Application of a spatially resolved model to contextualise monitoring data for risk assessment of down-the-drain chemicals over large scales

Many regulatory screening level exposure assessments are based on simple large scale conceptual scenarios. However, exposure, and therefore risks associated with chemicals, are characterised by high spatial variability. The Scenario assembly tool (ScenAT) is a global screening level model to enable spatially resolved local predictions of environmental concentrations of home and personal care chemicals. It uses the European Union Technical Guidance Document (TGD) equation to predict local scale freshwater concentrations (predicted environmental concentrations - PECs) of chemicals discharged via wastewater. ScenAT uses Geographic Information System (GIS) layers for the underlying socio-economic (population) and environmental parameters (per capita water use, sewage treatment plant connectivity, dilution factor). Using a probabilistic approach, we incorporate sources of uncertainty in the input data (tonnage estimation, removal in sewage treatment plants and seasonal variability in dilution factors) for two case-study chemicals: the antimicrobial triclosan (TCS) and the anionic surfactant linear alkylbenzene sulphonate (LAS). We then compare model estimates of wastewater and freshwater concentrations of TCS and LAS to UK monitoring data. Comparison showed that modeled PECs were on average higher than mean measured data for TCS and LAS by a factor 1.8 and 1.4, respectively. Considering the uncertainty associated with both model and monitoring data, the use of a probabilistic approach using the ScenAT model for screening assessment is reasonable. The combination of modelled and monitoring data enables the contextualisation of monitoring data. Spatial PECs can be used to identify areas of elevated concentration for further refined assessment. - Highlights: • A global screening level exposure model to predict surface water concentrations. • A probabilistic method to account for non-spatial sources of uncertainty proposed. • An overestimation by ScenAT PECs to monitoring data (factor of 1.8 (TCS) and 1.4 (LAS)). • Monitoring sites are representative of an 85th percentile UK worst-case situation. • ScenAT can be used to generate emission inventories for higher resolution modelling. - A probabilistic approach for large scale screening assessments to contextualise monitoring data for risk assessment.