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Extension of the River Water Quality Model No. 1 with the fate of pesticides
Author(s) -
Veerle C. J. De Schepper,
Katrijn Holvoet,
Lorenzo Benedetti,
Piet Seuntjens,
Peter A. Vanrolleghem
Publication year - 2011
Publication title -
journal of hydroinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 50
eISSN - 1465-1734
pISSN - 1464-7141
DOI - 10.2166/hydro.2011.028
Subject(s) - pesticide , sediment , environmental science , sedimentation , water quality , water column , sorption , hydrology (agriculture) , environmental chemistry , diffusion , soil science , environmental engineering , chemistry , geology , geomorphology , geotechnical engineering , ecology , physics , organic chemistry , adsorption , biology , oceanography , thermodynamics
The existing River Water Quality Model No. 1 (RWQM1) was extended with processes determining the fate of non-volatile pesticides in the water phase and sediments. The exchange of pesticides between the water column and the sediment is described by three transport processes: diffusion, sedimentation and resuspension. Burial of sediments is also included. The modified model was used to simulate the concentrations of diuron and chloridazon in the river Nil. A good agreement was found between the simulated pesticide concentrations and measured values resulting from a four-month intensive monitoring campaign. The simulation results indicate that pesticide concentrations in the bulk water are not sensitive to the selected biochemical model parameters. it seems that these concentrations are mainly determined by the imposed upstream concentrations, run-off and direct losses. The high concentrations in the bulk water were not observed in the sediment pore water due to a limited exchange between the water column and the sediment. According to a sensitivity analysis, the observed pesticide concentrations are highly sensitive to the diffusion and sorption coefficients. Therefore, model users should determine these parameters with accuracy in order to reduce the degree of uncertainty in their results

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