沉积物内源营养盐释放机制及其研究方法综述

A Review of Sediment Endogenous Nutrient Release Mechanisms and Research Methodologies

摘要: 湖泊作为关键淡水资源库，其富营养化治理因沉积物内源释放的“源–汇”双重特性而面临严峻挑战。沉积物通过吸附–沉降过程累积营养盐，构成污染物的“汇”，但在环境扰动下又可转化为加剧富营养化的持续性“源”。当前沉积物释放的研究方法分为理论模型(质量平衡、分子扩散、反应–输送模型)与实验模拟(柱芯培养、水底培养箱、动力水槽法)两大体系。理论法擅长大尺度预测，但受参数敏感性限制；实验法生态保真度高，却难以复现动态扰动。分子扩散模型因边界层厚度(Z)误差易致通量偏差>25%，柱芯培养法受限于静态模拟与壁面效应。新兴技术如数字孪生系统通过整合遥感与传感器数据，将内源负荷预测周期缩短至周尺度(如红枫湖案例)，展现多源数据融合潜力。未来研究需突破三大瓶颈：建立边界层厚度标准化参数体系，发展机器学习优化的扩散模型；研发高分辨原位监测技术(光纤阵列与声学多普勒联用)；构建沉积物通量大数据平台，评估气候变化对内源负荷的累积效应。通过跨学科融合与理论–技术–数据整合，可为富营养化防控提供精准决策支撑。

Abstract: Lakes, as critical freshwater reservoirs, face severe challenges in eutrophication control due to the dual “source-sink” characteristics of sediment endogenous nutrient release. Sediments accumulate nutrients through adsorption-sedimentation processes, acting as a “sink” for pollutants, yet they transform into persistent “sources” exacerbating eutrophication under environmental disturbances. Current research methodologies for sediment release are categorized into two systems: theoretical models (mass balance, molecular diffusion, and reactive transport models) and experimental simulations (core incubation, benthic chambers, hydrodynamic flume experiments). Theoretical approaches excel in large-scale predictions but are constrained by parameter sensitivity, while experimental methods offer high ecological fidelity yet struggle to replicate dynamic disturbances. Molecular diffusion models may yield flux deviations exceeding 25% due to errors in boundary layer thickness (Z), and core incubation methods are limited by static simulations and wall effects. Emerging technologies like digital twin systems, integrating remote sensing and sensor data, have shortened the prediction cycle for endogenous nutrient loads to a weekly scale (e.g., the Hongfeng Lake case), demonstrating the potential of multi-source data fusion. Future research must address three key bottlenecks: establishing standardized parameters for boundary layer thickness, developing machine learning-optimized diffusion models, advancing high-resolution in-situ monitoring technologies (e.g., fiber-optic arrays coupled with acoustic Doppler profilers), and constructing big data platforms for sediment flux analysis to assess cumulative climate change impacts on endogenous loads. Cross-disciplinary integration of theory, technology, and data will provide precise decision-making support for eutrophication mitigation.