Science&Technology,2014,48(13):7436-7444.
[2]郝静,孙成,郭兴宇,等.京津冀内陆平原区PM2.5浓度时空变化定量模拟[J].环境科学,2018,39(4):1455-1465.Hao J,Sun C,Guo X Y,et al.Simulation of the spatiotemporally resolved PM2.5aerosol mass concentration over the inland plain of the Beijing-Tianjin-Hebei region[J].Environmental Science,2018,39(4):1455-1465.
[3]Wang Z F,Chen L F,Tao J H,et al.Satellite-based estimation of regional particulate matter(PM)in Beijing using Vertical-andRH correcting method[J].Remote Sensing of Environment,2010,114(1):50-63.
[4]陈辉,厉青,张玉环,等.基于地理加权模型的我国冬季PM2.5遥感估算方法研究[J].环境科学学报,2016,36(6):2142-2151.Chen H,Li Q,Zhang Y H,et al.Estimations of PM2.5concentrations based on the method of geographically weighted regression[J].Acta Scientiae Circumstantiae,2016,36(6):2142-2151.
[5]薛文博,武卫玲,许艳玲,等.基于WRF模型与气溶胶光学厚度的PM2.5近地面浓度卫星反演[J].环境科学研究,2016,29(12):1751-1758.Xue W B,Wu W L,Xu Y L,et al.Satellite retrieval of nearsurface PM2.5based on WRF model and aerosol optical depth[J].Research of Environmental Sciences,2016,29(12):1751-1758.
[6]徐建辉,江洪.长江三角洲PM2.5质量浓度遥感估算与时空分布特征[J].环境科学,2015,36(9):3119-3127.Xu J H,Jiang H.Estimation of PM2.5concentration over the Yangtze Delta using remote sensing:analysis of spatial and temporal variations[J].Environmental Science,2015,36(9):3119-3127.
[7]Xiao L,Lang Y C,Christakos G.High-resolution spatiotemporal mapping of PM2.5concentrations at Mainland China using a combined BME-GWR technique[J].Atmospheric Environment,2018,173:295-305.
[8]Wu J S,Yao F,Li W F,et al.ⅦRS-based remote sensing estimation of ground-level PM2.5concentrations in BeijingTianjin-Hebei:a spatiotemporal statistical model[J].Remote Sensing of Environment,2016,184:316-328.
[9]马宗伟.基于卫星遥感的我国PM2.5时空分布研究[D].南京:南京大学,2015.Ma Z W.Study on spatiotemporal distributions of PM2.5in China using satellite remote sensing[D].Nanjing:Nanjing University,2015.
[10]谢杨,戴瀚程,花岡達也,等.PM2.5污染对京津冀地区人群健康影响和经济影响[J].中国人口·资源与环境,2016,26(11):19-27.Xie Y,Dai H C,Hanaoka T,et al.Health and economic impacts of PM2.5pollution in Beijing-Tianjin-Hebei area[J].China Population Resources and Environment,2016,26(11):19-27.
[11]环境保护部,国家发展和改革委员会,工业和信息化部,等.关于印发《京津冀及周边地区落实大气污染防治行动计划实施细则》的通知[EB/OL].http://www.zhb.gov.cn/g kml/hbb/bwj/201309/t20130918_260414.htm,2013-09-17.
[12]环境保护部.环境保护部组织开展2017年第一季度空气质量专项督查[EB/OL].http://www.mep.gov.cn/g kml/hbb/qt/201702/t20170214_395837.htm,2017-02-14.
[13]环境保护部,北京市人民政府,天津市人民政府,等.关于开展京津冀及周边地区2017-2018年秋冬季大气污染综合治理攻坚行动巡查工作的通知[EB/OL].http://www.zhb.gov.cn/g kml/hbb/bwj/201708/t20170831_420710.htm,2017-08-28.
[14]Vermote E F,Tanre D,Deuze J L,et al.Second simulation of the satellite signal in the solar spectrum,6S:an overview[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(3):675-686.
[15]刘佳,王利民,杨玲波,等.基于6S模型的GF-1卫星影像大气校正及效果[J].农业工程学报,2015,31(19):159-168.Liu J,Wang L M,Yang L B,et al.GF-1 satellite image atmospheric correction based on 6S model and its effect[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(19):159-168.
[16]陈健,周杰,李雅雯.基于静止卫星GOCI数据的陆地上空气溶胶光学厚度遥感反演[J].遥感技术与应用,2017,32(6):1040-1047.Chen J,Zhou J,Li Y W.Retrieving aerosol optical depth over land based on GOCI data onboard geostationary satellite[J].Remote Sensing Technology and Application,2017,32(6):1040-1047.
[17]王中挺,辛金元,贾松林,等.利用暗目标法从高分一号卫星16 m相机数据反演气溶胶光学厚度[J].遥感学报,2015,19(3):530-538.Wang Z T,Xin J Y,Jia S L,et al.Retrieval of AOD from GF-1 16 m camera via DDV algorithm[J].Journal of Remote Sensing,2015,19(3):530-538.
[18]Levy R C,Mattoo S,Munchak L A,et al.The Collection 6MODIS aerosol products over land and ocean[J].Atmospheric Measurement Techniques,2013,6(11):2989-3034.
[19]Levy R C,Remer L A,Mattoo S,et al.Second-generation operational algorithm:retrieval of aerosol properties over land from inversion of moderate resolution imaging spectroradiometer spectral reflectance[J].Journal of Geophysical Research,2007,112(13):D13211.
[20]Kaufman Y J,TanréD,Remer L A,et al.Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer[J].Journal of Geophysical Research,1997,102(D14):17051-17067.
[21]Hus C N,Jeong M J,Bettenhausen C,et al.Enhanced Deep Blue aerosol retrieval algorithm:the second generation[J].Journal of Geophysical Research,2013,118(16):9296-9315.
[22]Hsu C N,Tsay S C,King M D,et al.Aerosol properties over bright-reflecting source regions[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(3):557-569.
[23]Hsu C N,Tsay S C,King M D,et al.Deep blue retrievals of Asian aerosol properties during ACE-Asia[J].IEEETransactions on Geoscience and Remote Sensing,2006,44(11):3180-3195.
[24]Bilal M,Nichol J E,Nazeer M.Validation of Aqua-MODISC051 and C006 operational aerosol products using AERONETmeasurements over Pakistan[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(5):2074-2080.
[25]王晓君,马浩.新一代中尺度预报模式(WRF)国内应用进展[J].地球科学进展,2011,26(11):1191-1199.Wang X J,Ma H.Progress of application of the weather research and forecast(WRF)model in China[J].Advances in Earth Science,2011,26(11):1191-1199.
[26]石春娥,李耀孙,杨军,等.MM5和WRF对中国东部地区冬季边界层模拟效果比较[J].高原气象,2015,34(2):389-400.Shi C E,Li Y S,Yang J,et al.Comparison of simulations on winter sounding profiles in PBL in East China between WRF and MM5[J].Plateau Meteorology,2015,34(2):389-400.
[27]王丽霞,王颖,赖锡柳,等.WRF模式不同边界层参数化方案模拟兰州冬季边界层高度的研究[J].高原气象,2017,36(1):162-172.Wang L X,Wang Y,Lai X L,et al.Study on the simulation of boundary layer height in Lanzhou in winter using WRF model with different boundary layer parameterization schemes[J].Plateau Meteorology,2017,36(1):162-172.
[28]赵世强,张镭,王治厅,等.利用激光雷达结合数值模式估算兰州远郊榆中地区夏季边界层高度[J].气候与环境研究,2012,17(5):523-531.Zhao S Q,Zhang L,Wang Z T,et al.Boundary layer height estimate in summer over the Lanzhou suburb in the Yuzhong area using lidar measurement and numerical model[J].Climatic and Environmental Research,2012,17(5):523-531.
[29]汪明军.基于WRF模式的长三角地区风能资源评估[D].杭州:浙江大学,2017.
[30]Hu X F,Waller L A,Al-Hamdan M Z,et al.Estimating ground-level PM2.5concentrations in the southeastern U.S.using geographically weighted regression[J].Environmental Research,2013,121:1-10.
[31]Ma Z W,Hu X F,Huang L,et al.Estimating ground-level PM2.5in China using satellite remote sensing[J].Environmental Science&Technology,2014,48(13):7436-7444.
© 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 |