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

Résumé fourni par la source

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 4 ) and nitrous oxide (N 2 O) production in sewers and wastewater treatment plants (WWTPs). The EcoGNN-GHG model demonstrated high predictive accuracy, achieving an R 2 of 0.96 for CH 4 in sewers and 0.82 for N 2 O in WWTPs. Model interpretability analysis revealed fluctuations in contributions of the anaerobic hydrolysis acidification pathway to CH 4 production and the nitrification-denitrification pathway to N 2 O production under dynamic environmental conditions, guiding the formulation of a precise dissolved oxygen control strategy targeting critical water quality parameters (acetate for CH 4 production and nitrite for N 2 O production). Implementing this strategy to control DO thereby regulating biological production pathway contributions, CH 4 production in sewers and N 2 O 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.

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