Beyond the lockdowns: satellite observations of aerosol optical depth through 2020, the first year of the COVID-19 pandemic
Beyond the lockdowns: satellite observations of aerosol optical depth through 2020, the first year of the COVID-19 pandemic
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DOI：
10.1088/1748-9326/ac7889
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发表时间：
2022
影响因子：
6.7
通讯作者：
S. Smith;M. Ting;Yutian Wu;C. Zheng
中科院分区：
文献类型：
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作者：
S. Smith;M. Ting;Yutian Wu;C. Zheng
预计未来气溶胶排放的减少将加速变暖并显著改变降水特征。因此，确定人为气溶胶减少的现有模式和未来可能的途径至关重要。2019冠状病毒病大流行导致全球交通和工业活动突然减少，为研究大流行驱动的排放变化的气溶胶效应提供了机会。本文利用两种卫星仪器测量的气溶胶光学深度（AOD）来表征整个2020年北半球四个源区（中国东部和中部、美国、印度和欧洲）的气溶胶负担。在大多数地区，创纪录的低臭氧消耗指标在大流行最早的“封锁”期之后仍然存在。创历史新低的月份主要集中在寒带春夏两个月，56% ~ 72%的采样月份AOD值均创历史新低。然而，在印度和中国中东部，COVID-19的AOD特征分别被自然变率来源（沙尘）和多年趋势所掩盖。在美国和欧洲，2019冠状病毒病信号可能在2020年夏季达到峰值，贡献高达-。01 ~−.03AOD单位观察到异常。
Anticipated future reductions in aerosol emissions are expected to accelerate warming and substantially change precipitation characteristics. Therefore, it is vital to identify the existing patterns and possible future pathways of anthropogenic aerosol reductions. The COVID-19 pandemic prompted abrupt, global declines in transportation and industrial activities, providing opportunities to study the aerosol effects of pandemic-driven emissions changes. Here, measurements of aerosol optical depth (AOD) from two satellite instruments were used to characterize aerosol burdens throughout 2020 in four Northern Hemisphere source regions (Eastern & Central China, the United States, India, and Europe). In most regions, record-low measures of AOD persisted beyond the earliest ‘lockdown’ periods of the pandemic. Record-low values were most concentrated during the boreal spring and summer months, when 56% to 72% of sampled months showed record-low AOD values for their respective regions. However, in India and Eastern & Central China, the COVID-19 AOD signature was eclipsed by sources of natural variability (dust) and a multi-year trend, respectively. In the United States and Europe, a likely COVID-19 signal peaks in the summer of 2020, contributing as much as −.01 to −.03 AOD units to observed anomalies.