Incorporating model uncertainty into the evaluation of interventions to reduce microcontaminant loads in rivers. (1st November 2017)

• Record Type: Journal Article
• Authors: Gimeno, P.; Marcé, R.; Bosch, Ll.; Comas, J.; Corominas, Ll.
• Is Part Of: Water research. Volume 124(2017)
• Page Start: 415
• Page End: 424
• Publication Date: 2017-11-01
• Subjects: Microcontaminant modelling -- Catchment modelling -- Uncertainty -- Decision-making -- Pharmaceuticals -- WWTP upgrades; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2017.07.036
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: Models of microcontaminant fate and transport in wastewater treatment plants (WWTPs) and rivers have been developed and used to assist decision-making in the field of water management. These models come with parameter uncertainties that must be properly incorporated in the decision-making process. The main goal of this study is to evaluate how the magnitudes of key model parameter uncertainties influence the selection of end-of-pipe interventions (at WWTPs) designed to reduce the microcontaminant loads in rivers. We developed a model that describes the fate and removal of pharmaceuticals in WWTPs and the river network based on 3 key parameters: human pharmaceutical consumption and excretion (F) and the pharmaceutical degradation constants in WWTPs ( k WWTP ) and rivers ( k river ). We modelled the fate and transport of diclofenac in the Llobregat River basin (NE Spain). We calibrated the model using a Bayesian approach, which resulted in an accurate prediction of measured diclofenac loads at 9 locations along the Llobregat River and at the influents and effluents of 2 WWTPs (R 2 = 0.95). Using different scenarios, we evaluated three levels of uncertainty in the key model parameters. The first level of uncertainty corresponded to the reference distributions obtained from the Bayesian calibration. Then, for each parameter, we generated a narrower PDF (decreased uncertainty with respect to the reference) and a wider PDF (increased uncertainty). For each level of uncertainty, we evaluated increasing removal efficiencies of diclofenac at the WWTPs, from 38% to 98%. We assumed that removal efficiencies of up to 75% can be achieved by upgrading secondary treatment; beyond 75%, tertiary treatment is needed. The scenario analysis showed that achieving diclofenac removal efficiencies corresponding to tertiary treatment results in apparent concentration reductions (statistically significant differences relative to the reference situation), regardless of the level of uncertainty applied to the model parameters. However, upgrades in the secondary treatment resulted in apparent reductions only in the case of reduced uncertainty. We concluded that model uncertainty greatly influences the decisions that river basin authorities must make to reduce the microcontaminant loads released by WWTPs into rivers. In addition, we discussed research priorities to help reduce model uncertainty and thereby make more appropriate decisions. Graphical abstract: Highlights: A model describes the diclofenac consumption, fate and removal in WWTPs and river. Uncertainty influences the selection of measures to reduce diclofenac in rivers. Installation of tertiary treatments leads to apparent reductions of diclofenac. Secondary treatment upgrades lead to apparent reductions when uncertainty is reduced.