Publication
AGU 2024
Poster

Top-Down Finer-Scale CO2 Emission Across Nations

Abstract

Elevated atmospheric carbon dioxide (CO2)(CO{_2}) levels contribute to global warming, necessitating urgent emission reduction. Identifying CO2CO{_2} sources is crucial. This study develops end-to-end models for high-resolution national CO2CO{_2} estimation using remote sensing. Our methodology involves three steps. First, a machine learning-based model establishes relationships between satellite-derived column average CO2CO{_2} (XCO2)(XCO{_2}) and weather conditions, including anthropogenic proxies. This model generates daily 1 km2 spatial XCO2XCO{_2} maps. The second step separates dominant accumulated XCO2 (XCO2bg)XCO{_2} \ (XCO{_2}{^b}{^g}) and regional enhancement (ΔXCO2)({\Delta}XCO2) due to anthropogenic activities, challenging due to ΔXCO2{\Delta}XCO{_2} being small (ΔXCO2<<XCO2bg)({\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 XCO2NO2XCO{_2}-NO{_2} relationships for ΔXCO2{\Delta}XCO{_2} maps at a weekly frequency. The final step involves ΔXCO2{\Delta}XCO{_2} to CO2CO{-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.