Development of a quantitative chemical risk assessment (QCRA) procedure for contaminants of emerging concern in drinking water supply. (15th April 2021) - Cantoni, Beatrice; Penserini, Luca; Vries, Dirk; Dingemans, Milou M.L.; Bokkers, Bas G.H.; Turolla, Andrea; Smeets, Patrick W.M.H.; Antonelli, Manuela
Highlights: Quantitative chemical risk assessment (QCRA) procedure for drinking water was created GAC removal performance regarding contaminants of emerging concern was modeled Uncertainty in exposure and hazard assessment inputs highly affect risk estimation QCRA was successfully applied to optimize interventions in realistic case studies QCRA provides more insights compared to the deterministic CRA for risk management Abstract: The uncertainties on the occurrence, fate and hazard of Contaminants of Emerging Concern (CECs) increasingly challenge drinking water (DW) utilities whether additional measures should be taken to reduce the health risk. This has led to the development and evaluation of risk-based approaches by the scientific community. DW guideline values are commonly derived based on deterministic chemical risk assessment (CRA). Here, we propose a new probabilistic procedure, that is a quantitative chemical risk assessment (QCRA), to assess potential health risk related to the occurrence of CECs in DW. The QCRA includes uncertainties in risk calculation in both exposure and hazard assessments. To quantify the health risk in terms of the benchmark quotient probabilistic distribution, the QCRA estimates the probabilistic distribution of CECs concentration in DW based on their concentration in source water and simulating the breakthrough curves of a granular activated carbon (GAC) treatment process. The model inputs and output uncertainties were evaluated by sensitivity and uncertainty analyses for each step of the risk assessment to identify the most relevant factors affecting risk estimation. Dominant factors resulted to be the concentration of CECs in water sources, GAC isotherm parameters and toxicological data. To stress the potential of this new QCRA approach, several case studies are considered with focus on bisphenol A as an example CEC and various GAC management options. QCRA quantifies the probabilistic risk, providing more insight compared to CRA. QCRA proved to be more effective in supporting the intervention prioritization for treatment optimization to pursue health risk minimization. Graphical abstract: Image, graphical abstract