Quantifying Thermal Power Plants Co2 Emissions Globally from Space Using Hyperspectral Imagers

Abstract

Anthropogenic carbon dioxide (CO2) emission from fossil fuel combustion has been the key driving force for the global warming in the last century. Previous studies have demonstrated the potential of satellites to quantify CO2 emissions from large discrete power plants, which constitute roughly half of the global CO2 emissions from fossil fuels. Most of these studies relied on OCO-2 or OCO-3 data, which have narrow-swath imaging coverage and kilometer-level spatial resolution. As a result, only a small fraction of global power plants could be effectively assessed. In this study, we identified and quantified a larger number of power plants CO2 emission plumes from space using two hyperspectral imagers: Earth Mineral Dust Source Investigation (EMIT) and PRecursore IperSpettrale della Missione Applicativa (PRISMA). We applied the Scene Specific Iterative Matched Filter (SSIMF) algorithm to quantify emissions from 152 power plants across 20 countries on three continents and rectified the systematic underestimation by the algorithm using high-quality hourly emission data reported by the Environmental Protection Agency (EPA) in the U.S. The bias-corrected emission fluxes from hyperspectral imagers were consistent with bottom-up inventories. In addition, we show that hyperspectral imagers can distinguish nearby sources as close as 2 km, which is challenging for coarse resolution OCO. Our study highlights the potential of hyperspectral imagers in the global monitoring of CO2 emissions from individual power plants.

Keywords: Satellite, Hyperspectral Imager, Carbon Dioxide, Power plants, Matched filter