Abstract
Abstract. We use a convolutional neural network (CNN) to identify plumes of nitrogen dioxide (NO2), a tracer of combustion, from NO2 column data collected by the TROPOspheric Monitoring Instrument (TROPOMI). This approach allows us to exploit efficiently the growing volume of satellite data available to characterize Earth’s climate. For the purposes of demonstration, we focus on data collected between July 2018 and June 2020. We train the deep learning model using six thousand 28 × 28 pixel images of TROPOMI data (corresponding to ≃ 266 km × 133 km) and find that the model 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-resolution thermal infrared data from the Visible Infrared Imaging Radiometer Suite (VIIRS). We relate the remaining NO2 plumes to large urban centres, oil and gas production, and major power plants. We find no correlation between NO2 plumes and the location of natural gas flaring. We also find persistent NO2 plumes from regions where inventories do not currently include emissions. Using an established anthropogenic CO2 emission inventory, we find that our NO2 plume distribution captures 92 % of total CO2 emissions, with the remaining 8 % mostly due to a large number of small sources (< 0.2 g C m−2 d−1) to which our NO2 plume model is less sensitive. We argue that the underlying CNN approach could form the basis of a Bayesian framework to estimate anthropogenic combustion emissions.
Highlights
The Paris Agreement (PA) is the current inter-government vehicle that describes a progressive reduction in greenhouse gas (GHG) emissions to mitigate dangerous climate change, described as an increase larger than 2 ◦C in global mean temperature above pre-industrial values
After extracting the geographical locations for each plume location, we identified 62 040 (20 %) images that were within 15 km of an active fire as determined by Visible Infrared Imaging Radiometer Suite (VIIRS) thermal anomaly data and categorize these NO2 plumes as being associated with biomass burning
We find that anthropogenic combustion is widespread across the globe (Fig. 3a), with a focus over northern mid-latitudes, India and China, as expected
Summary
The Paris Agreement (PA) is the current inter-government vehicle that describes a progressive reduction in greenhouse gas (GHG) emissions to mitigate dangerous climate change, described as an increase larger than 2 ◦C in global mean temperature above pre-industrial values. Whether it will achieve its stated goals depends on commitments of its signatories to establish and more importantly realize stringent plans to reduce effectively national GHG emissions. The main focus of these MRV systems is anthropogenic emissions of carbon dioxide (CO2) and methane. One of the challenges faced by all these MRV systems is separating the anthropogenic and natural components of CO2 and methane fluxes. We use a deep learning model to identify automatically satellite-observed plumes of nitrogen dioxide (NO2), a proxy for combustion, to locate combustion hotspots, e.g. oil and gas industry, cities, and powerplants
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