Driving factors and predictive model of dissolved N₂O concentrations in a complex aquatic network

Inland water networks, comprising hydrologically integrated rivers, agricultural ditches, and aquaculture ponds, are significant N₂O sources, yet their complexity impedes accurate quantification. Here we developed an integrated framework combining structural equation modeling (SEM), machine learning (ML), and SHapley Additive exPlanations (SHAP) to bridge causal inference with nonlinear predictive modeling in China's Taihu Basin. Our results demonstrate that NO₃^--N and water temperature (WT) dominate N₂O variability, explaining significantly more variance than discrete water body categories. This framework successfully reconciled the dual role of dissolved organic carbon (DOC). SEM identifies DOC as a macroscopic sink driven by the complete denitrification of nitrate to N₂ (standardized effect = -0.143), while SHAP reveals its role as a microscopic catalyst that enhances N₂O production efficiency per unit of nitrate. Although the ensemble model achieved high accuracy (test R² = 0.70), the parsimonious model using four routine parameters (NO₃^--N, DO, NH₄⁺-N, and WT) proved more suitable for regional assessment, demonstrating satisfactory predictive capability (test R² = 0.54) and successfully reconstructing basin-wide spatiotemporal patterns. This study provides a scalable and transferable methodology for unlocking the driving mechanisms of complex aquatic ecosystems, offering a robust tool for basin-scale N₂O estimation and targeted greenhouse gas mitigation.