Research progress of groundwater pollution source analysis methods
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摘要:
地下水污染具有隐蔽性，污染过程缓慢且难以治理，近年来中国高度重视地下水环境保护工作，作为支撑“以防为主”保护理念的地下水污染源解析技术研究已成为地下水污染防治领域的研究热点。介绍了国内外地下水污染源解析方面的主要技术方法，包括同位素法、荧光光谱法、地质统计学法、主成分分析法、正定矩阵因子分析模型、自组织映射技术6种常用方法的基本原理及在地下水污染识别领域的应用和研究动态，总结了这些方法的优缺点与适用性，并就上述方法在地下水污染研究中的应用前景和发展趋势进行了展望。
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关键词:
- 地下水污染 /
- 源解析 /
- 同位素法 /
- 正定矩阵因子分析模型 /
- 自组织映射技术
Abstract:Groundwater pollution is concealed, and the pollution process is slow and difficult to manage. In recent years, China has attached great importance to the protection of groundwater environment. The research on groundwater pollution source analysis technology, which supports the protection concept of ‘prevention first’, has become a research hotspot in the field of groundwater pollution prevention and control. This paper introduces the
main technical methods of groundwater pollution source analysis at home and abroad, including isotope method, fluorescence spectroscopy, geological statistics, principal component analysis, positive matrix factorization, self-organizing mapping technology, the basic principles of these six common methods and their application and research dynamics in the field of groundwater pollution identification, summarizes the advantages and disadvantages and applicability of the methods, and looks forward to the application prospects and development trends of the above methods in the research of groundwater pollution.
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图 1 不同硝酸盐和硫酸盐来源的δ34S-SO4 和δ15N-NO3范围 （据Torres et al., 2020修改）
Figure 1. Range of δ34S-SO4 and δ15N-NO3 from different sources of nitrates and sulfates
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图 3 应用地质统计学的3个主题领域解决涉及单变量和多变量数据集的地下水盐渍化问题（据Constantinos et al.,2022修改）
Figure 3. Addressing groundwater salinization issues involving univariate and multivariate datasets through the application of three thematic areas in geostatistics
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图 4 SOM-K均值聚类结果和空间特征（SOM污染物分为3类，被聚为一类的污染物在空间分布上与实际地形情况相吻合）
Figure 4. SOM-K means clustering results and spatial features
表 1 不同污染源硝酸盐氮氧同位素值域范围（据吴娜娜等，2017）
Table 1 The range of nitrogen and oxygen isotope ratios of nitrate from various pollution sources
污染源 δ15N 典型值域范围/‰ 均值/‰ 大气NO3−沉降 −7.7 ～ +5.8 −0.4 大气NH4+沉降 −11.1 ～ +2.3 −4.3 粪肥 +5.9 ～ +22.0 +12.7 污水 +4.6 ～ +18.4 +11.4 土壤氮 −3.5 ～ +9.0 +2.2 NO3−化肥 −2.7 ～ +2.3 0 NH4+化肥 −2.0 ～ +4.0 +0.3 污染源 δ18O 典型值域范围/‰ 均值/‰ 大气沉降作用 +25.0 ～ +75.0 +54.2 硝态氮肥 +18.0 ～ +25.7 +21.7 土壤微生物硝化作用 +3.5 ～ +16.8 +10.6 
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表 2 不同地下水污染源解析方法对比
Table 2 Comparison of analysis methods for different groundwater pollution sources
技术方法 原理 数据量 定性/定量 优点 缺点 同位素法 同位素质量守恒 少 定量 涵盖元素广、物理意义明确、氮硫氧等同位素应用广泛、结果直观 需事先判断污染物类型、部分金属同位素和单体同位素技术还不够成熟、需要同位素值域范围、耗费成本和时间较高 荧光光谱法 光谱学原理 少 定量 灵敏度高、选择性好、操作简单 局限性较大、对无机离子变化无响应、存在不确定性、定量分析能力相对较弱 地质统计学法 数理方程反演 多 定量 客观性强，解析广度大 需要的受体数据量大，难以识别复杂污染 主成分分析法 多元统计理论 多 定性 方法简单、结果易解释、适用性高、可分析非浓度水质指标 难以处理非线性数据，常与其他方法联用 正定矩阵因子
分析模型多元统计理论 多 定量 可解释不确定性、无需源成分详细信息、数据处理范围广、结果精度高 无法分析非浓度水质指标、处理有协同作用的源时会不够精确 自组织映射技术 机器学习与
多元统计理论多 定性 直观、适合处理大量数据、适用性高 无法定量分析，需要与其他方法联用
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Chen R H, Teng Y G, Chen, H Y, et al. 2019. Groundwater pollution and risk assessment based on source apportionment in a typical cold agricultural region in Northeastern China[J]. Science of the Total Environment, 696: 133972. doi: 10.1016/j.scitotenv.2019.133972
Chen W, Westerhoff P, Jerry A, et al. 2003. Fluorescence Excitation-Emission Matrix Regional Integration to Quantify Spectra for Dissolved Organic Matter[J]. Environ. Sci. Technol., 37: 5701−5710. doi: 10.1021/es034354c
Cheng H F, Hu Y. 2010. Lead (Pb) isotopic fingerprinting and its applications in lead pollution studies in China: a review[J]. Environmental Pollution, 158(5): 1134−1146. doi: 10.1016/j.envpol.2009.12.028
Constantinos F, Phaedon K, Evangelos T. 2022. Application of geostatistical methods to groundwater salinization problems: A review[J]. Journal of Hydrology, 615: 128566. doi: 10.1016/j.jhydrol.2022.128566
Frank T, Marco S, Heinz J. 2012. Artificial sweeteners—a recently recognized class of emerging environmental contaminants: a review[J]. Anal. Bioanal. Chem., 403: 2503−2518. doi: 10.1007/s00216-012-5892-z
Glaser B. 2005. Compound-specific stable-isotope (δ13C) analysis in soil science[J]. Journal of Plant Nutrition and Soil Science, 168(5): 633−648. doi: 10.1002/jpln.200521794
Hamed H, Johannesson K H, Gonzalez-pinzon R, et al. 2022. Groundwater geochemistry, quality, and pollution of the largest lake basin in the Middle East: Comparison of PMF and PCA-MLR receptor models and application of the source-oriented HHRA approach[J]. Chemosphere, 288: 132489. doi: 10.1016/j.chemosphere.2021.132489
Huang X Y, Zhang D, Zhao Z Q, et al. 2021. Determining hydrogeological and anthropogenic controls on N pollution in groundwater beneath piedmont alluvial fans using multi-isotope data[J]. Journal of Geochemical Exploration, 229: 106844. doi: 10.1016/j.gexplo.2021.106844
Kanagaraj G, Elango L. 2018. Chromium and fluoride contamination in groundwater around leather tanning industries in southern India: implications from stable isotopic ratio δ53C-δ52Cr, geochemical and geostatistical modelling[J]. Chemosphere, 220: 943−953.
Kohonen T. 1982. Analysis of a simple self-organizing process[J]. Biological Cybernetics, 44(2): 135−140. doi: 10.1007/BF00317973
Kohonen T. 1997. Exploration of very large databases by self-organizing maps[C]//Proceedings of international conference on neural networks (icnn'97). IEEE, 1: PL1-PL6.
Lee K J. 2019. The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea[J]. Journal of Hydrology, 569: 685−697. doi: 10.1016/j.jhydrol.2018.12.031
Li M , Rui Z, Wang J S, et al. 2018, Apportionment and evolution of pollution sources in a typical riverside groundwater resource area using PCA-APCS-MLR model[J]. Journal of Contaminant Hydrology, 218: 70-83.
Li X D, Masuda H, Kusakabe M, et al. 2006. Degradation of groundwater quality due to anthropogenic sulfur and nitrogen contamination in the Sichuan Basin, China[J]. Geochemical Journal, 40(4): 309−332. doi: 10.2343/geochemj.40.309
Malsburg C. 1973. Self-organization of orientation sensitive cells in the striate cortex[J]. Kybernetik, 14: 85−100. doi: 10.1007/BF00288907
Mao H R, Wang G C, Rao Z, et al. 2021. Deciphering spatial pattern of groundwater chemistry and nitrogen pollution in Poyang Lake Basin (eastern China) using self-organizing map and multivariate statistics[J]. Journal of Cleaner Production, 329: 129697. doi: 10.1016/j.jclepro.2021.129697
Nazzal Y, Yousef Z, Faisal K, et al. 2015. The combination of principal component analysis and geostatistics as a technique in assessment of groundwater hydrochemistry in arid environment[J]. Current Science: A Fortnightly Journal of Research, 108(6): 1138−1145.
Nisi B, Raco B, Dotsika E. 2016. Groundwater contamination studies by environmental isotopes: A review[J]. Threats to the Quality of Groundwater Resources, 40: 115−150.
Paatero P, Tapper U. 1994. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values[J]. Environmetrics, 5(2): 111−126. doi: 10.1002/env.3170050203
Rashid A, Farooqi A, et al. 2020. Geochemical modeling, source apportionment, health risk exposure and control of higher fluoride in groundwater of sub-district Dargai, Pakistan[J]. Chemosphere, 243: 125409. doi: 10.1016/j.chemosphere.2019.125409
Schmidt T, Zwank L, Elsner M, et al. 2004. Compound-specific stable isotope analysis of organic contaminants in natural environments: a critical review of the state of the art, prospects, and future challenges[J]. Analytical & Bioanalytical Chemistry, 378(2): 283.
Taghvaee S, Sowlat M H, Mousavi A. 2018. Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization(PMF)model[J]. Science of the Total Environment, 628/629: 672−686. doi: 10.1016/j.scitotenv.2018.02.096
Torres J A, Mora A, Knappett P S K, et al. 2020. Tracking nitrate and sulfate sources in groundwater of an urbanized valley using a multi-tracer approach combined with a Bayesian isotope mixing model[J]. Water Research, 182: 115962. doi: 10.1016/j.watres.2020.115962
Torres J A, Mora A, Mahlknecht J, et al. 2021. Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion—A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model[J]. Journal of Hazardous Materials, 417: 126103. doi: 10.1016/j.jhazmat.2021.126103
Voltaggio M, Spadoni M, Sacch: E, et al. 2015. Assessment of groundwater pollution from ash ponds using stable and unstable isotopes around the Koradi and Khaperkheda thermal power plants (Maharashtra, India)[J]. Science of the Total Environment, 518/519: 616−625. doi: 10.1016/j.scitotenv.2015.02.083
Zhang Y Z, Liu Y D, Zhou A G, et al. 2021. Identification of groundwater pollution from livestock farming using fluorescence spectroscopy coupled with multivariate statistical methods[J]. Water Research, 206: 117754. doi: 10.1016/j.watres.2021.117754
陈宇男. 2017. 基于三维荧光光谱法的有机农药废水快速检测实验研究[D]. 合肥工业大学硕士学位论文. 郭涛, 陈海洋, 滕彦国, 等. 2017. 东北典型农产区流域地下水水质评价与污染源识别[J]. 北京师范大学学报(自然科学版), 53(3): 316−322. 郝红, 高博, 陆瑾, 等. 2013. 铅同位素示踪技术在水环境中的应用研究综述[C]//中国环境科学学会编. 2013 年水资源生态保护与水污染控制研讨会论文集, 194-200. 何小松, 余红, 席北斗, 等. 2014. 填埋垃圾浸提液与地下水污染物组成差异及成因[J]. 环境科学, 35(4): 1399−1406. 侯景儒. 1997. 中国地质统计学(空间信息统计学)发展的回顾与前景[J]. 地质与勘探, (1): 53−58. 李先国, 彭学伟, 张庆红. 2009. 单体同位素分析在有机污染物研究中的应用进展[J]. 中国海洋大学学报(自然科学版), 39(6): 1251−1256. 林斯杰, 齐永强, 杨梦曦, 等. 2020. 基于PCA-SOM的北京市平谷区地下水污染溯源[J]. 环境科学研究, 33(6): 1337−1344. 刘国卿, 张干, 彭先芝. 2004. 单体同位素技术在有机环境污染中的研究进展[J]. 地球与环境, (1): 23−27. doi: 10.3969/j.issn.1672-9250.2004.01.004 龙玉桥, 崔婷婷, 李伟, 等. 2017. 地质统计学法在地下水污染溯源中的应用及参数敏感性分析[J]. 水利学报, 48(7): 816−824. 马春龙, 施小清, 许伟伟, 等. 2021. 基于自组织神经网络的污染场地多监测指标相关性分析[J]. 水文地质工程地质, 48(3): 191−202. 孟瑞芳, 孟舒然. 2021. 基于正定矩阵因子分析模型的滹沱河冲洪积扇地下水污染源解析[J]. 环境污染与防治, 43(5): 586-591. 庞凤梅, 吴文良, 孟凡乔, 等. 2011. 利用氮、氧稳定同位素识别地下水硝酸盐污染源研究进展[J]. 农业环境与发展, 28(4): 64−69. 彭莉, 虞敏达, 何小松, 等. 2018. 垃圾填埋场地下水溶解性有机物光谱特征[J]. 环境科学, 39(10): 4556−4564. 王会霞, 史浙明, 姜永海, 等. 2021. 地下水污染识别与溯源指示因子研究进展[J]. 环境科学研究, 34(8): 1886−1898. 王晓红, 魏加华, 成志能, 等. 2013. 地下水有机污染源识别技术体系研究与示范[J]. 环境科学, 34(2): 662−667. 吴娜娜, 钱虹, 李亚峰, 等. 2017. 多种同位素追踪水体硝酸盐污染来源[J]. 沈阳大学学报(自然科学版), 29(3): 103−106. 许明明, 余成龙, 姜建芳, 等. 2022. 建筑垃圾填埋场周边地下水化学组分来源解析[J]. 地质通报, 41(12): 2125−2137. doi: 10.12097/j.issn.1671-2552.2022.12.005 闫颖, 张晓文, 郭波莉. 2020. 铅-镉-锌-汞稳定同位素在重金属污染源解析中的研究进展[J]. 环境化学, 39(10): 2712−2721. 杨琰, 蔡鹤生, 刘存富, 等. 2004. NO3-中15N和18O同位素新技术在岩溶地区地下水氮污染研究中的应用-以河南林州食管癌高发区研究为例[J]. 中国岩溶, (3): 40−46. 张凯, 郑新辉, 李晓楠, 等. 2020. 我国西南某区域地下水污染评价及其污染源解析[J]. 河南师范大学学报(自然科学版), 48(5): 64−73. 张琳, 张永涛, 刘君, 等. 2009. 有机单体同位素分析技术在地下水污染中的研究现状[J]. 地质科技情报, 28(5): 125−130. 张应华, 仵彦卿, 温小虎, 等. 2006. 环境同位素在水循环研究中的应用[J]. 水科学进展, 17(5): 732−747. 赵丽, 刘靖宇, 卫杰, 等. 2020. 某城市生活垃圾填埋场地下水“三氮”及溶解性有机质特征研究[J]. 河南理工大学学报(自然科学版), 39(3): 68−74. 赵然, 韩志伟, 申春华, 等. 2020. 典型岩溶地下河流域水体中硝酸盐源解析[J]. 环境科学, (41): 2664−2670. 智国铮. 2021. 三维荧光光谱技术在水环境中的研究与应用进展[J]. 四川环境, 40(5): 257−261. doi: 10.14034/j.cnki.schj.2021.05.040 周广峰, 刘欣. 2011. 主成分分析法在水环境质量评价中的应用进展[J]. 环境科学导刊, 30(1): 75−78. 周建华, 李晓伟, 陈锋. 2020. 污染物源解析技术正定矩阵因子分析法的现状研究[J]. 北华航天工业学院学报, 30(4): 10−12,25. 周圆, 李怀波, 郑凯凯, 等. 2020. 印染工业园区集中废水处理达标难点及DOM 特征解析[J]. 环境工程学报, (8): 1−10. 左海英. 2015. 地下水中典型挥发性有机污染物单体碳氢同位素方法研究及应用[D].中国地质大学(北京)博士学位论文.
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