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首页> 外文期刊>Ocean & coastal management >Geographically weighted regression to measure spatial variations in correlations between water pollution versus land use in a coastal watershed
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Geographically weighted regression to measure spatial variations in correlations between water pollution versus land use in a coastal watershed

机译：地理加权回归，用于测量沿海流域水污染与土地利用之间的相关性的空间变化

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摘要

Land use can influence river pollution and such relationships might or might not vary spatially. Conventional global statistics assume one relationship for the entire study extent, and are not designed to consider whether a relationship varies across space. We used geographically weighted regression to consider whether relationships between land use and water pollution vary spatially across a subtropical coastal watershed of Southeast China. Surface water samples of baseflow for seven pollutants were collected twelve times during 2010-2013 from headwater sub-watersheds. We computed 21 univariate regressions, which consisted of three regressions for each of the seven pollutants. Each of the three regressions considered one of three independent variables, i.e. the percent of the sub-watershed that was cropland, built-up, or forest. Cropland had a local R~2 less than 0.2 for most pollutants, while it had a positive association with water pollution in the agricultural sub-watersheds and a negative association with water pollution in the non-agricultural sub-watersheds. Built-up had a positive association with all pollutants consistently across space, while the increase in pollution per increase in built-up density was largest in the sub-watersheds with low built-up density. The local R~2 values were stronger with built-up than with cropland and forest. The local R~2 values for built-up varied spatially, and the pattern of the spatial variation was not consistent among the seven pollutants. Forest had a negative association with most pollutants across space. Forest had a stronger negative association with water pollution in the urban sub-watersheds than in the agricultural sub-watersheds. This research provides an insight into land-water linkages, which we discuss with respect to other watersheds in the literature.
机译：土地使用会影响河流污染，这种关系在空间上可能会或可能不会改变。常规的全球统计假设整个研究范围内只有一种关系，而并非旨在考虑这种关系是否跨空间变化。我们使用地理加权回归来考虑中国东南亚热带沿海流域的土地利用与水污染之间的关系是否在空间上变化。在2010-2013年期间，从源头小流域收集了十二种七种污染物的基流地表水样品。我们计算了21个单变量回归，其中包括对7种污染物中每种污染物的3个回归。三个回归中的每一个都被视为三个独立变量之一，即子流域中耕地，人工林或森林的百分比。对于大多数污染物，农田的局部R〜2小于0.2，而在农业子流域中与水污染呈正相关，而在非农业子流域中与水污染呈负相关。堆积物与整个空间中所有污染物的数量呈正相关，而堆积密度低的子集水区，堆积密度增加所带来的污染增加最大。耕地的当地R〜2值比农田和森林要强。堆积物的局部R〜2值在空间上变化，并且在七个污染物之间空间变化的模式不一致。森林与整个空间的大多数污染物呈负相关。与农业子集水区相比，城市子集水区与水污染的负相关性更强。这项研究提供了对陆水联系的见解，我们将参考文献中的其他流域对此进行讨论。

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  • 来源
    《Ocean & coastal management》 |2015年第1期|14-24|共11页
  • 作者

    Jinliang Huang; Yaling Huang; Robert Gilmore Pontius Jr.; Zhenyu Zhang;

  • 作者单位

    Coastal and Ocean Management Institute, Xiamen University, Xiamen 361005, China,Fujian Provincial Key Laboratory of Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361005, China;

    Coastal and Ocean Management Institute, Xiamen University, Xiamen 361005, China,Fujian Provincial Key Laboratory of Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361005, China;

    Graduate School of Geography, Clark University, Worcester, MA 01610, USA;

    Coastal and Ocean Management Institute, Xiamen University, Xiamen 361005, China,Fujian Provincial Key Laboratory of Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361005, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Geographically weighted regression; Water pollution; Land use; Linkages; Spatial variation;

    机译：地理加权回归;水污染;土地利用;链接;空间变异;

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