4.7 Article

A dynamic contaminant fate model of organic compound: A case study of Nitrobenzene pollution in Songhua River, China

Journal

CHEMOSPHERE
Volume 88, Issue 1, Pages 69-76

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2012.02.065

Keywords

Contaminant fate model; Nitrobenzene pollution; Organic pollutant; Songhua River

Funding

  1. National High Technology Research and Development Program [2008AA06A411]
  2. National Major Science and Technology Projects for Water Pollution Control and Governance [2009ZX07207-008-5]
  3. National Science Fund for Distinguished Young Scholars [51125033]
  4. State Key Laboratory of Urban Water Resource Environment [2010DX08]
  5. National Creative Research Groups of China [50821002]
  6. China Scholarship Council [2010612244]

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A one-dimensional dynamic contaminant fate model, coupling kinematic wave flow option with advection-dispersion-reaction equation, has been applied to predict Nitrobenzene pollution emergency in Songhua River, China that occurred on November 13, 2005. The model includes kinetic processes including volatilization, photolysis and biodegradation, and diffusive mass exchange between water column and sediment layer as a function of particles settling and resuspension. Four kinds of quantitative statistical tests, namely Nash-Sutcliffe efficiency, percent bias, ratio of root-mean-square to the standard deviation of monitoring data and Theil's inequality coefficient, are adopted to evaluate model performance. The results generally show that the modeled and detected concentrations exhibit good consistency. Flow velocity in the river is most sensitive parameter to Nitrobenzene concentration in water column based on sensitivity analysis of input parameters. It indicates flow velocity has important impact on both distribution and variance of contaminant concentration. The model performs satisfactory for prediction of organic pollutant fate in Songhua River, with the ability to supply necessary information for pollution event control and early warning, which could be applied to similar long natural rivers. (C) 2012 Elsevier Ltd. All rights reserved.

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