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

2 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2357-2365
Number of pages9
JournalPolish Journal of Environmental Studies
Volume27
Issue number5
Early online date13 Apr 2018
DOIs
Publication statusPublished - 30 May 2018

Keywords

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

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  • Prizes

    Liancheng Friendship Award of Xiangtan City, Hunan.

    Hursthouse, Andrew (Recipient), 12 Dec 2017

    Prize: Prize (including medals and awards)

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