Huang Xiao-Gang, Zhao Jing-Bo, Xin Wei-Dong
College of Geographical Sciences, Shanxi Normal University, Linfen 041004, China.
Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China.
Huan Jing Ke Xue. 2021 Jul 8;42(7):3107-3117. doi: 10.13227/j.hjkx.202012101.
Spatial features of PM 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 PM 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, PM 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, with noncompliance of the PM concentration scattered in urban areas like islands. Meanwhile, PM concentration is generally over 35 μg·m, and the pollution appeared like sheets. ② Besides, PM concentration showed an apparent positive spatial autocorrelation with "High-High" PM agglomeration areas in the north of the delta and "Low-Low" PM agglomeration areas in the south. ③ Based on GAM, hypsography, temperature, and precipitation negatively affected PM concentration, whereas pollutant emissions positively affected it. The effect of wind was minor when its speed <2.5 m·s, and more negatively significant when its speed ≥ 2.5 m·s. 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 PM 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 PM emission, and they all showed a significant interaction effect on the distribution of PM in the Yangtze River Delta.
利用遥感数据对2016年长江三角洲地区PM浓度的空间特征进行了分析。在气象、地形、植被和空气污染物排放清单中选取因子,基于广义相加模型(GAM)研究各因子及其交互作用对PM浓度空间分布的影响,网格评价单元为0.25°×0.25°。结果表明:①南北差异较为显著,PM浓度总体上北高西高、南高东低。在三角洲南部,浓度大多低于35μg·m ,PM浓度超标点像岛屿一样分散在城市区域。同时,PM浓度总体上超过35μg·m ,污染呈片状分布。②此外,PM浓度呈现明显的正空间自相关性,在三角洲北部存在“高高”PM聚集区,在南部存在“低低”PM聚集区。③基于GAM模型,地形、温度和降水对PM浓度有负向影响,而污染物排放对其有正向影响。风速<2.5m·s时,风的影响较小;风速≥2.5m·s时,风的负向影响更显著。三角洲南部地形、温度和降水较高,而北部较低,导致北部PM浓度较高,南部较低。东部风速较高,西部较低,也导致了两者之间的浓度差异。④除地形和PM排放外,所有因子均通过了显著的成对交互检验,它们对长江三角洲地区PM的分布均表现出显著的交互作用。