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Shu L, Chen W, Liu Y, Shang X, Yang Y, Dahlgren RA, Chen Z, Zhang M, Ji X. Riverine nitrate source identification combining δ 15N/δ 18O-NO 3- with Δ 17O-NO 3- and a nitrification 15N-enrichment factor in a drinking water source region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 918:170617. [PMID: 38311089 DOI: 10.1016/j.scitotenv.2024.170617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/15/2024] [Accepted: 01/30/2024] [Indexed: 02/06/2024]
Abstract
Dual nitrate isotopes (δ15N/δ18O-NO3-) are an effective tool for tracing nitrate sources in freshwater systems worldwide. However, the initial δ15N/δ18O values of different nitrate sources might be altered by isotopic fractionation during nitrification, thereby limiting the efficiency of source apportionment results. This study integrated hydrochemical parameters, site-specific isotopic compositions of potential nitrate sources, multiple stable isotopes (δD/δ18O-H2O, δ15N/δ18O-NO3- and Δ17O-NO3-), soil incubation experiments assessing the nitrification 15N-enrichment factor (εN), and a Bayesian mixing model (MixSIAR) to reduce/eliminate the influence of 15N/18O-fractionations on nitrate source apportionment. Surface water samples from a typical drinking water source region were collected quarterly (June 2021 to March 2022). Nitrate concentrations ranged from 0.35 to 3.06 mg/L (mean = 0.78 ± 0.46 mg/L), constituting ∼70 % of total nitrogen. A MixSIAR model was developed based on δ15N/δ18O-NO3- values of surface waters and the incorporation of a nitrification εN (-6.9 ± 1.8 ‰). Model source apportionment followed: manure/sewage (46.2 ± 10.7 %) > soil organic nitrogen (32.3 ± 18.5 %) > nitrogen fertilizer (19.7 ± 13.1 %) > atmospheric deposition (1.8 ± 1.6 %). An additional MixSIAR model coupling δ15N/δ18O-NO3- with Δ17O-NO3- and εN was constructed to estimate the potential nitrate source contributions for the June 2021 water samples. Results revealed similar nitrate source contributions (manure/sewage = 43.4 ± 14.1 %, soil organic nitrogen = 29.3 ± 19.4 %, nitrogen fertilizer = 19.8 ± 13.8 %, atmospheric deposition = 7.5 ± 1.6 %) to the original MixSIAR model based on εN and δ15N/δ18O-NO3-. Finally, an uncertainty analysis indicated the MixSIAR model coupling δ15N/δ18O-NO3- with Δ17O-NO3- and εN performed better as it generated lower uncertainties with uncertainty index (UI90) of 0.435 compared with the MixSIAR model based on δ15N/δ18O-NO3- (UI90 = 0.522) and the MixSIAR model based on δ15N/δ18O-NO3- and εN (UI90 = 0.442).
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Affiliation(s)
- Lielin Shu
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Wenli Chen
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Yinli Liu
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Xu Shang
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China; Southern Zhejiang Water Research Institute (iWATER), Wenzhou 325035, China
| | - Yue Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China; Southern Zhejiang Water Research Institute (iWATER), Wenzhou 325035, China
| | - Randy A Dahlgren
- Department of Land, Air and Water Resources, University of California, Davis, California 95616, USA
| | - Zheng Chen
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
| | - Minghua Zhang
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China; Department of Land, Air and Water Resources, University of California, Davis, California 95616, USA
| | - Xiaoliang Ji
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
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Xie R, Qi J, Shi C, Zhang P, Wu R, Li J, Waniek JJ. Changes of dissolved organic matter following salinity invasion in different seasons in a nitrogen rich tidal reach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163251. [PMID: 37023805 DOI: 10.1016/j.scitotenv.2023.163251] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/26/2023] [Accepted: 03/30/2023] [Indexed: 04/15/2023]
Abstract
Dissolved organic matter (DOM) is a heterogeneous mixture of dissolved material found ubiquitously in aquatic systems and dissolved organic nitrogen is one of its most important components. We hypothesised nitrogen species and salinity intrusions affect the DOM changes. Here, using the nitrogen rich Minjiang River as an easily accessible natural laboratory 3 field surveys with 9 sampling sites (S1-S9) were conducted in November 2018, April and August 2019. The excitation emission matrices (EEMs) of DOM were explored with parallel factor (PARAFAC) and cosine-histogram similarity analysis. Four indices including fluorescence index (FI), biological index (BIX), humification index (HIX) and the fluorescent DOM (FDOM) were calculated and the impact of physicochemical properties was assessed. The results suggested that the highest salinities of 6.15, 2.98 and 10.10, during each campaign corresponded to DTN concentrations of 119.29-240.71, 149.12-262.42 and 88.27-155.29 μmol·L-1, respectively. PARAFAC analysis revealed the presence of tyrosine-like proteins (C1), tryptophan-like proteins or a combination of the peak N and tryptophan-like fluorophore (C2) and the humic-like material (C3). The EEMs in the upstream reach (i.e. S1-S3) were complex with larger spectra ranges, higher intensities and similar similarity. Subsequently, the fluorescence intensity of three components decreased significantly with low similarity of EEMs (i.e. S4-S7). At the downstream, the fluorescence levels dispersed significantly and no obvious peaks were seen except in August. In addition, FI and HIX increased, while BIX and FDOM decreased from upstream to downstream. The salinity positively correlated with FI and HIX, and negatively related to BIX and FDOM. Besides, the elevated DTN had a significant effect on the DOM fluorescence indices. Altogether, salinity intrusion and elevated nitrogen are relevant for the distribution of the DOM, which is helpful for the water management tracing the DOM source according to the on-line monitoring of salinity and nitrogen in estuaries.
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Affiliation(s)
- Rongrong Xie
- College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350007, China; Key Laboratory of Pollution Control and Resource Recycling of Fujian Province, Fujian Normal University, Fuzhou 350007, China; Digital Fujian Environmental Monitoring Internet of Things Laboratory, Fujian Normal University, Fuzhou 350007, China.
| | - Jiabin Qi
- College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350007, China
| | - Chengchun Shi
- Fujian Provincial Academy of Environmental Sciences, Fuzhou 350013, China
| | - Peng Zhang
- School of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Rulin Wu
- College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350007, China
| | - Jiabing Li
- College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350007, China; Key Laboratory of Pollution Control and Resource Recycling of Fujian Province, Fujian Normal University, Fuzhou 350007, China; Digital Fujian Environmental Monitoring Internet of Things Laboratory, Fujian Normal University, Fuzhou 350007, China
| | - Joanna J Waniek
- Leibniz Institute for Baltic Sea Research, Warnemünde, Rostock 18119, Germany.
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