稳定同位素技术在水体硝酸盐污染源解析中的研究进展

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

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δ^(15)N-NO_(3)^(−)和δ^(18)O-NO_(3)^(−)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δ^(15)N-NO_(3)^(−)和δ^(18)O-NO_(3)^(−)的影响很大,使用符合环境特征的潜在来源δ^(15)N-NO_(3)^(−)和δ^(18)O-NO_(3)^(−)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。

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 ofδ^(15)N-NO_(3)^(−)andδ^(18)O-NO_(3)^(−)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 onδ^(15)N-NO_(3)^(−)andδ^(18)O-NO_(3)^(−)in water,and the use ofδ^(15)N-NO_(3)^(−)andδ^(18)O-NO_(3)^(−)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.