基于夜间灯光数据的郑州市大气污染暴露强度研究
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摘要
近年来随着城市化进程加快,大气污染问题日益严重。在人口聚集地,居民受大气污染影响较大,探究城市人口大气污染暴露强度的空间分布特征已成为研究热点。由于城市内部自然和社会要素空间分布极为不均,仅从污染物数值角度难以准确揭示城市人口大气污染暴露强度的分布特征。本文基于2015年郑州市空气质量检测站的小时数据,分别利用人口年鉴数据和2015年年均夜间灯光数据,反演出郑州市人口空间分布数据,作为人口数据输入参数,采用人口污染大气暴露强度指标法研究2015年冬半年郑州市人口空气污染暴露强度及空间分布特征。结果表明,利用夜间灯光反演人口数据得出的污染大气暴露强度表现优于年鉴人口数据得出的结果,前者能够揭示出相邻栅格单元之间暴露强度的微观差异,在城市大气污染暴露强度空间分布上更加细化、更加准确科学。从暴露强度空间分布规律看,高暴露强度区主要集中于郑州市区和所辖各县城镇人口高密度区,且"市—郊"两级分化明显;从季节上看,冬季暴露强度水平高于春季暴露强度水平。开展城市空气污染人口暴露强度的污染分布特征研究,能够更精准地揭示郑州市人口污染空气暴露风险空间分布特征,发现高暴露强度区域,从而更有针对性的开展城市大气污染防治。
In recent years,the problem of urban air pollution is becoming more and more serious with the acceleration of urbanization. Residents in the urban city,a gathering place of population,are greatly affected by air pollution. It has become a hot research topic to explore the spatial distribution characteristics of exposure intensity of air pollution in urban population. The spatial distribution of natural and social elements in cities is extremely uneven,so it is difficult to accurately reveal distribution characteristics of exposure intensity of air pollution in urban population from the perspective of pollutant value alone. Based on the hourly data of air quality monitoring stations in Zhengzhou city in 2015,the index method for of exposure intensity of air pollution in porulation was adopted to study exposure intensity of air pollution and the spatial distribution characteristics of urban population in Zhengzhou city in second half of 2015,by taking population spatial distribution data as input parameters for population data by the inversion of the demographic yearbook data and the annual average night light data in 2015 respectively. The results showed that the intensity of atmospheric exposure of polluted air obtained by inversion of population data by night light was better than that obtained by the demographic data of the yearbook. The former could reveal the microcosmic differences in the intensity of exposure between adjacent grid units and was more detailed and accurate in the spatial distribution of urban air pollution. According to the spatial distribution law of exposure intensity,the high exposure intensity areas were mainly concentrated in the urban areas of Zhengzhou and the town population high density areas of the counties under its jurisdiction,and the "city-suburb"division was obvious. In terms of season,the exposure intensity level in winter was higher than that in spring. The study above could reveal the spatial distribution characteristics of population exposure risk to pollured air in Zhengzhou City more precisely,and could find areas with high exposure intensity,so as to better target the prevention and control of urban air pollution.
In recent years,the problem of urban air pollution is becoming more and more serious with the acceleration of urbanization. Residents in the urban city,a gathering place of population,are greatly affected by air pollution. It has become a hot research topic to explore the spatial distribution characteristics of exposure intensity of air pollution in urban population. The spatial distribution of natural and social elements in cities is extremely uneven,so it is difficult to accurately reveal distribution characteristics of exposure intensity of air pollution in urban population from the perspective of pollutant value alone. Based on the hourly data of air quality monitoring stations in Zhengzhou city in 2015,the index method for of exposure intensity of air pollution in porulation was adopted to study exposure intensity of air pollution and the spatial distribution characteristics of urban population in Zhengzhou city in second half of 2015,by taking population spatial distribution data as input parameters for population data by the inversion of the demographic yearbook data and the annual average night light data in 2015 respectively. The results showed that the intensity of atmospheric exposure of polluted air obtained by inversion of population data by night light was better than that obtained by the demographic data of the yearbook. The former could reveal the microcosmic differences in the intensity of exposure between adjacent grid units and was more detailed and accurate in the spatial distribution of urban air pollution. According to the spatial distribution law of exposure intensity,the high exposure intensity areas were mainly concentrated in the urban areas of Zhengzhou and the town population high density areas of the counties under its jurisdiction,and the "city-suburb"division was obvious. In terms of season,the exposure intensity level in winter was higher than that in spring. The study above could reveal the spatial distribution characteristics of population exposure risk to pollured air in Zhengzhou City more precisely,and could find areas with high exposure intensity,so as to better target the prevention and control of urban air pollution.
引文
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[2]王跃思,姚利,王莉莉,等.2013年元月我国中东部地区强霾污染成因分析[J].中国科学:地球科学,2014,44(1):15-26.WANG Y S,YAO L,WANG L L,et al.January 2013China strong in central and eastern regions haze pollution causes analysis[J].Chinese Science:Earth Science,2014,44(1):15-26.
[3]蔺雪芹,王岱.中国城市空气质量时空演化特征及社会经济驱动力[J].地理学报,2016,71(8):1357-1371.LIN X Q,WANG D.Spatial-temporal evolution characteristics of urban air quality in China and its socio-economic driving force[J].Journal of Geography,2016,71(8):1357-1371.
[4]王振波,方创琳,许光,等.2014年中国城市PM2.5浓度的时空变化规律[J].地理学报,2015,70(11):1720-1734.WANG Z B,FANG C L,XU G,et al.Spatial and temporal variation law of PM2.5concentration in Chinese citiesin 2014[J].Journal of Geography,2015,70(11):1720-1734.
[5]刘海猛,方创琳,黄解军,等.京津冀城市群大气污染的时空特征与影响因素解析[J].地理学报,2018,73(1):177-191.LIU H M,FANG C L,HUANG J J,et al.Analysis on the spatial and temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration[J].Journal of Geography,2013,73(1):177-191.
[6]KAZUNARI ONISHI,TSUYOSHI THOMAS SEKIYA-MA,MASANORI NOJIMA,et al.Prediction of health effects of cross-border atmospheric pollutants using an aerosol forecast model[J].Environment International,2018,117(6):48-56.
[7]曹丽琴,李平湘,张良培.基于DMSP/OLS夜间灯光数据的城市人口估算:以湖北省各县市为例[J].遥感信息,2009(1):83-87.CAO L Q,LI X P,ZHANG L P,et al.Urban population estimation based on DMSP/OLS night lighting data:Taking Hubei counties and cities as examples[J].Remote Sensing Information,2009(1):83-87.
[8]YASUHIRO M,MIWAKO T,YOSHIKI N.The evaluation of distributed multiple-light arrangement in the daytime and nighttime using brightness image[C]//Session of the CIE.Beijing:[s.n.],2007.
[9]SUTTON P C,ROBERTS D.Census from Heaven∶An estimate of the global human population using nighttime satellite imagery[J].Remote Sensing,2001,22(16):3061-3076.
[10]NAIZHUO Z.Relationships between brightness of nighttime lights and population density[C]//Sam Marcos.USA:AGU Fall Meeting,2012.
[11]杜国明,张树文.基于遥感的城乡人口分布模拟:以松原市为例[J].遥感学报,2007(2):252-256.DU G M,ZHANG S W.Urban and rural population distrib ution simulation based on remote sensing:taking Songyuan city as an example[J].Journal of Remote Sensing,2007(2):252-256.
[12]赵云洁,王怡素,闫新庆.郑州市2014-2017年大气污染特征分析[J].科技资讯,2018,(1):110-111.ZHAO Y J,WANG Y S,YAN X Q.Characteristics of atmospheric pollution in Zhengzhou city from 2014 to2017[J].Science and Technology Information,2018,(1):110-111.
[13]王占山,李云婷,陈添,等.2013年北京市PM2.5的时空分布[J].地理学报,2015,70(1):110-120.WANG Z S,LI Y T,CHEN T,et al.2013 Beijing PM2.5of space-time distribution[J].Journal of Geography,2015(1):110-120.
[14]于向英.郑州统计年鉴[M].北京:中国统计出版社,1999.YU X Y.Statistical yearbook of zhengzhou[M].BeiJing:China Statistical Press,1999.
[15]邹滨,彭芬,焦利民,等.高分辨率人口空气污染暴露GIS空间区划研究[J].武汉大学学报(信息科学版),2013,38(3):334-338.ZOU B,PENG F,JIAO L M,et al.GIS spatial zoning study on high resolution population air pollution exposure[J].Journal of Wuhan University(information science edition),2013,38(3):334-338.
[16]邹滨,蒲强,罗岳平,等.城市PM2.5污染防控多指标空间区划研究[J].安全与环境学报,2016,16(1):337-342.ZOU B,PU Q,LUO Y P,et al.Study on multi-index spatial zoning of urban PM2.5pollution prevention and control[J].Journal of Safety and Environment,2016,16(1):337-342.
[17]李启华,牛生杰,许丹.基于太湖地区MFRSR遥感大气气溶胶光学特性和大气污染状况[J].大气科学学报,2012,35(3):364-371.LI Q H,NIU S J,XU D.Based on the optical properties and air pollution status of MFRSR remote sensing aerosol in taihu area[J].Journal of Atmospheric Science,2012,35(3):364-371.
[18]CARNEVALE C,DOUROS J,FINZI G,et al.Uncertainty evaluation in air quality planning decisions:Acase study for Northern Italy[J].Environmental Science and Policy,2016,65:37-47.
[19]孙兆彬,安兴琴,陶燕,等.基于GIS和大气数值模拟技术评估兰州市PM10的人群暴露水平[J].中国环境科学,2012,32(10):1753-1757.SUN Z B,AN X Q,TAO Y,et al.Population exposure level of PM10in lanzhou city was evaluated based on GIS and atmospheric numerical simulation technology[J].China Environmental Science,2012,32(10):1753-1757.
[20]MITCHELL L E,CROSMAN E T,JACQUES A A,et al.Monitoring of greenhouse gases and pollutants across an urban area using a light-rail public transit platform[J].Atmospheric Environment,2018,187:9-23.