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