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

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（NO₂）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NO₂浓度相关的下垫面影响因素,并模拟NO₂质量浓度的空间分布。LUR模型可以模拟出NO₂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NO₂浓度升高;耕地面积、绿地面积和水域面积的增加会导致NO₂浓度减少;NO₂浓度最高的区域主要集中在工业园区;NO₂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NO₂浓度。

Spatial Distribution Simulation and Underlying Surface Factors Analysis of NO2 Concentration Based on Land Use Regression

With the rapid development of economy, air pollution has become an important environmental problem, attracting wide attention. As one of the main air pollutants, NO₂ (nitrogen dioxide) becomes focus of the related research. It was found that the concentration of NO₂ varied with different regions by comparing monitoring data in different monitoring sites. Thus, simulation of its spatial distribution and analysis of the influential factors of the underlying surface have important value. The Land-use Regression (LUR) model is a method that combines, analyzes and display a multivariate regression model with spatial land-use data, monitoring data and other relevant geographic data on a map. In this study, the land use regression model is built by using a buffer analysis, overlay analysis, Spear-man correlation analysis and multiple regression analysis and it was used to identify the underlying surface factors related to the NO₂ concentration and simulate the spatial distribution ofNO₂ concentration. The results show that the spatial distribution of NO₂ mass concentration can be modeled accurately by LUR model. Based on the influential factors of the underlying surface, the following conclusions can be drawn: The increase of urban residence area, rural residence area, industrial land area and the length of the road and the reduction of the distance from the pollution source will increase the NO₂ concentration. The increase of arable land area, green area and water area will decrease the NO₂ concentration. The map of simulation results shows that the highest NO₂ concentration is located in industrial districts and the NO₂ concentration is lower where it is far from the city center. Changing the industrial structure of industrial land and increasing the green land can help reduce the NO₂ concentration.