基于CSGD的排放清单处理工具研究
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摘要
CSGD (crowd sourcing geographic data,众源地理数据)是通过互联网向大众或相关机构提供的一种开放地理空间数据,具有易获取、时效性好、准确性高等特点,在排放清单时空分配方面具有应用潜力.然而,现有排放清单处理工具不支持CSGD数据直接输入且难以满足排放清单空间分配和空气质量模式所需清单格式,因此,亟待开发一套可以拓展该类数据在排放清单领域应用的工具.以CSGD中的POI (城市设施兴趣点)数据为主要研究对象,基于QGIS平台、C++语言及Python语言,开发了在Windows系统下的ISAT (inventory spatial allocate tool,排放清单空间分配工具)工具及在Windows或Linux系统下的ISAT. M工具.结果表明:ISAT工具以POI数据为基础制作出的空间分配结果与排放源排放强度的空间分布特征的一致性较好; ISAT.M工具输出的inline清单可以作为CMAQ空气质量模式及其DDM敏感性分析模块的输入文件并开展模拟,通过与SMOKE模型的关闭源法模拟结果对比发现,二者在数据及空间分布上呈较好的一致性.研究显示,CSGD数据应用于排放清单空间分配可较好地反映排放源空间分布特征,同时由于此类数据存在信息冗杂、近郊区数据缺失等问题,在应用过程中应注意数据清洗及数据种类的选取工作.
crowd sourcing geographic data( CSGD) is a kind of open geospatial data provided to the public or related organizations through the internet. CSGD has the potential to be applied in the space-time allocation of emission inventories due to its characteristics of easy access,good timeliness and high accuracy. However,the existed emission inventory processing tools do not support the direct input of CSGD data and are difficult to meet the inventory formats required for the space allocation of emission inventories and the air quality models. Therefore,it is urgent to develop a new set of tools to expand the application of CSGD in the field of emission inventories. In the present study,we focused on the point of interest( POI) data in CSGD,and developed an emission inventory processing tool called ISAT based on the QGIS platform,C++ and Python. ISAT included the ISAT emission inventory spatial processing tool based on the Windows system and the ISAT.M tool under the Linux system. The results proved that the spatial distribution results based on the POI data in the ISAT were in good agreement with the actual emission characteristics of the emission source. The inline inventory outputted by the ISAT.M tool could be used as an input for the CMAQ air quality model and its DDM module. Moreover,the simulation results of inline inventory by ISAT.M showed high consistency with the results of Brute Force method of the SMOKE model in both the data and the spatial distribution.This study shows that the CSGD data applied to the emission inventory space allocation can reflect the spatial distribution characteristics of the emission source. At the same time,due to the information redundancy and the lack of data in the suburbs,attention should be paid to the data cleaning and data type selection during the application processes.
crowd sourcing geographic data( CSGD) is a kind of open geospatial data provided to the public or related organizations through the internet. CSGD has the potential to be applied in the space-time allocation of emission inventories due to its characteristics of easy access,good timeliness and high accuracy. However,the existed emission inventory processing tools do not support the direct input of CSGD data and are difficult to meet the inventory formats required for the space allocation of emission inventories and the air quality models. Therefore,it is urgent to develop a new set of tools to expand the application of CSGD in the field of emission inventories. In the present study,we focused on the point of interest( POI) data in CSGD,and developed an emission inventory processing tool called ISAT based on the QGIS platform,C++ and Python. ISAT included the ISAT emission inventory spatial processing tool based on the Windows system and the ISAT.M tool under the Linux system. The results proved that the spatial distribution results based on the POI data in the ISAT were in good agreement with the actual emission characteristics of the emission source. The inline inventory outputted by the ISAT.M tool could be used as an input for the CMAQ air quality model and its DDM module. Moreover,the simulation results of inline inventory by ISAT.M showed high consistency with the results of Brute Force method of the SMOKE model in both the data and the spatial distribution.This study shows that the CSGD data applied to the emission inventory space allocation can reflect the spatial distribution characteristics of the emission source. At the same time,due to the information redundancy and the lack of data in the suburbs,attention should be paid to the data cleaning and data type selection during the application processes.
引文
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[10]HEIPKE C. Crowdsourcing geospatial data[J]. Isprs Journal of Photogrammetry&Remote Sensing,2010,65(6):550-557.
[11]单杰,秦昆,黄长青,等.众源地理数据处理与分析方法探讨[J].武汉大学学报(信息科学版),2014,39(4):390-396.SHAN Jie,QIN Kun,HUANG Changqing,et al. Discussion on the processing and analysis method of crowding source geographic data[J]. Geomatics and Information Science of Wuhan University,2014,39(4):390-396.
[12]BAKILAH M,LIANG S,MOBASHERI A,et al. Fine-resolution population mapping using Open Street Map points-of-interest[J].International Journal of Geographical Information Science,2014,28(9):1940-1963.
[13]王明,胡庆武,李清泉,等.基于位置签到数据的城市分层地标提取[J].计算机学报,2016,39(2):405-413.WANG Ming,HU Qingwu,LI Qingquan,et al. Extracting hierarchical landmark from check-in data[J]. Chinese Journal of Computers,2016,39(2):405-413.
[14]ZHENG Songpan,CHEN Yuzhong,YU Zhiyong,et al. Urban impedance computing model based on points of interest[J].Journal of Zhejiang University,2016,50(6):1189-1195.
[15]INERIS LMD.Documentation of the chemistry-transportation model[EB/OL].Paris:INERIS LMD,2014-07-07[2014-07-07]. http://www.lmd.polytechnique.fr/chimere/docs/CHIMEREdoc2014b.pdf.
[16]The University of North Carolina at Chaple Hill. SMOKE v2. 3Users's Manual[M]. North Carolina:The University of North Carolina at Chaple Hill,2006:2-3.
[17]GODOWITCH J M. Simulations of aerosols and photochemical species with the CMAQ plume-in-grid modeling system[EB/OL].North Carolina:The University of North Carolina at Chaple Hill,2004-10-18[2015-10-20].https://www.cmascenter.org/conference/2004/abstracts/poster/godowitch_abstract.pdf.
[18]郑君瑜,车汶蔚,王兆礼.基于交通流量和路网的区域机动车污染物排放量空间分配方法[J].环境科学学报,2009,29(4):815-821.ZHENG Junyu,CHE Wenwei,WANG Zhaoli. Traffic flow and road network-based spatial allocation of regional mobile source emission inventories[J].Acta Scientiae Circumstantiate,2009,29(4):815-821.
[19]SHERMAN G E.Desktop GIS:mapping the planet with open source tools[M].North Carolina:Pragmatic Bookshelf,2008:67-69.
[20]吴迪.基于开源软件的C++关键语言特性实证研究[D].南京:南京大学,2016:2-4.
[21]何丽娴,甘淑,陈应跃.基于Python语言的空间数据处理[J].价值工程,2014(36):207-209.HE Lixian,GAN Shu,CHEN Yingyue. Programming language python for data processing[J].Value Engineering,2014(36):207-209.
[22]王路希,邓吉秋,李娜,等.基于Python的开源GIS应用开发[J].科技创新与生产力,2015(6):51-53.WANG Luxi,DENG Jiqiu,LI Na,et al. The development and application of open-source GIS based on python[J]. Applied Technology,2015(6):51-53.
[23]YUE T,GAO X,GAO J,et al.Emission characteristics of NOx,CO,NH3,and VOCs from gas-fired industrial boilers based on field measurements in Beijing City, China[J]. Atmospheric Environment,2018,184:1-8.
[24]张双,王阿婧,张增杰,等.北京市人为源氨排放清单的初步建立[J].安全与环境学报,2016,16(2):242-245.ZHANG Shuang,WANG Ajing,ZHANG Zengjie,et al. On creating an anthropogenic ammonia emission inventory in capital Beijing[J].Journal of Safety and Environment,2016,16(2):242-245.
[25]ZHOU Y,CHENG S,LANG J,et al. A comprehensive ammonia emission inventory with high-resolution and its evaluation in the Beijing-Tianjin-Hebei(BTH)Region,China[J]. Atmospheric Environment,2015,106:305-317.
[26]WANG W,BRUYERE C,DUDA M,et al. User's guide for the advanced research WRF(ARW)modeling system version 3. 3[R].Colorado:National Center for Atmospheric Research Technical Note,2011:115-116.
[27]吴志强,叶锺楠.基于百度地图热力图的城市空间结构研究:以上海中心城区为例[J].城市规划,2016,40(4):33-40.WU Zhiqiang,YE Zhongnan. Research on urban spatial structure based on Baidu heat map:a case study on the central city of Shanghai[J].City Planning Review,2016,40(4):33-40.
[28]于丙辰,陈刚.基于腾讯区域热力图的庐山核心景区客流研究[J].国土与自然资源研究,2017(2):83-89.YU Bingchen,CHEN Gang.Research on visitor flows of lushan core scenic area using Tencent regional heatmap[J].Territory&Natural Resources Study,2017(2):83-89.
[29]赵文慧,姜磊,张立坤,等.北京平原区平房冬季燃煤量及污染物排放估算[J].中国环境科学,2017,37(3):859-867.ZHAO Wenhui,JIANG Lei,ZHANG Likun,et al.Estimation of coal consumption and related contamination emission in the plain area around Beijing during winter season[J]. China Environmental Science,2017,37(3):859-867.
[30]王丽涛,张普,杨晶,等.CMAQ-DDM-3D在细微颗粒物(PM2. 5)来源计算中的应用[J].环境科学学报,2013,33(5):1355-1361.WANG Litao,ZHANG Pu,YANG Jing,et al.Application of CMAQDDM-3D in the source analysis of fine particulate matter(PM2. 5)[J].Acta Scientiae Circumstantiate,2013,33(5):1355-1361.
[31]NAPELENOK S L,COHAN D S,HU Y,et al.Decoupled direct 3D sensitivity analysis for particulate matter(DDM-3D/PM)[J].Atmospheric Environment,2006,40(32):6112-6121.
[2]王书肖,邱雄辉,张强,等.我国人为源大气污染物排放清单编制技术进展及展望[J].环境保护,2017(21):21-26.WANG Shuxiao,QIU Xionghui,ZHANG Qiang,et al. Developing anthropogenic air pollutant emission inventory in China:progress and outlook[J].Environmental Protection,2017(21):21-26.
[3]杨柳林,曾武涛,张永波,等.珠江三角洲大气排放源清单与时空分配模型建立[J].中国环境科学,2015,35(12):3521-3534.YANG Liulin,ZENG Wutao,ZHANG Yongbo,et al. Establishment of emission inventory and spatial-temporal allocation model for air pollutant sources in the Pearl River Delta Region[J]. China Environmental Science,2015,35(12):3521-3534.
[4]王堃,高佳佳,田贺忠,等.基于POI兴趣点的排放清单空间分配方法[J].中国环境科学,2017,37(6):2377-2382.WANG Kun,GAO Jiajia,TIAN Hezhong,et al. An emission inventory spatial allocatemethod based on POI data[J]. China Environmental Science,2017,37(6):2377-2382.
[5]MAYER-SCHNBERGER V,CUKIER K.Big data:a revolution that will transform how we live,work,and think[M]. London:John Murray,2013:13-23.
[6]王爽,李炯.基于城市网络空间的POI分布密度分析及可视化[J].城市勘测,2015(1):21-25.WANG Shuang,LI Jiong. Analysis and visualization of POI distribution density based on urban network space[J]. Urban Geotechnical Investigation and Surveying,2015(1):21-25.
[7]HSIEH H P,LIN S D,ZHENG Y.Inferring air quality for station location recommendation based on urban big data[C/OL]//ACM.Proceedings of the 21thSIGKDD conference on knowledge discovery and data mining. Sydney:ACM,2015[2015-08-11]. http://59. 80. 44. 46/users. wpi. edu/~yli15/courses/DS595CS525Spring17/includes/11_Env_Inferring.pdf.
[8]ZHENG Yu,LIU Furui,HSIEH H P.U-Air:when urban air quality inference meets big data[C/OL]//ACM. Proceedings of the 19th SIGKDD international conference knowledge discovery and data mining.Chicago:ACM,2013[2013-08-11]. https://www. microsoft.com/en-us/research/wp-content/uploads/2016/02/U-Air-KDD-cameraready.pdf.
[9]ZHENG Yu,YI Xxiuwen,LI Ming,et al. Forecasting fine-grained air quality based on big data[C]//ACM. SIGKDD international conference on knowledge discovery and data mining.Sydney:ACM,2015:2267-2276.
[10]HEIPKE C. Crowdsourcing geospatial data[J]. Isprs Journal of Photogrammetry&Remote Sensing,2010,65(6):550-557.
[11]单杰,秦昆,黄长青,等.众源地理数据处理与分析方法探讨[J].武汉大学学报(信息科学版),2014,39(4):390-396.SHAN Jie,QIN Kun,HUANG Changqing,et al. Discussion on the processing and analysis method of crowding source geographic data[J]. Geomatics and Information Science of Wuhan University,2014,39(4):390-396.
[12]BAKILAH M,LIANG S,MOBASHERI A,et al. Fine-resolution population mapping using Open Street Map points-of-interest[J].International Journal of Geographical Information Science,2014,28(9):1940-1963.
[13]王明,胡庆武,李清泉,等.基于位置签到数据的城市分层地标提取[J].计算机学报,2016,39(2):405-413.WANG Ming,HU Qingwu,LI Qingquan,et al. Extracting hierarchical landmark from check-in data[J]. Chinese Journal of Computers,2016,39(2):405-413.
[14]ZHENG Songpan,CHEN Yuzhong,YU Zhiyong,et al. Urban impedance computing model based on points of interest[J].Journal of Zhejiang University,2016,50(6):1189-1195.
[15]INERIS LMD.Documentation of the chemistry-transportation model[EB/OL].Paris:INERIS LMD,2014-07-07[2014-07-07]. http://www.lmd.polytechnique.fr/chimere/docs/CHIMEREdoc2014b.pdf.
[16]The University of North Carolina at Chaple Hill. SMOKE v2. 3Users's Manual[M]. North Carolina:The University of North Carolina at Chaple Hill,2006:2-3.
[17]GODOWITCH J M. Simulations of aerosols and photochemical species with the CMAQ plume-in-grid modeling system[EB/OL].North Carolina:The University of North Carolina at Chaple Hill,2004-10-18[2015-10-20].https://www.cmascenter.org/conference/2004/abstracts/poster/godowitch_abstract.pdf.
[18]郑君瑜,车汶蔚,王兆礼.基于交通流量和路网的区域机动车污染物排放量空间分配方法[J].环境科学学报,2009,29(4):815-821.ZHENG Junyu,CHE Wenwei,WANG Zhaoli. Traffic flow and road network-based spatial allocation of regional mobile source emission inventories[J].Acta Scientiae Circumstantiate,2009,29(4):815-821.
[19]SHERMAN G E.Desktop GIS:mapping the planet with open source tools[M].North Carolina:Pragmatic Bookshelf,2008:67-69.
[20]吴迪.基于开源软件的C++关键语言特性实证研究[D].南京:南京大学,2016:2-4.
[21]何丽娴,甘淑,陈应跃.基于Python语言的空间数据处理[J].价值工程,2014(36):207-209.HE Lixian,GAN Shu,CHEN Yingyue. Programming language python for data processing[J].Value Engineering,2014(36):207-209.
[22]王路希,邓吉秋,李娜,等.基于Python的开源GIS应用开发[J].科技创新与生产力,2015(6):51-53.WANG Luxi,DENG Jiqiu,LI Na,et al. The development and application of open-source GIS based on python[J]. Applied Technology,2015(6):51-53.
[23]YUE T,GAO X,GAO J,et al.Emission characteristics of NOx,CO,NH3,and VOCs from gas-fired industrial boilers based on field measurements in Beijing City, China[J]. Atmospheric Environment,2018,184:1-8.
[24]张双,王阿婧,张增杰,等.北京市人为源氨排放清单的初步建立[J].安全与环境学报,2016,16(2):242-245.ZHANG Shuang,WANG Ajing,ZHANG Zengjie,et al. On creating an anthropogenic ammonia emission inventory in capital Beijing[J].Journal of Safety and Environment,2016,16(2):242-245.
[25]ZHOU Y,CHENG S,LANG J,et al. A comprehensive ammonia emission inventory with high-resolution and its evaluation in the Beijing-Tianjin-Hebei(BTH)Region,China[J]. Atmospheric Environment,2015,106:305-317.
[26]WANG W,BRUYERE C,DUDA M,et al. User's guide for the advanced research WRF(ARW)modeling system version 3. 3[R].Colorado:National Center for Atmospheric Research Technical Note,2011:115-116.
[27]吴志强,叶锺楠.基于百度地图热力图的城市空间结构研究:以上海中心城区为例[J].城市规划,2016,40(4):33-40.WU Zhiqiang,YE Zhongnan. Research on urban spatial structure based on Baidu heat map:a case study on the central city of Shanghai[J].City Planning Review,2016,40(4):33-40.
[28]于丙辰,陈刚.基于腾讯区域热力图的庐山核心景区客流研究[J].国土与自然资源研究,2017(2):83-89.YU Bingchen,CHEN Gang.Research on visitor flows of lushan core scenic area using Tencent regional heatmap[J].Territory&Natural Resources Study,2017(2):83-89.
[29]赵文慧,姜磊,张立坤,等.北京平原区平房冬季燃煤量及污染物排放估算[J].中国环境科学,2017,37(3):859-867.ZHAO Wenhui,JIANG Lei,ZHANG Likun,et al.Estimation of coal consumption and related contamination emission in the plain area around Beijing during winter season[J]. China Environmental Science,2017,37(3):859-867.
[30]王丽涛,张普,杨晶,等.CMAQ-DDM-3D在细微颗粒物(PM2. 5)来源计算中的应用[J].环境科学学报,2013,33(5):1355-1361.WANG Litao,ZHANG Pu,YANG Jing,et al.Application of CMAQDDM-3D in the source analysis of fine particulate matter(PM2. 5)[J].Acta Scientiae Circumstantiate,2013,33(5):1355-1361.
[31]NAPELENOK S L,COHAN D S,HU Y,et al.Decoupled direct 3D sensitivity analysis for particulate matter(DDM-3D/PM)[J].Atmospheric Environment,2006,40(32):6112-6121.
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