Deciphering the impact of cascade reservoirs on nitrogen transport and nitrate transformation: Insights from multiple isotope analysis and machine learning

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Yufei Bao, Yuchun Wang, Mingming Hu, Peng Hu, Nanping Wu, Xiaodong Qu, Xiaobo Liu, Wei Huang, Jie Wen, Shanze Li, Meng Sun, Qian Zhang
{"title":"Deciphering the impact of cascade reservoirs on nitrogen transport and nitrate transformation: Insights from multiple isotope analysis and machine learning","authors":"Yufei Bao, Yuchun Wang, Mingming Hu, Peng Hu, Nanping Wu, Xiaodong Qu, Xiaobo Liu, Wei Huang, Jie Wen, Shanze Li, Meng Sun, Qian Zhang","doi":"10.1016/j.watres.2024.122638","DOIUrl":null,"url":null,"abstract":"Construction of cascade reservoirs has altered nutrient dynamics and biogeochemical cycles, thereby influencing the composition and productivity of river ecosystems. The Lancang River (LCR), characterized by its cascade reservoir system, presents uncertainties in nitrogen transport and nitrate transformation mechanisms. Herein, we conducted monthly monitoring of hydrochemistry and multiple stable isotopes (δ<sup>15</sup>N-NO<sub>3</sub><sup>-</sup>, δ<sup>18</sup>O-NO<sub>3</sub><sup>-</sup>, δ<sup>18</sup>O-H<sub>2</sub>O, δD-H<sub>2</sub>O) throughout 2019 in both the natural river reach (NRR) and cascade reservoirs reach (CRR) of the LCR. Through the monthly detection of nitrogen forms and runoff in the import (M2) and export (M9) section, the average annual retention ratios for Total nitrogen (TN), Nitrate nitrogen (NO<sub>3</sub><sup>-</sup>-N), Particulate Nitrogen (PN) and Ammonium Nitrogen (NH<sub>4</sub><sup>+</sup>-N) were about -35%, -53%, 48% and -65%, respectively. The retention rates were positively correlated with hydraulic retention time and negatively correlated with reservoir age, especially in the flood season. Compared to the NRR, the reservoir had significantly affected the nitrogen transport characteristics, especially for the large reservoirs (like Xiaowan and Nuozhadu), which enhanced phytoplankton uptake of NO<sub>3</sub><sup>-</sup>-N to form PN capabilities in the lentic environment and subsequently to precipitate or intercept it at the reservoir. This led to the overall decreasing trend of TN and PN concentrations along the CRR. The Bayesian stable isotope model quantified NO<sub>3</sub><sup>-</sup>-N sources from the NRR to the CRR. During this transition, soil nitrogen (SN) ratios decreased from 69.3% to 61.8%, while Manure &amp; sewage (M&amp;S) increased from 24.0% to 31.3%. Anthropogenic and natural factors, including urban sewage discharge, population density, and precipitation, were selected as key predictor variables. The eXtreme Gradient Boosting (XGBoost) model exhibited superior predictive performance for NO<sub>3</sub><sup>-</sup>-N concentrations, achieving an R<sup>2</sup> of 0.70. These findings deepen our understanding of the impact of reservoirs on river ecology.","PeriodicalId":443,"journal":{"name":"Water Research","volume":null,"pages":null},"PeriodicalIF":11.4000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.watres.2024.122638","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Construction of cascade reservoirs has altered nutrient dynamics and biogeochemical cycles, thereby influencing the composition and productivity of river ecosystems. The Lancang River (LCR), characterized by its cascade reservoir system, presents uncertainties in nitrogen transport and nitrate transformation mechanisms. Herein, we conducted monthly monitoring of hydrochemistry and multiple stable isotopes (δ15N-NO3-, δ18O-NO3-, δ18O-H2O, δD-H2O) throughout 2019 in both the natural river reach (NRR) and cascade reservoirs reach (CRR) of the LCR. Through the monthly detection of nitrogen forms and runoff in the import (M2) and export (M9) section, the average annual retention ratios for Total nitrogen (TN), Nitrate nitrogen (NO3--N), Particulate Nitrogen (PN) and Ammonium Nitrogen (NH4+-N) were about -35%, -53%, 48% and -65%, respectively. The retention rates were positively correlated with hydraulic retention time and negatively correlated with reservoir age, especially in the flood season. Compared to the NRR, the reservoir had significantly affected the nitrogen transport characteristics, especially for the large reservoirs (like Xiaowan and Nuozhadu), which enhanced phytoplankton uptake of NO3--N to form PN capabilities in the lentic environment and subsequently to precipitate or intercept it at the reservoir. This led to the overall decreasing trend of TN and PN concentrations along the CRR. The Bayesian stable isotope model quantified NO3--N sources from the NRR to the CRR. During this transition, soil nitrogen (SN) ratios decreased from 69.3% to 61.8%, while Manure & sewage (M&S) increased from 24.0% to 31.3%. Anthropogenic and natural factors, including urban sewage discharge, population density, and precipitation, were selected as key predictor variables. The eXtreme Gradient Boosting (XGBoost) model exhibited superior predictive performance for NO3--N concentrations, achieving an R2 of 0.70. These findings deepen our understanding of the impact of reservoirs on river ecology.

Abstract Image

解密级联水库对氮迁移和硝酸盐转化的影响:多同位素分析和机器学习的启示
梯级水库的建设改变了养分动力学和生物地球化学循环,从而影响了河流生态系统的组成和生产力。以梯级水库系统为特征的澜沧江(LCR)在氮迁移和硝酸盐转化机制方面存在不确定性。在此,我们对澜沧江天然河段(NRR)和梯级水库河段(CRR)进行了水化学和多种稳定同位素(δ15N-NO3-、δ18O-NO3-、δ18O-H2O、δD-H2O)的月度监测。通过对进口段(M2)和出口段(M9)氮形态和径流的月度检测,总氮(TN)、硝酸盐氮(NO3--N)、颗粒氮(PN)和铵态氮(NH4+-N)的年均滞留率分别约为-35%、-53%、48%和-65%。滞留率与水力滞留时间呈正相关,与库龄呈负相关,尤其是在汛期。与泥沙淤积率相比,水库对氮迁移特性有明显的影响,尤其是大型水库(如小湾水库和糯扎渡水库),它增强了浮游植物吸收 NO3-N 在湖泊环境中形成 PN 的能力,并随后在水库中沉淀或拦截。这就导致了中沙河沿岸 TN 和 PN 浓度的总体下降趋势。贝叶斯稳定同位素模型量化了从北回归线到中游河段的 NO3-N 来源。在这一转变过程中,土壤氮(SN)比率从69.3%下降到61.8%,而粪便和污水(M&S)比率从24.0%上升到31.3%。人类活动和自然因素,包括城市污水排放、人口密度和降水量,被选为关键的预测变量。极端梯度提升(XGBoost)模型在预测 NO3-N 浓度方面表现出色,R2 达到 0.70。这些发现加深了我们对水库对河流生态影响的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信