Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural Network.

Deciphering and mitigating dynamic greenhouse gas (GHG) emissions under environmental fluctuation in urban drainage systems (UDGSs) is challenging due to the absence of a high-prediction model that accurately quantifies the contributions of biological production pathways. Here we infused biological production pathways into the graph neural network (GNN) model architecture, developing ecological knowledge-infused GNN (EcoGNN-GHG) models to evaluate methane (CH) and nitrous oxide (NO) production in sewers and wastewater treatment plants (WWTPs). The EcoGNN-GHG model demonstrated high predictive accuracy, achieving an of 0.