Abstract：
Nitrogen pollution has emerged as a significant threat to the health of global river systems, garnering considerable attention. However, numerous challenges persist in understanding the characteristics and predicting the spatial changes of total nitrogen (TN) at the catchment scale. We leveraged data from 530 monitoring sections to calculate a land-use composite index and perform statistical analyses to explore the primary factors influencing nitrogen enrichment in the Yangtze River Watershed. We developed three machine learning models to forecast future TN concentrations at monitoring points. Our results showed that agricultural activities and rainfall were the primary drivers of monthly variations in TN concentrations. The upstream region of the watershed exhibited larger variations in TN concentrations (0.097 to 11.099 mg/L), significantly higher than the middle and downstream areas (0.348 to 6.844 mg/L). Microbial-mediated organic matter decomposition in sediment and changes in land-use were identified as key contributors to regional differences in nitrogen enrichment. Potential nitrogen sources include sediment release, urban sewage, and agricultural fertilization. Random Forest model achieved a prediction accuracy of 77.6 %, surpassing the BP and LSTM models. We identified 37 high-risk areas of nitrogen enrichment, concentrated in the Chengdu-Chongqing, Yunnan-Central urban cluster, and the Chaohu Lake sub-watershed. Increased urban land-use and industrial inputs primarily influenced nitrogen enrichment in the upstream area, while agricultural inputs were the main drivers in the middle and downstream regions. Our multi-machine learning models identified the relationship between TN and influencing factors, providing a reliable method for assessing nitrogen enrichment risk in the watershed.
摘要：
氮污染已成为全球河流系统健康的重大威胁，获得相当多的关注。然而,在流域尺度上理解总氮（TN）的特征和预测其空间变化方面仍然存在许多挑战。我们利用530个监测断面的数据计算了土地利用综合指数，并进行了统计分析，以探讨影响长江流域氮富集的主要因素。我们开发了三种机器学习模型来预测监测点未来的TN浓度。我们的结果表明，农业活动和降雨是TN浓度每月变化的主要驱动因素。流域上游区域的TN浓度变化较大（0.097至11.099mg/L），显著高于中下游地区（0.348～6.844mg/L）。沉积物中微生物介导的有机质分解和土地利用变化被确定为氮富集区域差异的主要原因。潜在的氮源包括沉积物释放，城市污水,农业施肥。随机森林模型的预测精度达到77.6%,超越BP和LSTM模型。我们确定了37个氮富集的高风险区域，集中在成渝，云南中部城市群,和巢湖子流域。城市土地利用和工业投入的增加主要影响上游地区的氮富集，而农业投入是中下游地区的主要驱动因素。我们的多机器学习模型确定了TN与影响因素之间的关系，为流域氮富集风险评估提供了可靠的方法。
