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.

Linear alkylbenzene sulfonate and boron monitoring in four catchments in the UK contribution to GREAT-ER #11.

A comprehensive monitoring programme was carried out in the Aire, Calder, Went and Rother catchments in the UK. A total of 804 effluent samples from 36 sewage treatment works (STWs) and 1100 water samples from 54 river sampling sites were analysed. Concentrations of linear alkylbenzene sulfonate (LAS), boron and other water quality determinands in STW effluent and river waters over a 2-year period (August 1996-August 1998) are reported. The data illustrate the temporal and spatial variations in concentrations of LAS and boron in river waters and effluents. Concentrations of LAS in effluents reflect the biological treatment employed and the influence of tertiary treatment is clearly demonstrated.