Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural Network.
Journal:
Environmental science & technology
PMID:
39936390
Abstract
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 0.96 for CH in sewers and 0.82 for NO in WWTPs. Model interpretability analysis revealed fluctuations in contributions of the anaerobic hydrolysis acidification pathway to CH production and the nitrification-denitrification pathway to NO production under dynamic environmental conditions, guiding the formulation of a precise dissolved oxygen control strategy targeting critical water quality parameters (acetate for CH production and nitrite for NO production). Implementing this strategy to control DO thereby regulating biological production pathway contributions, CH production in sewers and NO production in WWTPs were reduced by 35.50% and 29.94%, respectively. Our findings offer a robust, accurate method for predicting GHG emissions, quantifying production pathway contributions, and developing effective control strategies in UDGSs.