Top-Down Finer-Scale CO2 Emission Across Nations

Abstract

Elevated atmospheric carbon dioxide $(CO{_2})$ levels contribute to global warming, necessitating urgent emission reduction. Identifying $CO{_2}$ sources is crucial. This study develops end-to-end models for high-resolution national $CO{_2}$ estimation using remote sensing. Our methodology involves three steps. First, a machine learning-based model establishes relationships between satellite-derived column average $CO{_2}$ $(XCO{_2})$ and weather conditions, including anthropogenic proxies. This model generates daily 1 km2 spatial $XCO{_2}$ maps. The second step separates dominant accumulated $XCO{_2} \ (XCO{_2}{^b}{^g})$ and regional enhancement $({\Delta}XCO2)$ due to anthropogenic activities, challenging due to ${\Delta}XCO{_2}$ being small $({\Delta}XCO2 << XCO{_2}{^b}{^g})$ and often near measurement noise. Addressing this, we adopt a geometrically connected segmentation to identify emission and non-emission sources, establishing $XCO{_2}-NO{_2}$ relationships for ${\Delta}XCO{_2}$ maps at a weekly frequency. The final step involves ${\Delta}XCO{_2}$ to $CO{-2}$ emission conversion, challenging due to dispersion processes. We customize an integrated mass balance method for weekly, 1 km2 spatial $XCO{_2}) emissions mapping. Our approach aligns closely with reported annual the Kingdom of Saudi Arabia (KSA) emissions, showcasing high-resolution emissions tracking and a departure from traditional bottom-up approaches, enabling near real-time (NRT) finer to country-level emission monitoring, circumventing delays associated with annual reporting.