Tian Jiaxin, Yuan Ze, Mao Xiaoteng, Ma Ting
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, PR China.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, PR China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, 210023, PR China.
Environ Pollut. 2025 Oct 1;382:126641. doi: 10.1016/j.envpol.2025.126641. Epub 2025 Jun 7.
Understanding the spatiotemporal dynamics of riverine nitrogen and its responses to socioeconomic and hydroclimatic variability is crucial for sustainable water management, particularly in water-scarce basins facing increasing anthropogenic pressures. This study investigated multiple time-scale fluctuations in surface total nitrogen (TN) concentration and load in the Yellow River Basin (YRB) from 2019 to 2022, utilizing high-frequency water monitoring data. We compiled various anthropogenic pollution sources and natural factors, including climate, topography, and hydrology across sub-basins. Random forest models were employed to identify drivers of TN variations, and a spatiotemporal statistical model quantified anthropogenic contributions to TN loads. Our results indicated that approximately 70 % of daily TN concentrations exceeded China's Class V water quality standard. Riverine TN concentrations and loads exhibited distinct seasonal patterns, with peak concentrations during the dry season and maximum loads during the wet season. Notably, 45-85 % of annual TN loads were exported under high-flow and wet conditions. Precipitation emerged as the dominant driver of monthly concentration changes, while variations in upstream loads primarily influenced monthly load changes. Atmospheric nitrogen deposition, often overlooked in water pollution assessments, was an important contributor to annual TN loads in populated areas. These findings emphasize the need for strengthened monitoring and pollution control measures in the YRB to address challenges posed by human activities and climate change.
了解河流氮素的时空动态及其对社会经济和水文气候变异性的响应对于可持续水资源管理至关重要，特别是在面临日益增加的人为压力的缺水流域。本研究利用高频水质监测数据，调查了2019年至2022年黄河流域（YRB）地表总氮（TN）浓度和负荷的多时间尺度波动。我们汇总了各子流域的各种人为污染源和自然因素，包括气候、地形和水文。采用随机森林模型识别TN变化的驱动因素，并使用时空统计模型量化人为因素对TN负荷的贡献。我们的结果表明，大约70%的每日TN浓度超过了中国的V类水质标准。河流TN浓度和负荷呈现出明显的季节性模式，旱季浓度最高，雨季负荷最大。值得注意的是，45%-85%的年度TN负荷是在高流量和湿润条件下输出的。降水是月度浓度变化的主要驱动因素，而上游负荷变化主要影响月度负荷变化。在水污染评估中经常被忽视的大气氮沉降是人口密集地区年度TN负荷的重要贡献因素。这些发现强调了黄河流域需要加强监测和污染控制措施，以应对人类活动和气候变化带来的挑战。
