An Improved SWAT for predicting manganese pollution load at the soil-water interface in a manganese mine area

Yao Zhang, Bozhi Ren, Andrew Hursthouse, Ren-Jian Deng, Baolin Hou

Research output: Contribution to journalArticle

Abstract

The prediction of heavy metal pollution load at the soil-water interface of a mining area was studied through an improved soil and water assessment tool (SWAT) model. The Red Flag Mining Area of Xiangtan Manganese Mine in Hunan Province, China, was selected as the research district. GPS, ARCGIS, RS technology, and field experiments were employed in this study. A modified one-dimensional migration model was embedded in the sediment migration source module of SWAT in order to establish an Improved SWAT model for the prediction of manganese pollution load at the soil-water interface. The key pollution areas identified by the improved model were consistent with actual mine pollution, with the Nash-Sutcliffe efficiency Ens and regression R2 coefficients of 0.88 and 0.91, respectively. The study would provide the theoretical foundation and scientific basis for management and repair at the site.
LanguageEnglish
Pages2357-2365
Number of pages9
JournalPolish Journal of Environmental Studies
Volume27
Issue number5
Early online date13 Apr 2018
DOIs
StatePublished - 30 May 2018

Fingerprint

Manganese mines
Manganese
manganese
Pollution
soil water
Soils
Water
pollution
prediction
repair
Heavy Metals
GPS
Heavy metals
heavy metal
Global positioning system
Sediments
Repair
soil and water assessment tool
pollution load
sediment

Keywords

  • SWAT model
  • soil
  • soil-water interface
  • manganese
  • pollution load

Cite this

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abstract = "The prediction of heavy metal pollution load at the soil-water interface of a mining area was studied through an improved soil and water assessment tool (SWAT) model. The Red Flag Mining Area of Xiangtan Manganese Mine in Hunan Province, China, was selected as the research district. GPS, ARCGIS, RS technology, and field experiments were employed in this study. A modified one-dimensional migration model was embedded in the sediment migration source module of SWAT in order to establish an Improved SWAT model for the prediction of manganese pollution load at the soil-water interface. The key pollution areas identified by the improved model were consistent with actual mine pollution, with the Nash-Sutcliffe efficiency Ens and regression R2 coefficients of 0.88 and 0.91, respectively. The study would provide the theoretical foundation and scientific basis for management and repair at the site.",
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An Improved SWAT for predicting manganese pollution load at the soil-water interface in a manganese mine area. / Zhang, Yao; Ren, Bozhi; Hursthouse, Andrew; Deng, Ren-Jian; Hou, Baolin.

In: Polish Journal of Environmental Studies, Vol. 27, No. 5, 30.05.2018, p. 2357-2365.

Research output: Contribution to journalArticle

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T1 - An Improved SWAT for predicting manganese pollution load at the soil-water interface in a manganese mine area

AU - Zhang,Yao

AU - Ren,Bozhi

AU - Hursthouse,Andrew

AU - Deng,Ren-Jian

AU - Hou,Baolin

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AB - The prediction of heavy metal pollution load at the soil-water interface of a mining area was studied through an improved soil and water assessment tool (SWAT) model. The Red Flag Mining Area of Xiangtan Manganese Mine in Hunan Province, China, was selected as the research district. GPS, ARCGIS, RS technology, and field experiments were employed in this study. A modified one-dimensional migration model was embedded in the sediment migration source module of SWAT in order to establish an Improved SWAT model for the prediction of manganese pollution load at the soil-water interface. The key pollution areas identified by the improved model were consistent with actual mine pollution, with the Nash-Sutcliffe efficiency Ens and regression R2 coefficients of 0.88 and 0.91, respectively. The study would provide the theoretical foundation and scientific basis for management and repair at the site.

KW - SWAT model

KW - soil

KW - soil-water interface

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KW - pollution load

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