气候与水文

京津冀地区AOD时空变化及影响因子的地理探测

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  • 1 天津师范大学地理与环境科学学院,天津3003872 西安地球环境创新研究院,陕西西安710061
景悦(1994-),女,硕士,主要研究方向为大气环境遥感. E-mail:15222750696@163.com

收稿日期: 2019-05-01

  修回日期: 2019-08-27

  网络出版日期: 2020-01-05

基金资助

国家重点研发计划青年项目(2016YFC0201700);国家自然科学基金青年项目(41907194);天津市自然科学基金(17JCYBJC42900)

Spatiotemporal variations of AOD and geographical detection of its influence factors in Beijing-Tianjin-Hebei region

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  • 1College of Geography and Environment Science,Tianjin Normal University,Tianjin 300387,China; 2 Xian Institute for Innovative Earth Environmental Research,Xian 710061,Shaanxi,China

Received date: 2019-05-01

  Revised date: 2019-08-27

  Online published: 2020-01-05

摘要

基于MODIS 3 km AOD遥感数据,利用空间自相关模型及地理探测器对20102016年京津冀地区AOD的时空变化特征及其影响因子进行探测。结果表明:(1 20102016年京津冀地区年平均AOD值为0.83,其中天津市年均AOD值为研究区最高,河北省次之,北京市最低。研究区及各分区AOD7 a变化趋势大体一致,呈现先下降后上升再小幅波动的状态。(2 空间自相关分析表明京津冀地区AOD空间分布呈现显著正相关。局部高高聚集区主要集中在北京市东南部、天津市南部及河北省的中南部,低低聚集区集中分布在西北部山区。研究区高低聚集区面积均呈减小趋势,不显著区呈扩大态势。(3 地理探测器结果表明不同区域的主导影响因子不同,北京市首要影响因子为NDVI,其次为人口密度,且二者交互作用明显。天津市主导因子为风速,人口密度、第二产业生产总值等人为因子的作用力也较大,风速与其交互作用较强。河北省主导因子为人口密度,GDP与第二产业生产总值等的作用力次之,整体交互作用偏弱。通过地理探测器解析京津冀地区AOD空间分异的影响机理,其结果对我国大气污染治理具有重要意义。

本文引用格式

景悦, 孙艳玲, 高爽, 陈莉, 潘隆, 马含 . 京津冀地区AOD时空变化及影响因子的地理探测[J]. 干旱区地理, 2020 , 43(1) : 87 -98 . DOI: 10.12118/j.issn.1000-6060.2020.01.11

Abstract

The Beijing-Tianjin-Hebei region, which is located in the Bohai Rim, is the most economically developed region in northern China. It is also one of the most polluted regions in China. Aerosol optical depth (AOD) can effectively reflect the degree of air pollution. In this study, the MODIS 3 km AOD remote sensing data was analyzed and the research results would be of great significance to China’s air pollution. Firstly, the temporal variation characteristics and spatial autocorrelation characteristics of AOD in Beijing-Tianjin-Hebei region from 2010 to 2016 were analyzed. The factors of precipitation, wind speed, relative humidity, normalized difference vegetation index (NDVI),gross domestic product (GDP),secondary industry GDP, population density were selected as the affecting factors of AOD, using geographical detection to analyze the trend of the factors contribution ratio and its dominant affecting factors in different regions and different times in Beijing-Tianjin-Hebei region. The results showed as follows: (1) The annual average AOD of the Beijing-Tianjin-Hebei region was 0.83 from 2010 to 2016.The annual average AOD of Tianjin was the highest in the study area, followed by Hebei and Beijing. The annual variation trend of AOD was shown an overall state of decline first followed by rise and then a small fluctuation change and a similar status in each local region as well. (2) The spatial autocorrelation analysis indicated a significant positive correlation among spatial distribution of AOD in Beijing-Tianjin-Hebei region. Local high-accumulation areas were mainly concentrated in the southeastern part of Beijing, southern part of Tianjin, and central-southern part of Hebei Province. The low-accumulation areas were concentrated in the mountains of the northwest. The area of the high and low accumulation areas in Beijing-Tianjin-Hebei region showed a decreasing trend, while the area of the non-significant areas showed an increasing trend. (3) Geographic detection analysis indicated that the primary influencing factor was NDVI in Beijing, followed by population density, and their interaction was significant. The dominant factor was wind speed in Tianjin. The effect of human factors such as population density and the GDP of the secondary industry were also important. The effect of interactions between wind speed and the above factors were also significant. The dominant factor was population density in Hebei Province, followed by GDP and the secondary industry’s GDP, and the overall interaction was relatively weak. In the past, the correlation analysis of the influencing factors was based on linear relationship of the overall area, while the geographical detection not only has wireless assumptions, but also reveals the linear, nonlinear and spatial relationships of the driving factors. From this perspective, the geographical detection is more reliable in studying the influencing factors. This study applied geographical detection to analysis of the impacting factors of AOD in Beijing-Tianjin-Hebei region, which proved its feasibility in this field and this region. At the same time, the results also had important reference value for air pollution prevention, industrial and agricultural layout and urban construction in Beijing-Tianjin-Hebei region.

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