大气与环境光学学报 ›› 2026, Vol. 21 ›› Issue (2): 225-237.

• 大气光学 • 上一篇    

2000―2022年汾渭平原气溶胶光学厚度时空变化及驱动因素分析

董金芳 1,2, 王娟 1, 何慧娟 1, 袁媛 3, 王亚妮 4*   

  1. 1 陕西省农业遥感与经济作物气象服务中心, 陕西 西安 710016; 2 陕西省气象局秦岭和黄土高原生态环境气象重点实验室, 陕西 西安 710016; 3 南京市气象局, 江苏 南京 210019; 4 西安文理学院生物与环境工程学院, 陕西 西安 710065
  • 收稿日期:2023-09-28 修回日期:2024-03-19 出版日期:2026-03-28 发布日期:2026-03-27
  • 通讯作者: E-mail: Yaniwang0704@163.com E-mail:Yaniwang0704@163.com
  • 作者简介:董金芳 (1984- ), 女, 河北滦州人, 硕士, 高级工程师, 主要从事生态环境遥感监测方面的研究。E-mail: dongjinfang213@163.com
  • 基金资助:
    陕西省自然科学基础研究计划项目 (2020JQ-978), 陕西省秦岭和黄土高原生态环境气象重点实验室开放研究基金课题 (2020G-13)

Spatio-temporal distribution and driving factors of aerosol optical depth in Fenwei Plain during 2000–2022

DONG Jinfang1,2, WANG Juan1, HE Huijuan1, YUAN Yuan3, WANG Yani4*   

  1. 1 Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi'an 710016, China; 2 Key Laboratory of Eco-Environment and Meteorology for the Qinling Mountains and Loess Plateau, Shaanxi Provincial Meteorological Bureau, Xi'an 710016, China; 3 Nanjing Meteorological Bureau, Nanjing 210019, China; 4 School of Biological and Environmental Engineering, Xi'an University, Xi'an 710065, China
  • Received:2023-09-28 Revised:2024-03-19 Online:2026-03-28 Published:2026-03-27

摘要: 基于MODIS气溶胶数据产品MCD19A2 获取了2000―2022 年汾渭平原气溶胶光学厚度 (AOD), 采用热点分 析和地理探测器方法对汾渭平原AOD时空演变及驱动因素进行了分析。结果表明, 2000―2022 年汾渭平原AOD均 值的年际变化具有阶段性, 2000―2013 年AOD均值呈现不显著的小幅上升趋势, 2014―2022 年AOD均值呈现显著下 降趋势; 而同一时期该地区的AOD空间分布呈现较为稳定的由城市群中心向周边区县递减的阶梯状分布格局。在季 节变化特征上, 2000―2013 年汾渭平原AOD均值夏季 (0.44) > 春季 (0.42) > 冬季 (0.36) > 秋季 (0.32), 未呈现显著年 际变化趋势; 2014―2022 年汾渭平原AOD均值春季 (0.36) > 夏季 (0.32) > 冬季 (0.31) > 秋季 (0.29), 四个季节AOD均 值均呈明显的下降趋势。在空间分布上, 春季AOD热点聚集区主要分布在汾河-黄河交汇处和洛阳盆地, 夏季热点聚 集区主要分布在地势较低的河谷盆地, 秋冬季节AOD热点分布较为一致, 主要分布在渭河谷地城市群和洛阳盆地。 地理探测器结果显示, 大气状态因子的驱动力最强, 社会经济因子次之, 降水冲刷因子、大气污染因子和大气水平扩 散能力因子的驱动力较弱。其中, 大气状态因子和社会经济因子驱动力较为稳定, 其余因子存在较强的季节效应。

关键词: 汾渭平原, 气溶胶光学厚度, 驱动因素, 热点分析, 地理探测器

Abstract: Objective The purpose of this study is to comprehensively analyze the long-term spatiotemporal evolution of aerosol optical depth (AOD) in the Fenwei Plain, a region recognized as one of the key areas for atmospheric pollution control in China, from 2000 to 2022. Specifically, the study aims to (1) quantify interannual and seasonal variations of AOD, (2) characterize the spatial clustering and persistence of high- and low-value AOD regions, and (3) assess the relative contributions of socioeconomic activities, meteorological conditions, precipitation characteristics, air pollution events, and atmospheric diffusion capacity to the spatial heterogeneity of AOD. The ultimate objective is to provide scientific evidence and insights to support long-term air quality improvement strategies, environmental governance, and sustainable development of the Fenwei Plain. Methods AOD observation data were derived from the MODIS MAIAC MCD19A2 satellite product, and processed on the Google Earth Engine platform to generate annual and seasonal mean composites for the years from 2000 to 2022. Spatial clustering patterns of AOD were analyzed at the county level using the Getis-Ord Gi* hot spot analysis to detect statistically significant AOD "hot spots" and "cold spots". To explore the driving mechanisms behind the observed patterns, the Geographical Detector Model was applied to 16 influencing factors, which were divided into five categories: socioeconomic indicators (population density, GDP per unit area), atmospheric state variables (air temperature, air pressure, water vapor pressure), precipitation cleansing factors (precipitation amount and frequency), air pollution factors (haze days, dust storm days), and atmospheric horizontal diffusion indicators (wind speed and strong wind frequency). The model quantified the explanatory power (q-values) of each factor for different seasons and representative years (2010 and 2020), revealing both long-term changes and seasonal dependencies. Results and Discussion AOD in the Fenwei Plain from 2000 to 2022 demonstrated a clear two-stage interannual trend. From 2000 to 2013, AOD showed a slight but statistically insignificant upward trend, with a multi-year mean of 0.38. In contrast, from 2014 to 2022, AOD declined significantly at an annual rate of 0.011, with the mean decreasing to 0.32, reflecting the effectiveness of China's air pollution control policies implemented after 2013. Spatially, AOD in the Fenwei Plain from 2000 to 2022 exhibited a persistent stepwise pattern decreasing from major urban agglomerations such as Xi'an, Xianyang, Luoyang, and Yuncheng toward surrounding rural and high-elevation regions. The three major hot spot regions, including the Weihe River valley, the Fenhe-Yellow River confluence, and the Luoyang Basin, remained stable for decades, indicating spatially locked pollution accumulation zones influenced by topography, population distribution, and industrial structure. Seasonal analysis revealed the substantial differences that during 2000 – 2013, AOD followed the order of summer > spring > winter > autumn, and during 2014–2022, the pattern shifted to the order of spring > summer > winter > autumn, with a pronounced downward trend for each season. Seasonal hot spot patterns were consistent with sources such as spring dust transport, summer agricultural burning and photochemical activity, and winter heating emissions. The results of Geographical Detector indicated that atmospheric state factors exerted the strongest and most consistent influence on AOD spatial heterogeneity, followed by socioeconomic factors whose influence increased markedly from 2010 to 2020. In addtion, precipitation cleansing effects and diffusion capacity played secondary but seasonally variable roles, while pollution-related factors such as haze days demonstrated increasingly importance in recent years. Conclusions The findings highlight that AOD in the Fenwei Plain is determined by a combination of meteorological processes, socioeconomic development intensity, and seasonal pollution sources. Although regional AOD has decreased significantly since 2014 due to effective national and regional environmental policies, persistent spatial hot spots remain. These reflect deep structural challenges related to industrial activity, population density, and terrain-induced unfavorable conditions for atmospheric diffusion. Sustained improvement will require strengthening emission control, adjusting industrial restructure, enhancing the penetration of clean energy, and implementing season-specific pollution mitigation strategies for dust transportation, agricultural burning, and winter heating emissions.

Key words: Fenwei Plain, aerosol optical depth, spatio-temporal variation, hot spot analysis, geographical detector; atmospheric pollution, driving factors

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