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.

翻译：黑碳（BC）是一种重要的人为活动（包括天然气燃烧）排放的污染物气溶胶。燃烧过程中的不完全燃烧会释放大量黑碳，这对人体健康有害并具有强烈的气候增温效应。据我们所知，此前尚无研究通过卫星影像直接监测黑碳排放。以往研究通过将排放系数应用于基于卫星影像估算的燃烧量来间接量化黑碳排放。本文开发了一种深度学习框架，并将其应用于2022年北非地区的Sentinel-2影像，以检测和量化天然气燃烧产生的黑碳排放。我们发现，该区域黑碳排放量约相当于100万吨二氧化碳当量（tCO$_{2,\mathrm{eq}}$），或100万辆乘用车，其中超过四分之一来自仅10个燃烧点。本研究展示了黑碳排放的实时监测能力，这是实施有效减排政策以减少石油与天然气作业气候影响的关键一步。