School of Geomatics and Urban Information, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China.
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Environ Pollut. 2024 May 1;348:123838. doi: 10.1016/j.envpol.2024.123838. Epub 2024 Mar 21.
Accurate fine-mode and coarse-mode aerosol knowledge is crucial for understanding their impacts on the climate and Earth's ecosystems. However, current satellite-based Fine-Mode Aerosol Optical Depth (FAOD) and Coarse-Mode Aerosol Optical Depth (CAOD) methods have drawbacks including inaccuracies, low spatial coverage, and limited temporal duration. To overcome these issues, we developed new global-scale FAOD and CAOD from 2005 to 2020 using a novel deep learning model capable of the synergistic retrieval of two aerosol sizes. After validation with the aerosol robotic network (AERONET) and sky radiometer network (SKYNET), the new monthly FAOD and CAOD showed significant improvements in accuracy and spatial coverage. From 2005 to 2020, the new data showed that China had the greatest decrease in FAOD and CAOD. In contrast, India and South Latin America had a significant increase in FAOD versus North Africa in CAOD. FAOD in the regions of Argentina, Paraguay, and Uruguay in South America have shown an upward trend. The results reveal that FAOD and CAOD display distinct patterns of change in the same regions, particularly on the west coast of the United States where FAOD is increasing, while CAOD is decreasing. Aside from the year 2020 due to the global COVID-19 pandemic, the analysis showed that although China has seen at least an +85% increase in energy consumption and urban expansion in 2019 compared to 2005 due to the needs of development and construction, the implementation of China's air pollution control policies has led to a significant decrease in FAOD (-46%) and CAOD (-65%) after 2013. This research enriches our comprehension of global fine and coarse aerosol patterns, additional investigations are needed to determine the potential global implications of these changes.
准确的细颗粒模态和粗颗粒模态气溶胶知识对于理解它们对气候和地球生态系统的影响至关重要。然而,当前基于卫星的细颗粒模态气溶胶光学深度(FAOD)和粗颗粒模态气溶胶光学深度(CAOD)方法存在不准确、空间覆盖范围有限和时间持续时间有限等缺点。为了克服这些问题,我们使用一种新的深度学习模型,该模型能够协同检索两种气溶胶尺寸,开发了 2005 年至 2020 年期间新的全球尺度 FAOD 和 CAOD。经过与气溶胶机器人网络(AERONET)和天空辐射计网络(SKYNET)的验证,新的月度 FAOD 和 CAOD 在准确性和空间覆盖范围方面有了显著提高。2005 年至 2020 年,新数据表明中国 FAOD 和 CAOD 的降幅最大。相比之下,印度和南拉丁美洲的 FAOD 增加量大于北非的 CAOD。阿根廷、巴拉圭和乌拉圭的 FAOD 在南美洲地区呈上升趋势。结果表明,FAOD 和 CAOD 在同一地区呈现出不同的变化模式,特别是在美国西海岸,FAOD 呈上升趋势,而 CAOD 呈下降趋势。由于 2020 年全球 COVID-19 大流行,分析表明,尽管 2019 年中国的能源消耗和城市扩张至少比 2005 年增加了 85%,这是发展和建设的需要,但中国实施的空气污染控制政策导致 2013 年后 FAOD(-46%)和 CAOD(-65%)显著减少。这项研究丰富了我们对全球细颗粒和粗颗粒气溶胶模式的理解,需要进一步研究这些变化的潜在全球影响。