Observation-based quantification of aerosol transport using optical flow: A satellite perspective to characterize interregional transport of atmospheric pollution

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Tianhao Zhang , Yu Gu , Bin Zhao , Lunche Wang , Zhongmin Zhu , Yun Lin , Xing Chang , Xinghui Xia , Zhe Jiang , Hongrong Shi , Wei Gong
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引用次数: 0

Abstract

Interregional transport plays a significant role in haze formation with varying and disputable contribution extent. Current research on quantitatively analyzing interregional atmospheric pollution transport has mainly relied on meteorological and chemical models. However, these models are typically affected by uncertainties due to the assumptions and simplifications inherent in the numerical simulations and source emission estimations. In this study, a comprehensive optical flow framework is developed to offer a new perspective on quantitative characterization of interregional transport of atmospheric pollution based on synergistic observations from geostationary and sun-synchronous satellites. In this framework, the high-frequency continuous aerosol observing images are regarded as video in computer vision, and an aerosol dynamic optical flow algorithm is proposed by incorporating aerosol-specific assumptions and constraints, overcoming the limitation that traditional optical flow methods are typically confined to rigid bodies. Results demonstrate that the developed optical flow framework could distinguish the aerosol transport process from other dynamic processes of aerosol development and accurately capture the fast-changing details of transport processes. Moreover, the satellite-based optical flow framework achieves aerosol transport results comparable to those of widely accepted model-based methods, demonstrating the physical interpretation of pixel-based optical flow results and highlighting its effectiveness in quantitative characterization of the atmospheric pollution transport process via the Aerosol Transport Index (ATI). Furthermore, a case analysis of long-term assessments of interregional transport of atmospheric pollution indicates that Beijing acts as a “sink” of atmospheric pollution, and a downward trend could be found from the annually averaged transported aerosol net loadings due to the emission reduction policy. Compared with model-based methods, the satellite-based optical flow framework is directly grounded in observations and does not rely on emission inventories that take years to update. Therefore, it not only helps improve understanding the patterns of atmospheric pollution interregional transport, but also provides a more efficient and economical way to assess the effectiveness of regional joint control policy.
利用光流对气溶胶迁移进行基于观测的量化:从卫星角度描述区域间大气污染传输特征
区域间传输在灰霾形成过程中扮演着重要角色,其贡献程度各不相同,且存在争议。目前,定量分析区域间大气污染传输的研究主要依赖气象和化学模型。然而,由于数值模拟和源排放估算中固有的假设和简化,这些模型通常会受到不确定性的影响。本研究基于地球静止卫星和太阳同步卫星的协同观测,建立了一个全面的光流框架,为定量描述大气污染的区域间传输提供了一个新的视角。在这一框架中,高频连续气溶胶观测图像被视为计算机视觉中的视频,通过纳入气溶胶特定的假设和约束条件,提出了气溶胶动态光学流算法,克服了传统光学流方法通常局限于刚体的局限性。结果表明,所开发的光学流框架能将气溶胶迁移过程与气溶胶发展的其他动态过程区分开来,并能准确捕捉迁移过程中快速变化的细节。此外,基于卫星的光学流框架所获得的气溶胶传输结果可与广泛接受的基于模型的方法相媲美,证明了基于像素的光学流结果的物理解释能力,并突出了其通过气溶胶传输指数(ATI)定量表征大气污染传输过程的有效性。此外,对区域间大气污染传输长期评估的案例分析表明,北京是大气污染的 "汇",从年均传输气溶胶净负荷中可以发现减排政策带来的下降趋势。与基于模型的方法相比,基于卫星的光流框架直接以观测数据为基础,不依赖于需要多年更新的排放清单。因此,它不仅有助于更好地理解大气污染的区域间传输模式,还为评估区域联合控制政策的有效性提供了一种更高效、更经济的方法。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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