New Poster: Evaluating HPC ingredients in WWTPs & surface water of the Songhua Catchment using monitoring & high tier modelling tools

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Ingredients commonly used in home and personal care (HPC) products can enter the aquatic environment after use if they are not completely removed during wastewater treatment. We investigated the occurrence and fate of a range of widespread used ingredients in HPC products in wastewater treatment plants (WWTPs) and surface waters of the Songhua River catchment (China) using a high tier modelling framework and monitoring. The aim of our study was to advance understanding in the occurrence and fate of ingredients found in HPC products in the Songhua catchment, in particular 1) to assess spatial trends in the catchment, and 2) to evaluate and improve modelling predictions. Methods: A monitoring campaign was carried out by IRJC-PTS, in the Songhua catchment (China) undertaken from June-July 2017, sampling WWTPs and watersheds. Emission estimates were generated for each ingredient based on product sales data for China and were input into the modelling framework. The hydrobasins hydrological dataset has been integrated within the Pangea multiscale multimedia modeling framework, using the hydrological flow between each basin and its downstream basin to parameterize the transfer rates from the corresponding water compartments. Results: Initial monitoring results for the Songhua catchment indicate the concentration of HPCs are dominated by LAS in WWTPs and rivers. Modelled influent concentrations show good agreement with measured concentrations for all materials, demonstrating emission estimates are reasonable. WWTP median measured removal rates range from 90.6% for TCS up to 99.8% for LAS. In the freshwater compartment there is good agreement, with the model overpredicting concentrations for most ingredients. Conclusion: Our combined modelling and monitoring approach is advantageous for assessing exposure, as monitoring data can be used to evaluate model predictions and refine parametrization while modelling provides feedback to improve the representiveness of sampling. This method enables a more detailed analysis of the key sources of uncertainty and variability at each step of the modelling framework (i.e. influent, effluent and river concentrations). Further work to understand the uncertainties in both monitoring and modelling will be carried out.

 

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