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A framework for tracing the sources of nitrate in surface water through remote sensing data coupled with machine learning.

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Název: A framework for tracing the sources of nitrate in surface water through remote sensing data coupled with machine learning.
Autoři: Tian, Di, Zhao, Xinfeng, Gao, Lei, Jiang, Tao, Liang, Zuobing, Yang, Zaizhi, Zhang, Pengcheng, Wu, Qirui, Ren, Kun, Yang, Chenchen, Li, Rui, Li, Shaoheng, Cao, Yingjie, Xuan, Yingxue, Chen, Jianyao, Zhu, Aiping
Zdroj: NPJ Clean Water; 5/21/2025, Vol. 8 Issue 1, p1-13, 13p
Témata: NITRATES, REMOTE sensing, NITROGEN cycle, ENVIRONMENTAL monitoring, STABLE isotope analysis, BODIES of water, MACHINE learning, POLLUTION source apportionment
Abstrakt: As an integral component of the global nitrogen cycle, nitrate are readily transferred from urban sewage discharge, agricultural activities, and atmospheric sedimentation to surface water. This paper introduces an innovative framework that combines multi-source remote sensing technology with stable nitrate nitrogen (δ15N-NO3−) and oxygen (δ18O-NO3−) isotopes mixing model, to identify nitrate sources quantitatively in surface water for the first time (R2 range from 0.50 to 0.99, RMSE range from 0.05‰ to 2.31‰, MAE range from 0.03‰ to 1.35‰). By reconstructing the historical nitrate isotopes from 2006 to 2023, we found that manure and sewage were the main contributing sources, followed by soil nitrogen, fertilizer and atmospheric deposition (contribution ratio of 3.5:2.5:2.5:1.5), wastewater discharge and fertilizer application in Xijiang river had a significant impact on this. This framework fills a gap in the research pertaining to remote sensing technology's identification of surface nitrate sources, facilitating straightforward and user-friendly forecasting of nitrate source spatio-temporal sequences. [ABSTRACT FROM AUTHOR]
Copyright of NPJ Clean Water is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A framework for tracing the sources of nitrate in surface water through remote sensing data coupled with machine learning.
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  Data: NPJ Clean Water; 5/21/2025, Vol. 8 Issue 1, p1-13, 13p
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  Data: As an integral component of the global nitrogen cycle, nitrate are readily transferred from urban sewage discharge, agricultural activities, and atmospheric sedimentation to surface water. This paper introduces an innovative framework that combines multi-source remote sensing technology with stable nitrate nitrogen (δ<superscript>15</superscript>N-NO<subscript>3</subscript><superscript>−</superscript>) and oxygen (δ<superscript>18</superscript>O-NO<subscript>3</subscript><superscript>−</superscript>) isotopes mixing model, to identify nitrate sources quantitatively in surface water for the first time (R<superscript>2</superscript> range from 0.50 to 0.99, RMSE range from 0.05‰ to 2.31‰, MAE range from 0.03‰ to 1.35‰). By reconstructing the historical nitrate isotopes from 2006 to 2023, we found that manure and sewage were the main contributing sources, followed by soil nitrogen, fertilizer and atmospheric deposition (contribution ratio of 3.5:2.5:2.5:1.5), wastewater discharge and fertilizer application in Xijiang river had a significant impact on this. This framework fills a gap in the research pertaining to remote sensing technology's identification of surface nitrate sources, facilitating straightforward and user-friendly forecasting of nitrate source spatio-temporal sequences. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of NPJ Clean Water is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1038/s41545-025-00473-3
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      – Code: eng
        Text: English
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        PageCount: 13
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    Subjects:
      – SubjectFull: NITRATES
        Type: general
      – SubjectFull: REMOTE sensing
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      – SubjectFull: NITROGEN cycle
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      – SubjectFull: ENVIRONMENTAL monitoring
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      – SubjectFull: MACHINE learning
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      – SubjectFull: POLLUTION source apportionment
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