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利用稳定同位素和贝叶斯同位素混合模型追踪岩溶水库中硫酸盐的来源和行为。

Tracing the source and behaviour of sulphate in karst reservoirs, using stable isotopes and Bayesian isotopic-mixing models.

作者信息

Kong Jie, Zhou Zhongfa, Xie Rukai, Cao Weitang

机构信息

School of Geography & Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550001, China; The State Key Laboratory Incubation Base for Karst Mountain Ecology Environment of Guizhou Province, Guiyang 550001, China.

School of Geography & Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550001, China; The State Key Laboratory Incubation Base for Karst Mountain Ecology Environment of Guizhou Province, Guiyang 550001, China.

出版信息

Sci Total Environ. 2025 Jan 1;958:177994. doi: 10.1016/j.scitotenv.2024.177994. Epub 2024 Dec 14.

DOI:10.1016/j.scitotenv.2024.177994
PMID:39675293
Abstract

Increases in sulphate concentrations in natural water bodies can lead to the deterioration of water quality. Human activities, such as coal mining and agricultural fertilisation, can generate sulphate, which can enter water bodies through surface runoff or underground pipelines. Owing to the widespread distribution of coal-bearing strata and an intensification of industrial and agricultural activities, the Pingzhai Reservoir is increasingly at risk of sulphate pollution. In this study, 42 water samples were collected from the Pingzhai Reservoir in April (normal season), July (wet season), and December (dry season) of 2022. Additionally, two precipitation samples, two sewage samples, and two acidic mine drainage samples were collected. Using hydrochemistry, multiple isotopes (δS, δO, δC, and δO) and Bayesian isotopic-mixing model methods, we qualitatively and quantitatively determined the source, contribution proportion, and behaviour of SO in the Pingzhai Reservoir watershed and evaluated the uncertainty of the estimated results. Isotope analysis and the Bayesian isotope-mixing model results indicated that the sources of SO in the Pingzhai Reservoir were coal sulphides and organic sulphur oxidation (64.2 %), soil organic sulphur (18.7 %), sewage (9.9 %), and agricultural sulphur fertiliser (7.2 %). The characteristics of karst landforms (thin soil that is easily eroded), combined with periodic fluctuations in water level (hydrofluctuation belts) in reservoirs, resulted in the release of organic matter from soil to water. The proportion of SO sources of coal sulphide and organic sulphur oxidation in the river was lower than that in the reservoir area, whereas the proportion of the SO sources of soil organic sulphur, sewage, and agricultural sulphur fertiliser was greater than that in the reservoir area. Isotope evidence and the aerobic conditions in water indicated that bacterial sulphate reduction processes did not play a major role. The uncertainty index (UI90) indicated that the contributions of agricultural sulphur fertiliser and sewage manure to SO were relatively constant. This study provides a reference for the protection of water environments and for the development of water pollution control strategies in the karst areas of southwestern China.

摘要

天然水体中硫酸盐浓度的增加会导致水质恶化。煤炭开采和农业施肥等人类活动会产生硫酸盐,这些硫酸盐可通过地表径流或地下管道进入水体。由于含煤地层分布广泛以及工农业活动加剧,平寨水库面临的硫酸盐污染风险日益增加。在本研究中,于2022年4月(平季)、7月(雨季)和12月(旱季)从平寨水库采集了42份水样。此外,还采集了两份降水样品、两份污水样品和两份酸性矿井排水样品。利用水化学、多种同位素(δS、δO、δC和δO)以及贝叶斯同位素混合模型方法,我们定性和定量地确定了平寨水库流域中硫酸根的来源、贡献比例和行为,并评估了估计结果的不确定性。同位素分析和贝叶斯同位素混合模型结果表明,平寨水库中硫酸根的来源为硫化物和有机硫氧化(64.2%)、土壤有机硫(18.7%)、污水(9.9%)和农用硫肥(7.2%)。喀斯特地貌的特征(土壤浅薄且易侵蚀),再加上水库水位的周期性波动(涨落带),导致土壤中的有机物释放到水中。河流中硫化物和有机硫氧化产生的硫酸根来源比例低于库区,而土壤有机硫、污水和农用硫肥产生的硫酸根来源比例高于库区。同位素证据和水中的好氧条件表明,细菌硫酸盐还原过程不起主要作用。不确定性指数(UI90)表明,农用硫肥和污水对硫酸根的贡献相对恒定。本研究为中国西南喀斯特地区的水环境保护和水污染控制策略制定提供了参考。

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