School of Geography and the Environment, University of Oxford, Oxford, UK; Environmental Change Institute, University of Oxford, Oxford, UK; Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.
School of Geography and the Environment, University of Oxford, Oxford, UK; Environmental Change Institute, University of Oxford, Oxford, UK.
Sci Total Environ. 2023 Apr 10;868:161623. doi: 10.1016/j.scitotenv.2023.161623. Epub 2023 Jan 16.
Anthropogenic loading of nitrogen to river systems can pose serious health hazards and create critical environmental threats. Quantification of the magnitude and impact of freshwater nitrogen requires identifying key controls of nitrogen dynamics and analyzing both the past and present patterns of nitrogen flows. To tackle this challenge, we adopted a machine learning (ML) approach and built an ML-driven representation that captures spatiotemporal variability in nitrogen concentrations at global scale. Our model uses random forests to regress a large sample of monthly measured stream nitrogen concentrations onto a set of 17 predictors with a spatial resolution of 0.5-degree over the 1990-2013, including observations within the pixel and upstream drivers. The model was validated with data from rivers outside the training dataset and was used to predict nitrogen concentrations in 520 major river basins of the world, including many with scarce or no observations. We predicted that the regions with highest median nitrogen concentrations in their rivers (in 2013) were: United States (Mississippi), Pakistan, Bangladesh, India (Indus, Ganges), China (Yellow, Yangtze, Yongding, Huai), and most of Europe (Rhine, Danube, Vistula, Thames, Trent, Severn). Other major hotspots were the river basins of the Sebou (Morroco), Nakdong (South Korea), Kitakami (Japan), and Egypt's Nile Delta. Our analysis showed that the rate of increase in nitrogen concentration between 1990s and 2000s was greatest in rivers located in eastern China, eastern and central parts of Canada, Baltic states, Pakistan, mainland southeast Asia, and south-eastern Australia. Using a new grouped variable importance measure, we also found that temporality (month of the year and cumulative month count) is the most influential predictor, followed by factors representing hydroclimatic conditions, diffuse nutrient emissions from agriculture, and topographic features. Our model can be further applied to assess strategies designed to reduce nitrogen pollution in freshwater bodies at large spatial scales.
人为向河流系统输入氮会带来严重的健康危害，并造成重大的环境威胁。量化淡水氮的规模和影响需要确定氮动态的关键控制因素，并分析过去和现在的氮流动模式。为了应对这一挑战，我们采用了机器学习（ML）方法，并构建了一个 ML 驱动的表示，该表示可以捕获全球范围内氮浓度的时空变化。我们的模型使用随机森林来将大量的月度实测溪流氮浓度数据回归到一组 17 个预测因子上，这些预测因子的空间分辨率为 0.5 度，时间范围为 1990-2013 年，包括像素内的观测值和上游驱动因素。该模型在训练数据集之外的河流数据上进行了验证，并用于预测世界上 520 个主要河流流域的氮浓度，其中包括许多观测数据稀缺或没有观测数据的流域。我们预测，2013 年河流中氮浓度中位数最高的地区是：美国（密西西比河）、巴基斯坦、孟加拉国、印度（印度河、恒河、扬子江、永定河、淮河）和中国大部分地区（黄河、长江、永定河、淮河）以及欧洲大部分地区（莱茵河、多瑙河、维斯瓦河、泰晤士河、特伦特河、塞文河）。其他主要热点是摩洛哥的塞布河、韩国的礼东江、日本的北上川和埃及的尼罗河三角洲。我们的分析表明，20 世纪 90 年代至 21 世纪初，氮浓度增长率最高的河流位于中国东部、加拿大东部和中部、波罗的海国家、巴基斯坦、东南亚大陆和澳大利亚东南部。使用新的分组变量重要性度量方法，我们还发现，时间性（一年中的月份和累积月份数）是最具影响力的预测因子，其次是代表水热条件、农业面源营养物排放和地形特征的因子。我们的模型可以进一步应用于评估旨在减少大空间尺度淡水中氮污染的策略。
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