结合贝叶斯同位素混合模型和氡质量平衡模型揭示湖泊-地下水系统中硫酸盐的来源和去向

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Shen Qu , Yuanzhen Zhao , Muhan Li , Xiaohui Ren , Chenyu Wang , Xu Yang , Yanling Hao , Shaogang Dong , Ruihong Yu
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引用次数: 0

摘要

各种自然和人为因素在控制硫酸盐在湖泊-地下水系统中的行为中的作用尚不完全清楚。本文采用多同位素(D/18OH2O/34S/18OSO4/222Rn)和定量模型相结合的混合同位素方法,揭示了内蒙古代海和乌兰素海两个湖泊-地下水系统中硫酸盐的来源和去向。岱海盆地溶解硫酸盐主要来源于蒸发岩溶解(>;50%)和硫化物氧化(10% ~ 30%;包括矿化和黄铁矿氧化)。乌兰苏海盆地溶解硫酸盐的主要来源为蒸发岩溶解(40% ~ 60%)和污水和粪肥(20% ~ 40%),并伴有细菌硫酸盐还原过程。乌兰苏海流域与湖泊地下水排放相关的硫酸盐通量(2.77 × 108 g/L)显著大于岱海流域(1.63 × 106 g/L)。然而,地下水硫酸盐的贡献却表现出相反的结果。两个流域水文地质条件和人类活动的差异是控制硫酸盐来源和命运变化的主导因素。本研究的方法和发现可以提高在类似地区湖泊-地下水系统中确定硫酸盐来源和命运的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Unveiling sources and fate of sulfate in lake-groundwater system combined Bayesian isotope mixing model with radon mass balance model

Unveiling sources and fate of sulfate in lake-groundwater system combined Bayesian isotope mixing model with radon mass balance model

Unveiling sources and fate of sulfate in lake-groundwater system combined Bayesian isotope mixing model with radon mass balance model
The role of various natural and anthropogenic factors in controlling sulfate behavior in lake-groundwater system is not fully understood. In this study, a hybrid isotopic approach incorporating multi-isotopes (D/18OH2O/34S/18OSO4/222Rn) and quantitative models was used to reveal and quantify the sources and fate of sulfate in two lake-groundwater systems (Daihai and Ulansuhai Lake basins) in Inner Mongolia, China. In the Daihai Lake basin, dissolved sulfate was mainly derived from evaporite dissolution (> 50 %) and sulfide oxidation (10 % ∼ 30 %; including mineralization and pyrite oxidation). In contrast, the main dissolved sulfate sources in the Ulansuhai Lake basin included evaporite dissolution (40 % ∼ 60 %) and sewage and manure (20 % ∼ 40 %) accompanied by the bacterial sulfate reduction process. Notably, the sulfate flux associated with lacustrine groundwater discharge in the Ulansuhai Lake basin (2.77 × 108 g/L) was significantly greater than that in the Daihai Lake basin (1.63 × 106 g/L). However, the contribution of groundwater-derived sulfate showed the opposite result. Differences in hydrogeological conditions and human activities in the two basins were the dominant factors controlling the variability in sulfate sources and fate. The approaches and findings of this study can enhance the ability to identify sulfate sources and fate in lake-groundwater systems in similar regions.
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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