Resilience of groundwater systems in the presence of Bisphenol A under uncertainty

Assessing the health risks associated with emerging contaminants in groundwater systems is a complex issue that has been receiving increased attention in indirect potable reuse applications. Among several emerging contaminants, our study focuses on developing a numerical model that aims to compute the transport characteristics of Bisphenol A (BPA) in a 3D spatially heterogeneous aquifer under uncertainty. Traditional approaches that characterize the health risk of BPA to humans rely on the monotonic dose-response (MDR) relationship with a regulatory dose limit. Recent public health studies indicate that BPA can cause endocrine-related health effects in specific low dose ranges, which requires the consideration of the non-monotonic dose-response (NMDR) model. This work investigates the impact of different BPA DR models (i.e., monotonic vs. non-monotonic) on the resilience of the aquifer against BPA contamination in the presence of hydrogeological heterogeneity. For the resilience estimation, a systematic stochastic methodology linking risk characterization to aquifer resilience is established. Our results show the importance of the interplay between the DR models and aquifer heterogeneity on controlling the uncertainty of the resilience loss R_L (d) at a specified environmentally sensitive target. In the increased level of aquifer heterogeneity, the uncertainty bounds are higher for R_L estimated through the NMDR model as opposed to the MDR model. Moreover, R_L is controlled by η (-), the ratio of the volumetric flow rate at the source zone to the average flow rate at the background aquifer. In a risk management perspective, the consideration of the NMDR model needs to be emphasized due to its impact on the uncertainty of R_L. A critical case is when the land use of a contamination site indicates a large number of the vulnerable population to endocrine-related health effects. In this case, η as an indicator of aquifer resilience can reduce the uncertainty of R_L.