Close to two decades of NASA Earth Observing System satellite data and algorithms refinements present unique opportunity to revisit the attributions of variability of tropospheric pollutants to economic activity, as well as pollutants contribution to the anthropogenic radiative forcing. The nitrogen dioxide (NO₂) is one of the top pollutants produced by industrial and power plants and traffic. That long data record now covers events of economic variability like the recession of 2008/2009, as well as the effects of COVID-19 closures of 2019/2020. In particular, the COVID-19 lockdowns in the winter of 2019/2020 and the spring of 2020 resulted in contracting economies around the world. While numerous imageries of reduced nitrogen dioxide (NO₂) appeared to be popular, few studied gave numerical assessment of how much of the anomalies is attributable to the economic slowdown. Furthermore, few studies touched on the possible climate offsets under the reduced emissions. Using principal component (PC) analysis of 16 years of NO₂ monthly series data from the Ozone Monitoring Instrument on the Aura satellite, we show that it is the third PC (PC3) from the deseasonalized hierarchy of principal modes that is best coupled with the economic indicators. This coupling is positive, i.e. PC3 and economic indicators manifest positive covariance. However, the economic variability can explain only 40% of the information in PC3. Furthermore, this mode by itself explains only 3% of the total deseasonalized NO₂ variability. We therefore conclude that, while having an unambiguous impact, the economy can be awarded at best third order of importance in the NO₂ departures from the seasonal averages.

Once we identified PC3 as the NO₂ mode that is coupled with the economic variability, we use this mode as an indicator and look for rapid climate adjustments to that part of the NO₂ variability that we are confident is coupled with the economic variability. We focus on observational data from the Atmospheric Infrared Sounder (AIRS) on board the NASA Aqua satellite, decompose the series of surface skin temperature and clear-sky outgoing longwave radiances (OLR) into principal components, and identify potential impacts of the NO₂ PC3 on these climate variables.

近20年的NASA地球观测系统卫星数据和算法改进为重新审视对流层污染物的变化对经济活动的影响以及污染物对人为辐射强迫的贡献提供了独特的机会。二氧化氮（NO ₂）是由工业，发电厂和交通运输产生的主要污染物之一。漫长的数据记录现在涵盖了经济多变的事件，例如2008/2009年的经济衰退以及2019/2020年COVID-19关闭的影响。尤其是2019/2020年冬季和2020年春季的COVID-19锁定导致全球经济萎缩。虽然大量还原二氧化氮（NO ₂）似乎很受欢迎，很少有研究提供数值评估多少异常归因于经济放缓。此外，很少有研究涉及减少排放量下可能的气候抵消。使用来自Aura卫星上的臭氧监测仪器的16年NO ₂月度序列数据的主成分（PC）分析，我们发现，这是从主模式的反季节分层结构中获得的最佳第三种PC（PC3）与经济指标。这种耦合是正的，即PC3和经济指标表现出正的协方差。但是，经济差异只能解释PC3中40％的信息。此外，此模式本身仅解释了全部淡化的NO _2的3％变化性。因此，我们得出的结论是，在产生明确影响的同时，如果NO ₂偏离季节性平均值，则经济最多可以被评为三等重要。

一旦我们将PC3识别为与经济可变性相关的NO ₂模式，我们便以该模式为指标，并寻找对我们确信与经济可变性相关的那部分NO ₂可变性的快速气候调整。我们专注于NASA Aqua卫星上的大气红外探测器（AIRS）的观测数据，将一系列表面皮肤温度和晴朗的长波辐射（OLR）分解为主要成分，并确定NO ₂ PC3的潜在影响在这些气候变量上。