FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224022173> ?p ?o ?g. }

• W4224022173 endingPage "127829" @default.
• W4224022173 startingPage "127829" @default.
• W4224022173 abstract "Identifying the sources and transformations of riverine nitrate plays a critical role in mitigating nitrogen enrichment of river networks. Several previous studies have used δ18O-NO3− to quantitatively assess riverine nitrate contributed by atmospheric nitrate and terrestrial sources, but their results have great uncertainty due to the wide range of δ18O-NO3− values and isotopic fractionation during nitrogen-cycling processes in terrestrial environment. The nitrate 17O anomaly (△17O-NO3−), as an unambiguous tracer of atmospheric nitrate, is a promising tool to effectively separate atmospheric nitrate from microbially produced nitrate. However, to our knowledge, △17O-NO3− approach has not been previously applied to identify nitrate pollution sources in plain river networks of eastern China. In this study, we used a multiple isotope approach (δD/δ18O-H2O and δ15N/δ18O/△17O-NO3−) for the first time to quantitatively identify sources and transformations of riverine nitrate in a hypereutrophic coastal plain river network namely Wen-Rui Tang River located in eastern China, which is a region receiving high inputs of atmospheric nitrogen deposition. The △17O-NO3− values in precipitation and river water during the study period (April–June of 2021) varied from 14.83‰ to 31.39‰ and from − 2.82‰ to 9.66‰, respectively. The δD/δ18O-H2O values revealed that river water mainly originated from recent precipitation with little evaporation. Moreover, the δ15N/δ18O-NO3− values indicated that microbial nitrification, not denitrification, was the predominant nitrogen-cycling process in the watershed. Based on a Bayesian mixing model (Stable Isotope Analysis in R, SIAR) using δ15N/△17O-NO3−, municipal sewage was identified as the dominant nitrate source (50.5 ± 11.7%), followed by soil nitrogen (23.8 ± 13.7%), atmospheric nitrate deposition (14.3 ± 2.9%), and nitrogen fertilizer (11.4 ± 8.7%). Finally, an uncertainty analysis for nitrate source apportionment demonstrated that the greatest uncertainty was associated with soil nitrogen, followed by municipal sewage, nitrogen fertilizer, and atmospheric nitrate deposition. This study provides important scientific information on riverine nitrate source apportionment to guide pollution control/remediation strategies and highlights the benefits of utilizing △17O-NO3− to enhance nitrate source apportionment." @default.
• W4224022173 created "2022-04-19" @default.
• W4224022173 creator A5011805813 @default.
• W4224022173 creator A5025704414 @default.
• W4224022173 creator A5032175744 @default.
• W4224022173 creator A5032769690 @default.
• W4224022173 creator A5033673090 @default.
• W4224022173 creator A5039537374 @default.
• W4224022173 creator A5048224337 @default.
• W4224022173 creator A5052883326 @default.
• W4224022173 creator A5063411806 @default.
• W4224022173 creator A5067439693 @default.
• W4224022173 date "2022-07-01" @default.
• W4224022173 modified "2023-10-05" @default.
• W4224022173 title "Tracing nitrate sources and transformations using △17O, δ15N, and δ18O-NO3− in a coastal plain river network of eastern China" @default.
• W4224022173 cites W1967048892 @default.
• W4224022173 cites W1983340920 @default.
• W4224022173 cites W2005584054 @default.
• W4224022173 cites W2005696788 @default.
• W4224022173 cites W2025254507 @default.
• W4224022173 cites W2032292978 @default.
• W4224022173 cites W2033978351 @default.
• W4224022173 cites W2034828825 @default.
• W4224022173 cites W2041161441 @default.
• W4224022173 cites W2059682795 @default.
• W4224022173 cites W2071754162 @default.
• W4224022173 cites W2075406903 @default.
• W4224022173 cites W2084157484 @default.
• W4224022173 cites W2086297163 @default.
• W4224022173 cites W2113141996 @default.
• W4224022173 cites W2130313781 @default.
• W4224022173 cites W2264261383 @default.
• W4224022173 cites W2279768944 @default.
• W4224022173 cites W2314103220 @default.
• W4224022173 cites W2346357836 @default.
• W4224022173 cites W2396168338 @default.
• W4224022173 cites W2729727354 @default.
• W4224022173 cites W2884975912 @default.
• W4224022173 cites W2887526226 @default.
• W4224022173 cites W2905184106 @default.
• W4224022173 cites W2905839030 @default.
• W4224022173 cites W2941309518 @default.
• W4224022173 cites W2946691057 @default.
• W4224022173 cites W2953198548 @default.
• W4224022173 cites W2959490046 @default.
• W4224022173 cites W2977600319 @default.
• W4224022173 cites W3008332724 @default.
• W4224022173 cites W3011792952 @default.
• W4224022173 cites W3011861537 @default.
• W4224022173 cites W3021076132 @default.
• W4224022173 cites W3026314563 @default.
• W4224022173 cites W3033519242 @default.
• W4224022173 cites W3046545977 @default.
• W4224022173 cites W3080316213 @default.
• W4224022173 cites W3087649732 @default.
• W4224022173 cites W3096051714 @default.
• W4224022173 cites W3112712415 @default.
• W4224022173 cites W3116359591 @default.
• W4224022173 cites W3138910815 @default.
• W4224022173 cites W3157988795 @default.
• W4224022173 cites W3163270285 @default.
• W4224022173 cites W3200397208 @default.
• W4224022173 cites W3217232516 @default.
• W4224022173 cites W3217683179 @default.
• W4224022173 doi "https://doi.org/10.1016/j.jhydrol.2022.127829" @default.
• W4224022173 hasPublicationYear "2022" @default.
• W4224022173 type Work @default.
• W4224022173 citedByCount "5" @default.
• W4224022173 countsByYear W42240221732022 @default.
• W4224022173 countsByYear W42240221732023 @default.
• W4224022173 crossrefType "journal-article" @default.
• W4224022173 hasAuthorship W4224022173A5011805813 @default.
• W4224022173 hasAuthorship W4224022173A5025704414 @default.
• W4224022173 hasAuthorship W4224022173A5032175744 @default.
• W4224022173 hasAuthorship W4224022173A5032769690 @default.
• W4224022173 hasAuthorship W4224022173A5033673090 @default.
• W4224022173 hasAuthorship W4224022173A5039537374 @default.
• W4224022173 hasAuthorship W4224022173A5048224337 @default.
• W4224022173 hasAuthorship W4224022173A5052883326 @default.
• W4224022173 hasAuthorship W4224022173A5063411806 @default.
• W4224022173 hasAuthorship W4224022173A5067439693 @default.
• W4224022173 hasBestOaLocation W42240221732 @default.
• W4224022173 hasConcept C107054158 @default.
• W4224022173 hasConcept C107872376 @default.
• W4224022173 hasConcept C121332964 @default.
• W4224022173 hasConcept C127313418 @default.
• W4224022173 hasConcept C153294291 @default.
• W4224022173 hasConcept C156398188 @default.
• W4224022173 hasConcept C164752452 @default.
• W4224022173 hasConcept C178790620 @default.
• W4224022173 hasConcept C185544564 @default.
• W4224022173 hasConcept C185592680 @default.
• W4224022173 hasConcept C187320778 @default.
• W4224022173 hasConcept C18903297 @default.
• W4224022173 hasConcept C205649164 @default.
• W4224022173 hasConcept C22117777 @default.
• W4224022173 hasConcept C2776384668 @default.
• W4224022173 hasConcept C2778863792 @default.

• next