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Identification of river nitrogen trends and key factors in the Haihe River
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Hao WANG1, Kai-lu GAO1, Gao-fei YIN1, Xue-ying SUN2, Yi-hang LIU1, Xu-lin ZHANG1, Feng WANG3, Meng-yu ZHAI4, Jia-bao WANG1, Zhao-hui LI1, Hong-da WEN1, Wen-chao LI1, *
China Environmental Science | 2025, 45(7) : 3968 - 3977
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China Environmental Science | 2025, 45(7): 3968-3977
• Environmental Ecology •
Identification of river nitrogen trends and key factors in the Haihe River
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Hao WANG1, Kai-lu GAO1, Gao-fei YIN1, Xue-ying SUN2, Yi-hang LIU1, Xu-lin ZHANG1, Feng WANG3, Meng-yu ZHAI4, Jia-bao WANG1, Zhao-hui LI1, Hong-da WEN1, Wen-chao LI1, *
Affiliations
  • 1.Key Laboratory of Ecological Environment of Farmland in Hebei, State Key Laboratory of North China Crop Improvement and Regulation, College of Resources and Environmental Sciences, Hebei Agricultural University, Baoding 071000, China
  • 2.School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  • 3.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 4.Institute of Circular Economy, Beijing University of Technology, Beijing 100124, China
Published: 2025-07-20
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This study constructed a data-driven RF-LSTM model to predict Total Nitrogen (TN) concentrations in the Haihe River, based on monitoring data from 69 water quality stations collected between 2021 and 2024. By coupling the Random Forest (RF) algorithm with a Long Short-Term Memory (LSTM) neural network, the model also explored the spatiotemporal variation and key influencing factors of TN. Results showed that TN concentrations were relatively high in 2021 and lower in 2022, with annual averages ranging from 4.97 to 5.22mg/L. Across the five watersheds, TN levels displayed a U-shaped seasonal pattern, peaking in January and February and reaching the lowest levels in August. Temperature and dissolved oxygen were identified as the main influencing factors. TN concentration showed a highly significant negative correlation with temperature and a highly significant positive correlation with dissolved oxygen. The RF-LSTM model outperformed the traditional standalone LSTM model in prediction accuracy (R2:0.830~0.939, R2:0.818~0.931) and demonstrated stronger generalization performance.

Haihe River  /  temporal and spatial patterns of change  /  influence factors  /  machine learning
汪昊, 高凯璐, 尹高飞, 孙学莹, 刘一航, 张旭琳, 王凤, 翟梦瑜, 王佳宝, 李昭慧, 文宏达, 李文超. 海河流域河流氮素变化趋势及关键因子识别. 中国环境科学, 2025 , 45 (7) : 3968 -3977 .
Hao WANG, Kai-lu GAO, Gao-fei YIN, Xue-ying SUN, Yi-hang LIU, Xu-lin ZHANG, Feng WANG, Meng-yu ZHAI, Jia-bao WANG, Zhao-hui LI, Hong-da WEN, Wen-chao LI. Identification of river nitrogen trends and key factors in the Haihe River[J]. China Environmental Science, 2025 , 45 (7) : 3968 -3977 .
Appendix
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Year 2025 volume 45 Issue 7
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Article Info
  • Receive Date：2024-12-02
  • Online Date：2026-10-08
  • Published：2025-07-20
Article Data
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History
  • Received：2024-12-02
Funding
Affiliations
    1.Key Laboratory of Ecological Environment of Farmland in Hebei, State Key Laboratory of North China Crop Improvement and Regulation, College of Resources and Environmental Sciences, Hebei Agricultural University, Baoding 071000, China
    2.School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
    3.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    4.Institute of Circular Economy, Beijing University of Technology, Beijing 100124, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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