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基于网格的长三角PM2.5分布影响因素及交互效应
摘要点击 2915  全文点击 726  投稿时间:2020-12-14  修订日期:2021-01-04
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中文关键词  网格  长三角  PM2.5  空间分布  影响因素  交互效应
英文关键词  grid  Yangtze River Delta  PM2.5  spatial distribution  factors  interaction effect
作者单位E-mail
黄小刚 山西师范大学地理科学学院, 临汾 041004
中国科学院地球环境研究所气溶胶化学与物理重点实验室, 西安 710061
陕西师范大学地理科学与旅游学院, 西安 710119 
huangxg@sxnu.edu.cn 
赵景波 中国科学院地球环境研究所气溶胶化学与物理重点实验室, 西安 710061
陕西师范大学地理科学与旅游学院, 西安 710119 
zhaojb@snnu.edu.cn 
辛未冬 山西师范大学地理科学学院, 临汾 041004  
中文摘要
      基于遥感反演数据,研究了2016年长三角地区PM2.5浓度空间分布特征,从气象因素、地形、植被和大气污染物排放清单等方面选取评价因子,以0.25°×0.25°网格为评价单元,利用GAM模型研究了长三角PM2.5空间分布的影响因素及交互效应.结果表明:①长三角PM2.5浓度总体呈北高南低、西高东低的分布态势,但以南北向差异为主.长三角南部PM2.5浓度多低于35 μg·m-3,PM2.5超标零星出现在城镇周围,呈孤岛状分布.北部PM2.5浓度多超过35μg·m-3,PM2.5污染多呈连片状分布.②长三角PM2.5浓度分布具有显著的正的空间自相关性,高高集聚区集中分布在长三角北部,低低集聚区集中分布在南部.③ GAM模型分析表明,地形起伏度、气温和降水量对PM2.5浓度主要呈负向影响;污染物排放量主要呈正向影响;风速<2.5 m·s-1时影响不显著,风速≥2.5 m·s-1后有显著的负向影响.地形起伏度、气温和降水量南高北低是造成长三角PM2.5北高南低的重要原因,风速东高西低是造成长三角PM2.5浓度东西向差异的原因之一.④除地形起伏度-PM2.5排放量外,其余因素两两间的交互项均通过了显著性检验,对PM2.5分布有显著的交互效应.
英文摘要
      Spatial features of PM2.5 concentration in the Yangtze River Delta in 2016 were analyzed using remote sensing data. Selecting factors among meteorology, topography, vegetation, and emission list of air pollutants, factors and their interaction effects on the spatial distribution of PM2.5 concentration were studied based on GAM, with an evaluation unit of 0.25°×0.25° for the grid. It showed that:① With a more significant difference between the north and south, PM2.5 concentration was generally higher in the north and west but lower in the south and east. In the southern part of the delta, the concentration was mostly lower than 35 μg·m-3, with noncompliance of the PM2.5 concentration scattered in urban areas like islands. Meanwhile, PM2.5 concentration is generally over 35 μg·m-3, and the pollution appeared like sheets. ② Besides, PM2.5 concentration showed an apparent positive spatial autocorrelation with "High-High" PM2.5 agglomeration areas in the north of the delta and "Low-Low" PM2.5 agglomeration areas in the south. ③ Based on GAM, hypsography, temperature, and precipitation negatively affected PM2.5 concentration, whereas pollutant emissions positively affected it. The effect of wind was minor when its speed <2.5 m·s-1, and more negatively significant when its speed ≥ 2.5 m·s-1. Hypsography, temperature, and precipitation were higher in the southern part of the delta, but they were lower in the northern part, leading to a higher PM2.5 concentration in the northern parts and lower in the southern parts. A higher wind speed in the east and lower in the west also led to a concentration difference between them. ④ All factors had passed a significant pair interaction test, except for hypsography and PM2.5 emission, and they all showed a significant interaction effect on the distribution of PM2.5 in the Yangtze River Delta.

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