Modelling daily and hourly loads of pharmaceuticals in urban wastewater

Pharmaceuticals are known contaminants of the environment. Assessing and managing the risk associated to this contamination has become an important field of study in environmental sciences. Accurately sampling and measuring pharmaceuticals concentrations in wastewater or in the environment is still costly and difficult. Thus only a few studies have looked at the temporal variability of the concentrations. In parallel, models have been proposed to predict the occurrence of pharmaceuticals. They usually assume that the loads of pharmaceuticals entering a wastewater treatment plant (WWTP) are proportional to the pharmaceuticals sales. However, most of the time, the results are difficult to interpret. The main problem with those models is the lack of data at WWTP. In this context, a stochastic model predicting daily and hourly loads is proposed to better understand the processes influencing the occurrence of pharmaceuticals in wastewater. Using accurate statistical pharmaceutical sales data, the mass consumed daily in the catchment is randomly picked from empirical probability density functions. Then, patients are randomly generated until the picked total mass is reached. For each patient, posology, metabolism and excretion rates of pharmaceuticals to the sewer system are randomly generated according to relevant literature data. In particular, time-use data are used to generate the time-use of patients to pick the times when patients consume pharmaceuticals and also when they excrete them in toilets. Applied to 9 molecules in a French catchment, results show that the model accurately and reliably predicts both the daily and hourly loads of pharmaceuticals at the inlet of the WWTP.