Data requirements of GREAT-ER: modelling and validation using LAS in four UK catchments.
Higher-tier environmental risk assessments on "down-the-drain" chemicals in river networks can be conducted using models such as GREAT-ER (Geography-referenced Regional Exposure Assessment Tool for European Rivers). It is important these models are evaluated and their sensitivities to input variables understood. This study had two primary objectives: evaluate GREAT-ER model performance, comparing simulated modelled predictions for LAS (linear alkylbenzene sulphonate) with measured concentrations, for four rivers in the UK, and investigate model sensitivity to input variables. We demonstrate that the GREAT-ER model is very sensitive to variability in river discharges. However it is insensitive to the form of distributions used to describe chemical usage and removal rate in sewage treatment plants (STPs). It is concluded that more effort should be directed towards improving empirical estimates of effluent load and reducing uncertainty associated with usage and removal rates in STPs. Simulations could be improved by incorporating the effect of river depth on dissipation rates.
Price
Oliver R
OR
Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK. oliver.price@unilever.com
Munday
Dawn K
DK
Whelan
Mick J
MJ
Holt
Martin S
MS
Fox
Katharine K
KK
Morris
Gerard
G
Young
Andrew R
AR
D017738:Alkanesulfonic Acids / Q000032:analysis*; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D008962:Models, Theoretical; D045483:Rivers / Q000737:chemistry*; D006113:United Kingdom; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014876:Water Pollution
D017738:Alkanesulfonic Acids; D014874:Water Pollutants, Chemical; C030641:alkylbenzyl sulfonic acid