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.