Molecular-Level Insights into the Spatial Distribution, Transformation, and Potential Sources of Dissolved Organic...

Dissolved organic nitrogen (DON) is an important component of the aquatic nitrogen pool, which is composed of countless molecular individuals with different ecological functions. Yet, its sources and molecular composition remain poorly understood in reservoir ecosystems. In this study, water samples were collected from different regions of the Beidagang Reservoir. Hydrochemical measurements and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were used to characterize the molecular composition, spatial distribution, and potential sources of DON. The results demonstrated that DON constituted a substantial fraction of total dissolved nitrogen and exhibited pronounced spatial variability in both concentration and molecular composition. CHO and CHON formulas were abundant across the reservoir, with carboxylic-rich alicyclic molecules (CRAM-like) representing the dominant molecular class. A total of 16,338 CHON formulas were identified, of which only 407 were shared by all samples, indicating pronounced spatial variation in DON composition. Paired mass distance (PMD) analysis further showed that approximately 60% of the DON formulas could be classified as potential precursors or products. Amine reactions accounted for approximately 40% of all putative transformation pairs, suggesting that transformations involving nitrogen functional groups may represent an important pathway during DON transport. Upstream DON was likely influenced by the combined effects of surface runoff, atmospheric deposition, and internal reservoir processes, whereas downstream DON appeared to be more strongly affected by anthropogenic activities and seawater-related sources. These findings revealed the molecular diversity and potential transformation pathways of DON in the Beidagang Reservoir and provided molecular-level evidence for understanding nitrogen cycling in reservoirs exposed to multiple environmental inputs, which helped predict the response of nitrogen cycling to global climate change.