基于LUR的二氧化氮浓度空间分布模拟及其下垫面影响因素分析

发布时间：2018-05-31 20:09

  本文选题：下垫面 + 道路交通　； 参考：《地球信息科学学报》2017年01期

【摘要】：随着经济的快速发展,空气污染已经成为当今重要的环境问题,引起公众的广泛关注,二氧化氮(NO_2)作为主要的空气污染物之一,成为相关研究的重点。通过监测数据发现,二氧化氮质量浓度值的空间分布具有区域性差异,所以对其空间分布模拟,以及形成区域差异的下垫面影响因素分析,具有重要的研究价值。土地利用回归模型(Land-use Regression,LUR)是将统计方法中的回归模型与空间上的土地利用数据、监测数据和其他相关的地理数据结合分析并在地图上显示的方法。本文结合缓冲区分析、叠加分析、Spearman相关性分析、多元回归分析等方法构建土地利用回归模型(Land Use Regression,LUR),用于识别与NO_2浓度相关的下垫面影响因素,并模拟NO_2质量浓度的空间分布。LUR模型可以模拟出NO_2质量浓度空间分布特征,并针对下垫面影响因素得到以下结论:城乡居住地及工业用地面积增加、污染源的距离减少和道路长度增加会导致NO_2浓度升高;耕地面积、绿地面积和水域面积的增加会导致NO_2浓度减少;NO_2浓度最高的区域主要集中在工业园区;NO_2浓度值从城区到郊区递减,需要通过改变工业区结构和增加绿地面积来减少城区的NO_2浓度。
[Abstract]:With the rapid development of economy, air pollution has become an important environmental problem, which has aroused widespread public concern. As one of the main air pollutants, nitrogen dioxide (NO2) has become the focus of related research. Through monitoring data, it is found that the spatial distribution of nitrogen dioxide mass concentration is different in different regions, so it is of great value to simulate the spatial distribution of nitrogen dioxide and to analyze the influencing factors of underlying surface that form regional differences. Land use regression model (Land-use regression) is a method which combines the regression model of statistical method with spatial land use data, monitoring data and other relevant geographic data and shows on the map. Combined with buffer zone analysis, superposition analysis, Spearman correlation analysis and multivariate regression analysis, a land Use regression model was constructed to identify the factors affecting the underlying surface associated with NO_2 concentration. The spatial distribution characteristics of NO_2 mass concentration can be simulated by simulating the spatial distribution of NO_2 mass concentration, and the following conclusions are obtained according to the influencing factors of underlying surface: the increase of urban and rural residential area and industrial land area. Reduced distance from pollution sources and increased road length lead to higher NO_2 concentrations; The increase of green area and water area will result in the decrease of NO_2 concentration and the highest concentration of No2 in the industrial park. It is necessary to reduce the concentration of NO_2 in urban areas by changing the structure of industrial areas and increasing the area of green space.
【作者单位】： 中国科学院测量与地球物理研究所;中国科学院大学;湖北省林业调查规划院;
【基金】：国家自然科学基金面上项目(41571487) 湖北省自然科学基金项目(面上)(2015CFB608)
【分类号】：X51;X831
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本文编号：1961128