A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

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

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 (δN-NO, δO-NO, δO-HO, δD-HO) 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-N), Particulate Nitrogen (PN) and Ammonium Nitrogen (NH-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-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-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-N concentrations, achieving an R of 0.70. These findings deepen our understanding of the impact of reservoirs on river ecology.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.watres.2024.122638DOI Listing

Publication Analysis

Top Keywords

cascade reservoirs
12
nitrogen transport
12
nitrogen
9
transport nitrate
8
nitrate transformation
8
reservoirs
5
deciphering impact
4
cascade
4
impact cascade
4
reservoirs nitrogen
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!