结合δN/δO-NO、ΔO-NO和硝化作用氮富集因子对饮用水源地河流硝酸盐来源进行识别
Riverine nitrate source identification combining δN/δO-NO with ΔO-NO and a nitrification N-enrichment factor in a drinking water source region.
作者信息
Shu Lielin, Chen Wenli, Liu Yinli, Shang Xu, Yang Yue, Dahlgren Randy A, Chen Zheng, Zhang Minghua, Ji Xiaoliang
机构信息
Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
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.
出版信息
Sci Total Environ. 2024 Mar 25;918:170617. doi: 10.1016/j.scitotenv.2024.170617. Epub 2024 Feb 2.
Dual nitrate isotopes (δN/δO-NO) are an effective tool for tracing nitrate sources in freshwater systems worldwide. However, the initial δN/δO 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/δO-HO, δN/δO-NO and ΔO-NO), soil incubation experiments assessing the nitrification N-enrichment factor (ε), and a Bayesian mixing model (MixSIAR) to reduce/eliminate the influence of N/O-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 δN/δO-NO values of surface waters and the incorporation of a nitrification ε (-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 δN/δO-NO with ΔO-NO and ε 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 ε and δN/δO-NO. Finally, an uncertainty analysis indicated the MixSIAR model coupling δN/δO-NO with ΔO-NO and ε performed better as it generated lower uncertainties with uncertainty index (UI) of 0.435 compared with the MixSIAR model based on δN/δO-NO (UI = 0.522) and the MixSIAR model based on δN/δO-NO and ε (UI = 0.442).
双硝酸盐同位素（δN/δO-NO）是追踪全球淡水系统中硝酸盐来源的有效工具。然而，不同硝酸盐来源的初始δN/δO值可能会在硝化过程中因同位素分馏而改变，从而限制了源分配结果的效率。本研究整合了水化学参数、潜在硝酸盐来源的特定地点同位素组成、多种稳定同位素（δD/δO-H₂O、δN/δO-NO和Δ¹⁸O-NO）、评估硝化氮富集因子（ε）的土壤培养实验以及贝叶斯混合模型（MixSIAR），以减少/消除N/O分馏对硝酸盐源分配的影响。从一个典型饮用水源区按季度（2021年6月至2022年3月）采集地表水样本。硝酸盐浓度范围为0.35至3.06毫克/升（平均值 = 0.78 ± 0.46毫克/升），占总氮的约70%。基于地表水的δN/δO-NO值并纳入硝化ε（-6.9 ± 1.8‰）建立了MixSIAR模型。模型源分配结果如下：粪便/污水（46.2 ± 10.7%）>土壤有机氮（32.3 ± 18.5%）>氮肥（19.7 ± 13.1%）>大气沉降（1.8 ± 1.6%）。构建了一个将δN/δO-NO与Δ¹⁸O-NO和ε耦合的额外MixSIAR模型，以估算2021年6月水样中潜在硝酸盐源的贡献。结果显示，与基于ε和δN/δO-NO的原始MixSIAR模型相比，硝酸盐源贡献相似（粪便/污水 = 43.4 ± 14.1%，土壤有机氮 = 29.3 ± 19.4%，氮肥 = 19.8 ± 13.8%，大气沉降 = 7.5 ± 1.6%）。最后，不确定性分析表明，将δN/δO-NO与Δ¹⁸O-NO和ε耦合的MixSIAR模型表现更好，因为与基于δN/δO-NO的MixSIAR模型（不确定性指数（UI）= 0.522）和基于δN/δO-NO和ε的MixSIAR模型（UI = 0.442）相比，它产生的不确定性更低，UI为0.435。
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