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. We use a convolutional neural network (CNN) to identify plumes of nitrogen dioxide (NO2), a tracer of combustion, fromNO2 column data collected by the TROPOspheric Monitoring Instrument(TROPOMI). This approach allows us to exploit efficiently the growingvolume of satellite data available to characterize Earth’sclimate. For the purposes of demonstration, we focus on data collectedbetween July 2018 and June 2020. We train the deep learning modelusing six thousand 28 × 28 pixel images of TROPOMI data(corresponding to ≃ 266 km × 133 km) and find that themodel can identify plumes with a success rate of more than 90 %. Over our study period, we find over 310 000 individual NO2 plumes, of which≃ 19 % are found over mainland China. We have attempted to remove the influence of open biomass burning using correlative high-resolutionthermal infrared data from the Visible Infrared Imaging RadiometerSuite (VIIRS). We relate the remaining NO2 plumes to large urbancentres, oil and gas production, and major power plants. We find nocorrelation between NO2 plumes and the location of natural gasflaring. We also find persistent NO2 plumes from regions whereinventories do not currently include emissions. Using an establishedanthropogenic CO2 emission inventory, we find that our NO2 plumedistribution captures 92 % of total CO2 emissions, with the remaining 8 % mostly due to a large number of small sources (&lt; 0.2 g C m−2 d−1)to which our NO2 plume model is less sensitive. We argue that the underlyingCNN approach could form the basis of a Bayesian framework to estimateanthropogenic combustion emissions.

atmospheric measurement techniques

Automated detection of atmospheric NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; plumes from satellite data: a tool to help infer anthropogenic combustion emissions