Probabilistic risk and uncertainty analysis for bioremediation of four chlorinated ethenes in groundwater

Abstract

Groundwater contamination risk assessment for health-threatening compounds should benefit from a stochastic environmental risk assessment which considers the effects of biological, chemical, human behavioral, and physiological processes that involve elements of biotic and abiotic aquifer uncertainty, and human population variability. This paper couples a complex model of chemical degradation and transformation with movement in an aquifer undergoing bioremediation to generate a health risk analysis for different population cohorts in the community. A twostage Monte Carlo simulation has separate stages for population variability and aquifer uncertainty yielding a computationally efficient and conceptually attractive algorithm. A hypothetical example illustrates how risk variance analysis can be conducted to determine the distribution of risk, and the relative impact of uncertainty and variability in different sets of parameters upon the variation of risk values for adults, adolescents, and children. The groundwater example considers a community water supply contaminated with chlorinated ethenes. Biodegradation pathways are enhanced by addition of butyrate. The results showed that the contribution of uncertainty to the risk variance is comparable to that of variability. Among the uncertain parameters considered, transmissivity accounted for the major part of the output variance. Children were the most susceptible and vulnerable population cohort.

Probabilistic human health risk assessment of degradation‐related chemical mixtures in heterogeneous aquifers: Risk statistics, hot spots, and preferential channels

“…In general, the computational effort associated with stochastic simulations of multicomponents reactive transport in three-dimensional heterogeneous aquifers has often limited risk analysis to simple scenarios. For example, a human health risk model related to multispecies groundwater contamination was reported by Benekos et al [2007], who used a biochemical model to evaluate the human health risk response to a bioremediation scenario in a two-dimensional aquifer contaminated by chlorinated solvents. Siirila et al [2014] investigated the total risk posed by the mobilization of metals from the leakage of CO 2 .…”

Screening level ecological risk assessment for synthetic musks in surface water of Lake Taihu, China

“…eutrophication, declines in aquatic species, and extensive list of exotic species) in aquatic ecosystem (Wu et al 2001;Wang et al 2008;Qin et al 2011;Luo and Opaluch 2011). These changes have been linked to conventional pollutants, such as heavy metals (Schuler et al 2008), ammonia (Carrasco and Chang 2005), salinity (Muschal 2006), chlorinated ethenes (Benekos et al 2007), PAHs (Guo et al 2012a), and OCPs (Guo et al 2012b) in the aquatic ecosystem. However, some emerging persistent organic pollutants increasingly occurred in the environmental compartments and their ecological risks to aquatic organisms deserved most concern.…”

A random walk solution for modeling solute transport with network reactions and multi-rate mass transfer in heterogeneous systems: Impact of biofilms

“…In this context, organic chlorinated solvents are often described by first-order reaction chains schematically described by A → B → C → D, meaning that species A is transformed into species B, B into C and so on. The quantification of the risk posed by these contaminants is not a trivial problem (Benekos et al, 2006;Henri and Fernàndez-Garcia, 2014). The degradation products can constitute new noxious chemical compounds not necessarily less toxic than its parent product.…”

“…Reactive transport codes based on Eulerian methods (finite differences or finite-elements) typically encounter numerical problems and suffer from computational burden when dealing with strong heterogeneities Boso et al, 2013). These limitations have been largely emphasized by the recent need to conduct integrated risk analysis studies with many uncertain parameters at high resolution (Maxwell et al, 2008;De Barros et al, 2009;Benekos et al, 2006). In this context, Particle Tracking Methods (PTMs) constitute an efficient numerical alternative to simulate reactive transport (Kitanidis, 1994;Henri and Fernàndez-Garcia, 2014).…”