Research reveals shifts in nitrate sources during floods in Baiyangdian Lake, indicating increased sewage contributions and nitrification risks.
Urban development and intensive human activities have led to increasingly prominent nitrogen pollution issues in the Baiyangdian Lake basin. Accurately identifying the sources of nitrate pollution is a crucial prerequisite for implementing targeted remediation strategies, while flooding further complicates this task by exacerbating the transport and mixing of multi-source pollutants within the basin. This study, conducted from August to October 2023 (encompassing flood and post-flood periods), established 20 sampling sites in the lake area and its major inflow rivers. By integrating hydrochemical parameters, nitrate dual-isotope tracers (δ15N-NO3− and δ18O-NO3−), and the Bayesian mixing model (MixSIAR), we quantitatively revealed the contributions of nitrate sources and their response mechanisms to a major flood event. The results indicate that domestic sewage and livestock wastewater (Manure & Sewage, MS) were the dominant sources of nitrate, with an average contribution of 84.0%, which further increased to 90.3% after the flood. Soil nitrogen was a secondary source (average 12.3%), while contributions from chemical fertilizers and atmospheric deposition were negligible (<4%). The results quantified a flood-driven dynamic response process of the nitrate source structure, characterized by “dilution-mixing-pollution rebound-process transformation”: the initial flood stage (August) showed multi-source mixing; the post-flood period (September) witnessed a rapid rebound of sewage sources; and during the October, nitrification persisted, but the basin’s overall denitrification capacity was limited, indicating a risk of nitrogen accumulation. Spatially, rivers like the Fu River were identified as key input pathways. This study revises the traditional understanding by emphasizing the absolute dominance of sewage sources after extreme hydrological events and the risk of insufficient denitrification capacity. The findings provide a scientific basis for water quality management in Baiyangdian and similar lakes.
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Zhang et al. (2025) studied this question.
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