稳定同位素技术在水体硝酸盐污染源解析中的研究进展

张列宇; 马阳阳; 李国文; 唐文忠; 杜彩丽

doi:10.12153/j.issn.1674-991X.20221103

稳定同位素技术在水体硝酸盐污染源解析中的研究进展

doi: 10.12153/j.issn.1674-991X.20221103

张列宇^1,,
马阳阳^1, ,,
李国文¹,
唐文忠²,
杜彩丽^{1, 3}

1.
中国环境科学研究院
2.
中国科学院生态环境研究中心
3.
同济大学环境科学与工程学院

基金项目: 国家重点研发计划项目（2020YFD1100101）

详细信息

作者简介:
张列宇(1981—)，男，研究员，博士，主要从事流域水体治理研究，zhangly@craes.org.cn

通讯作者:
马阳阳(1998—)，女，硕士研究生，主要从事流域水体治理研究，mayang_y@163.com

中图分类号: X52
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出版历程
- 收稿日期: 2022-11-04
- 网络出版日期: 2023-07-19

Research progress of stable isotopes in source analysis of nitrate pollution in water

ZHANG Lieyu^1
,,
MA Yangyang^{1
, ,},
LI Guowen¹,
TANG Wenzhong²,
DU Caili^{1, 3}

1.
Chinese Research Academy of Environmental Sciences
2.
Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences
3.
College of Environmental Science and Engineering, Tongji University

摘要

摘要:
准确识别水体中硝酸盐污染来源至关重要，目前稳定同位素已被广泛应用于水体硝酸盐污染源解析研究，但关于同位素分馏影响源解析结果准确性的研究仍不足。介绍了稳定同位素分析技术及其在水体硝酸盐污染源解析中的应用，通过对比氮转化中δ¹⁵N-NO₃ ⁻和δ¹⁸O-NO₃ ⁻的时空差异性，结合其他技术方法在硝酸盐污染源解析研究中的应用，提出目前稳定同位素技术在硝酸盐污染源解析中应用的局限性。结果表明，氮转化中同位素分馏对水体δ¹⁵N-NO₃ ⁻和δ¹⁸O-NO₃ ⁻的影响很大，使用符合环境特征的潜在来源δ¹⁵N-NO₃ ⁻和δ¹⁸O-NO₃ ⁻是保证稳定同位素模型解析结果准确性的关键。因此，深入研究水体中与氮转化相关的微生物信息将有助于进一步了解硝酸盐在迁移转化中的特征；土壤层或者地下水硝酸盐的输入应成为地表水体硝酸盐污染源解析的重点考察端元；结合机器学习发展出适应研究区域地理气候特征的稳定同位素模型是今后实现精准溯源的研究方向。
- 稳定同位素 /
- 硝酸盐污染 /
- 源解析 /
- 同位素分馏
Abstract:
Accurate identification of nitrate pollution sources in water bodies is crucial, and stable isotopes have been widely used in source analysis studies of nitrate pollution in water bodies. Still, there are few studies on the influence of isotope fractionation on the accuracy of source analysis results. The stable isotope analysis technology and its application in the analysis of nitrate pollution sources in water bodies were introduced, and by comparing the spatial and temporal variability of δ¹⁵N-NO₃ ⁻ and δ¹⁸O-NO₃ ⁻ in nitrogen transformations and the application of other technical methods in the source analysis of nitrate pollution, the limitations of the current stable isotope techniques in the source analysis of nitrate pollution were presented. The results showed that isotopic fractionation in nitrogen transformation had a strong influence on δ¹⁵N-NO₃ ⁻ and δ¹⁸O-NO₃ ⁻ in water, and the use of δ¹⁵N-NO₃ ⁻ and δ¹⁸O-NO₃ ⁻ of potential sources that met environmental characteristics was the key to ensure the accuracy of stable isotope model analysis results. Therefore, an in-depth study of microbial information related to nitrogen transformation in water bodies would help to further understand the characteristics of nitrate in migration transformation; the input of nitrate from soil layer or groundwater should be the key investigation end source for nitrate pollution source analysis in surface water bodies; the development of stable isotope models adapted to the geoclimatic characteristics of the study area in combination with machine learning was the future research direction to achieve accurate source tracing.
- stable isotope /
- nitrate pollution /
- source analysis /
- isotope fractionation

HTML全文

图 1 水体中硝酸盐来源及氮转化过程与同位素分馏

Figure 1. Nitrate source and nitrogen transformation mechanism and isotopic fractionation in water environment^[28]

下载: 全尺寸图片幻灯片

图 2 2011—2021年稳定同位素技术识别水体中硝酸盐污染来源相关文献关键词共现聚类分析

Figure 2. Co-occurrence cluster analysis of keywords related to the identification of sources of nitrate pollution in water bodies using stable isotope techniques from 2011 to 2021

下载: 全尺寸图片幻灯片

表 1 定性研究中不同硝酸盐来源δ¹⁵N-NO₃ ⁻和δ¹⁸O-NO₃ ⁻范围

Table 1. Range of δ¹⁵N-NO₃ ⁻ and δ¹⁸O-NO₃ ⁻ values from different nitrate sources in qualitative research

硝酸盐来源	δ¹⁵N-NO₃ ⁻/‰	δ¹⁸O-NO₃ ⁻/‰	文献来源
大气沉降	−13~13	25~75	[31,35,37,58-60]
	−5~5	25~75	[56]
	−15~15	60~98	[57]
土壤氮	0~8	−10~10	[31,35,37,58-60]
	3~8	−5~15	[56]
	0~8	−10~10	[57]
无机肥料	−6~6	17~25	[31, 35, 37, 58-60]
	−4~4	−5~15	[56]
	−4~4	17~25	[57]
粪便和污水	4~25	−5~10	[31, 35, 37, 58-60]
	8~25	−5~15	[56]
	5~20	−5~15	[57]

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表 2 各模型优缺点比较

Table 2. Comparison of advantages and disadvantages of each model

模型名称	优点	缺点
线性混合模型^[75]	可以准确计算各种源的贡献	只适用于n个同位素和 n+1种源
IsoError 模型^[78]	考虑了污染源数据的不确定性	只适用于n个同位素和 n+1种源
IsoSource模型^[79]	适用于n个同位素和>n+1种源，界面易操作，结果易提取分析	只能给出范围，未能考虑不确定性和变异系数
贝叶斯混合模型^[56,77-80]	适用于n个同位素和>n+1种源，整合先验信息降低不确定性来源	同位素端元数据引用缺少有关环境背景特征相似性的解释

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表 3 定量研究中SIAR模型所使用的端元同位素组成

Table 3. Endmember isotope values used by SIAR models in quantitative studies ‰　

研究区域		硝酸盐来源	δ¹⁵N-NO₃ ⁻		δ¹⁸O-NO₃ ⁻
研究区域		硝酸盐来源	均值	标准差	均值	标准差
国内	福建^[86]	大气沉降	0.8	3.94	40.7	13.4
		化学肥料	0.4	0.3	2.9	1.7
		土壤氮	1.79	1.6	1.7	0.5
		粪便和污水	14.3	1.9	6.7	2.5
	河南^[87]	大气沉降	3.2	2.4	44	9.1
		化学肥料	0.9	2	3.02	0.55
		土壤氮	5	1.5	3.02	0.55
		粪便和污水	16.3	5.7	7	2.7
	浙江杭州^[13]	大气沉降	0.6	1.5	57.2	6.9
		化学肥料	−2.1	0.7	−4.1	2.7
		土壤氮	3.8	1.8	−2.7	4.4
		粪便和污水	17.4	3.9	6.1	1.6
	山东胶州^[47]	大气沉降	3.4	2.4	21.3	8.0
		化学肥料	0.2	2.2	1.4	0.3
		土壤氮	6.7	1.3	1.4	0.3
		粪便和污水	12.5	2.5	2.5	2.2
	安徽淮北^[18]	大气沉降	−2.7	2.1	61.7	13.7
		化学肥料	4.2	0.17	−5.7	1.7
		土壤氮	4.4	2.13	3.7	0.29
		粪便和污水	11.5	1.16	5.6	1.4
	浙江^[55]	大气沉降	2.3	1.4	60.8	15.0
		化学肥料	0.04	1.87	4.08	0.33
		土壤氮	4.52	2.67	4.08	0.33
		粪便和污水	12.75	3.40	4.08	0.33
	北京昌平^[60]	大气沉降	4.23	4.34	54	13.2
		化学肥料	0.4	0.3	2.92	1.69
		土壤氮	1.79	1.56	1.7	0.5
		粪便和污水	14.3	1.9	6.7	2.5
	广西桂林^[35]	大气沉降	3.1	1.5	56.7	17.8
		化学肥料	−1.12	1.41	−5.7	1.7
		土壤氮	5.7	2.0	1.24	3.13
		粪便和污水	14.3	1.9	6.7	2.5
国外	北欧波罗的海^[88]	大气沉降	0.3	1.4	76.7	6.8
		氮的固定	−1.0	1.0	−0.7	2.9
		土壤氮	1.3	1.4	1.5	0.9
		农田径流	9.9	1.5	4.6	1.0
	非洲加纳^[89]	大气沉降	0.6	1.5	57.2	6.9
		化学肥料	−2.1	0.7	−4.1	2.7
		土壤氮	3.8	1.8	−2.7	4.4
		粪便和污水	17.4	3.9	6.1	1.6
	韩国^[90]	大气沉降	−7.2	0.8	51.1	3.8
		化学肥料	−2.6	0.7	11.2	6.1
		土壤氮	4.1	0.3	9.6	0.1
		污水	11.2	2.4	5.8	1.6
	墨西哥^[91]	大气沉降	0.89	2.12	57.59	12.47
		土壤氮	3.98	1.95	2.51	1.41
		污水	13.25	3.24	4.87	1.87

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