Black carbon plumes from gas flaring in North Africa identified from multi-spectral imagery with deep learning

TL;DR

This study introduces a deep learning approach to directly detect and quantify black carbon emissions from gas flaring in North Africa using satellite imagery, enabling real-time monitoring and policy implementation.

Contribution

It presents the first direct satellite-based detection and quantification of black carbon emissions from gas flaring using deep learning on Sentinel-2 imagery.

Findings

Black carbon emissions in North Africa are approximately 1 million tons CO2 equivalent.

Over 25% of emissions originate from just 10 sites.

The method enables operational monitoring of flaring emissions.

Abstract

Black carbon (BC) is an important pollutant aerosol emitted by numerous human activities, including gas flaring. Improper combustion in flaring activities can release large amounts of BC, which is harmful to human health and has a strong climate warming effect. To our knowledge, no study has ever directly monitored BC emissions from satellite imagery. Previous works quantified BC emissions indirectly, by applying emission coefficients to flaring volumes estimated from satellite imagery. Here, we develop a deep learning framework and apply it to Sentinel-2 imagery over North Africa during 2022 to detect and quantify BC emissions from gas flaring. We find that BC emissions in this region amount to about 1 million tCO$_{2,\mathrm{eq}}$, or 1 million passenger cars, more than a quarter of which are due to 10 sites alone. This work demonstrates the operational monitoring of BC emissions from flaring, a key step in implementing effective mitigation policies to reduce the climate impact of oil and gas operations.