تقرير
Black carbon plumes from gas flaring in North Africa identified from multi-spectral imagery with deep learning
| العنوان: | Black carbon plumes from gas flaring in North Africa identified from multi-spectral imagery with deep learning |
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| المؤلفون: | Alexandre, Tuel, Thomas, Kerdreux, Louis, Thiry |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval |
| الوصف: | 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. Comment: Published at the workshop Tackling Climate Change with Machine Learning at ICLR 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.06183 |
| رقم الأكسشن: | edsarx.2406.06183 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |