College of Water Sciences, Beijing Normal University, Beijing 100875, China.
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; Innovation Research Center of Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
J Contam Hydrol. 2024 Jan;260:104282. doi: 10.1016/j.jconhyd.2023.104282. Epub 2023 Dec 12.
Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH-N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China's surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake's water quality. To ensure the continued health of Hulun Lake's water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.
呼伦湖面临严重的水质退化，需要进行有效的安全监测。传统方法在空间和时间上的覆盖范围不足，因此需要建立遥感模型。本研究利用 Landsat 8 OLI 数据集，综合了横断面监测和野外采样数据。采用随机森林算法，建立了呼伦湖 6 个水质参数的遥感反演模型：叶绿素-a（Chl-a）、总氮（TN）、总磷（TP）、氨氮（NH-N）、化学需氧量（COD）和溶解氧（DO）。该模型应用于 2016 年至 2021 年呼伦湖非冻结期，表现出良好的性能，并生成了高分辨率的地图。时间序列分析表明，在研究期间，呼伦湖的 TN、TP 和 COD 污染水平极为严重，超过了中国地表水环境质量标准中 V 类水的标准。区域分析表明，湖心区的污染物浓度低于入湖口。高污染的入湖河流对呼伦湖的水质造成了不利影响。为了确保呼伦湖水质的持续健康，需要密切监测湖水水质，并采取必要措施防止进一步恶化。本研究对制定和执行呼伦湖生态保护和恢复策略具有重要意义。
