Migration mechanism of pollutants in karst groundwater system of tailings impoundment and management control effect analysis: Gold mine tailing impoundment case

Li, Bo; Zhang, Huiling; Long, Jie; Fan, Jun; Wu, Pan; Chen, Mengyu; Liu, Pu; Li, Tao

doi:10.1016/j.jclepro.2022.131434

Cited by 20 publications

(7 citation statements)

References 29 publications

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“…With economic development and population growth around the world, the contradiction of supply and demand for water resources has become aggravated, and under the background of the current COVID-19 pandemic, the pattern of global carbon emissions has changed. The rational use of water resources is an important part of carbon emissions so making water resource planning is increasingly crucial (Wang et al 2018a , b ; Liu et al 2021 ; Wang and Su 2020 ; Li et al 2022 ). Precisely predicting water consumption is the first and foremost task for water resource planning and management and the prediction results directly influence the reliability and practicality of water resource planning and decision-making (Hao et al 2009 ; Sivapalan et al 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province

Yang

Huang

et al. 2022

Environ Sci Pollut Res

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Water consumption prediction is an integral part of water resource planning and management. Constructing a highly precise water consumption prediction model is of great significance for promoting regional water resource planning and high-quality development of the socio-economy. This paper focuses on the case of the typical karst region in Guizhou Province in China. Based on data on water consumption and its influencing factors spanning 2000–2020, the principal component analysis method was applied to reduce the dimensionality of 16 influencing factors of water consumption in Guizhou; the principal components extracted were used as input samples of the BP neural network and a PCA-BP neural network water consumption prediction model was conducted to predict water consumption of Guizhou Province in the next 10 years. The results show that the mean absolute error and mean relative error of prediction based on the constructed PCA-BP neural network were 2.8% and 2.9%, respectively, with superior performance in terms of prediction error and trends compared with other models. This paper discusses the main influencing factors of water consumption and analyzes their influence on the water consumption forecasting model so that the parameters of the water consumption forecasting model can be selected more efficiently and provide a reference for regional water consumption analysis and water resource planning and management. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-24604-2.

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Section: Introductionmentioning

confidence: 99%

Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province

Yang

Huang

et al. 2022

Environ Sci Pollut Res

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“…Pollutant migration pathways mainly include surface runoff, litter and soil leaching and infiltration, and groundwater diffusion migration [44,45]. The pattern of pollutant transport and transformation in the investigation area is closely related to the topography, hydrogeological conditions of the site [46], and the distribution of pollutants and pollution sources [47][48][49].…”

Section: Migration Analysis Of Contaminationmentioning

confidence: 99%

Identification and Assessment of Groundwater and Soil Contamination from an Informal Landfill Site

Liu

Wang

2022

Sustainability

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Landfills are a potential source of local environmental pollution of all kinds, and the gradual destruction of seepage-proof structures in informal landfills will lead to contamination of the surrounding soil and groundwater environment. In this study, an informal landfill site in eastern China is used as the research object. Using technologies such as unmanned vessels and monitoring well imaging to delineate the amount and distribution of polluting media, sampling of the surrounding soil, sediment, groundwater, and surface water for testing, analysis, and evaluation is carried out visually and finely for heavy metals, petroleum hydrocarbons, volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), and other indicators. The test results show that volatile phenols are the main contaminant species in the shallow groundwater, chlorinated hydrocarbons and benzene were prevalent in the deep groundwater, hexachlorobenzene and lead in the surface soil, and di(2-Ethylhexyl) phthalate in the deep soil (5.5 m), with a maximum exceedance of 1.24 times. Nearly 10 years have passed since the waste dumping incident at the landfill, but characteristic contaminants are still detected in the topsoil of the dumping area, which shows the long-term nature of the environmental impact of illegal dumping on the site. The study recommends that when developing a comprehensive remediation plan, the persistence of the environmental impact of the waste should be considered and appropriate remediation measures should be screened.

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“…Recently, excessive activities of industry, farming, and humankind have resulted in the deterioration of karst water quality in the karst area [1]. The karst area has a complicated topography, widespread carbonate rock formation, and rich karst water.…”

Section: Introductionmentioning

confidence: 99%

Effect and Mechanism of Bicarbonate Ion on Lead Absorption in Pontederia crassipes from Karst Water

Zhou,

Jiang,

Qin

et al. 2024

Water

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Bicarbonate ions (HCO3−) are abundant in karst water with poor lead (Pb) utilization and biodegradation. This study aimed to investigate the mechanism of HCO3− on the Pb removal efficiency and uptake ability of Pontederia crassipes (a widespread hydrophyte in the karst area) from karst water. The Pb concentration, Pontederia crassipes morphology, and functional group were detected. As the HCO3− molarity in karst water increased (3, 4, and 5 mmol/L), the removal of Pb increased (85.31%, 93.28%, and 95.16%), whereas the bioconcentration amount of Pb decreased (573, 501, and 301 mg/kg), mainly due to the insoluble PbCO3 and Pb (OH)2. The Pb bioconcentration factor was the highest (15,564) at 4 mmol/L HCO3− due to the maximum strength of cation exchange and cell wall protein C=O. High HCO3− molarities changed the variety of positive ions of cation exchange (HCO3− ≤ 4 mmol/L: Na, K, and Mg; HCO3− > 4 mmol/L: Mg and K), and relieved the breaking of roots, stomatal closure, and vascular system shrinking. Moreover, high HCO3− molarities diminished the C≡C oxidation, enlarged the displacement of SO42− and C-O, and stimulated the methyl transfer reaction and the bonding between -CH3 and Pb.

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Migration mechanism of pollutants in karst groundwater system of tailings impoundment and management control effect analysis: Gold mine tailing impoundment case

Cited by 20 publications

References 29 publications

Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province

Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province

Identification and Assessment of Groundwater and Soil Contamination from an Informal Landfill Site

Effect and Mechanism of Bicarbonate Ion on Lead Absorption in Pontederia crassipes from Karst Water

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