DUGi

Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@2072@@298166

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2–4 h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The ‘traditional’ variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities

The authors acknowledge the People Program (Marie Curie Actions) of the European Union’s Seventh Framework Program FP7/2007-2013 under REA agreement 289193 (SANITAS), REA agreement 329349 (PROTEUS) and the European Union (Marie Curie Career Integration Grant PCIG9-GA-2011-293535). Dr Flores-Alsina gratefully acknowledges the financial support of the collaborative international consortium WATERJPI2015 WATINTECH of the Water Challenges for a Changing World Joint Programming Initiative (Water JPI) 2015 call. Additionally, the Spanish Ministry of Science and Innovation (Ramon y Cajal, RYC-2013-14595), Ministry of Economy and Competitiveness (CTM2012-38314-C02-01 (WATERFATE)) and the Economy and Knowledge Department of the Catalan Government through the Consolidated Research Group (2014 SGR 291) - Catalan Institute for Water Research are acknowledged

Elsevier