邹滨, 许珊, 张静. 土地利用视角空气污染空间分异的地理分析[J]. 武汉大学学报 ( 信息科学版), 2017, 42(2): 216-222. DOI: 10.13203/j.whugis20150042
引用本文: 邹滨, 许珊, 张静. 土地利用视角空气污染空间分异的地理分析[J]. 武汉大学学报 ( 信息科学版), 2017, 42(2): 216-222. DOI: 10.13203/j.whugis20150042
ZOU Bin, XU Shan, ZHANG Jing. Spatial Variation Analysis of Urban Air Pollution Using GIS: A Land Use Perspective[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 216-222. DOI: 10.13203/j.whugis20150042
Citation: ZOU Bin, XU Shan, ZHANG Jing. Spatial Variation Analysis of Urban Air Pollution Using GIS: A Land Use Perspective[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 216-222. DOI: 10.13203/j.whugis20150042

土地利用视角空气污染空间分异的地理分析

Spatial Variation Analysis of Urban Air Pollution Using GIS: A Land Use Perspective

  • 摘要: 针对土地利用/覆盖(land-use and land-cover,LULC)方式是否影响城市空气污染空间分异特征形成的问题,利用遥感技术和景观生态学方法分别获取长株潭城市群核心城区LULC及其景观格局,绘制空气污染物浓度与气象影响因子空间分异图,引入地理探测器定量分析土地因子在融合气象要素前后对NO2、PM10、O3、PM2.5浓度空间分布差异的贡献强度。结果表明,建设用地面积比例越高,林地越低,NO2、PM2.5浓度越高,O3越低。非建设用地区域,污染物浓度随着土地景观格局破碎度、多样性指数值增大而升高,建设用地区域反之。LULC和土地景观格局的复合因子贡献力(P0.03~0.28)高于两者任意单独因子贡献力(P:0.01~0.11),融合气象要素后,LULC对空气污染物空间分异特征形成的因子贡献力(P:0.18~0.53)显著增强。

     

    Abstract: Does land-use and land-cover (LULC) impact the formation of spatial variation characteristics of urban air pollution? Aiming at this problem, we firstly retrieved land use/cover from Landsat 8 image and consequently used them to calculate and map landscape metrics and area ratio of each land use/cover type. Meanwhile, space mapping of NO2, PM10, O3, and PM2.5 concentrations as well as meteorological factors were conducted through inverse distance weighted interpolation method. After that, geographical detector has been introduced to analyze the influence of land use on air pollution quantitatively with/without meteorological elements as confounding factors. The results show that the NO2 and PM2.5 concentrations increased with the increment of area ratio of built-up area, contrast to the fact that it was negatively correlated with that of green-land. This situation was completely opposite to that of O3. The air pollution concentrations were higher in the non-built-up area with greater Shannon diversity index and Perimeter-Area Fractal (PAFRAC) Dimension index values. The power of landscape factors is a little bit lower than that of LULC type factors in indicating air pollution. Pairs of LULC type factors were found to enhance each other to increase the air pollution concentrations, so were pairs of landscape factors. Meteorological factors were found to reinforce the control of air pollution of LULC type factors as well as landscape factors significantly. In view of the above considerations, we draw the conclusion that the spatial patterns of air pollution probed were closely related to land factors, either in LULC or landscape metrics. The land use and meteorological conditions are both factors that have to be considered in forecasting and mitigating urban air pollution in early urban planning.

     

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