Development of a quantitative chemical risk assessment (QCRA) procedure for contaminants of emerging concern in drinking water supply

The uncertainties on the occurrence, fate and hazard of Contaminants of Emerging Concern (CECs) in-creasingly challenge drinking water (DW) utilities whether additional measures should be taken to re-duce 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 in-cludes 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 as-sessment 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, pro-viding more insight compared to CRA. QCRA proved to be more effective in supporting the intervention prioritization for treatment optimization to pursue health risk minimization. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study introduces a new probabilistic quantitative chemical risk assessment (QCRA) procedure for assessing the health risk of emerging contaminants in drinking water, quantifying probabilistic risk and providing more insight compared to traditional chemical risk assessment.