On the development of a new methodology for groundwater‐Driven health risk assessment

Abstract

Abstract. A methodology and hypothetical case study are presented for incorporation of uncertainty and variability into calculations of human health risk appropriate for regional, or basin-scale, groundwater management problems. Uncertainty in well water concentration is introduced through complex contaminant migration patterns in the subsurface. Variability is considered in parameters related to individual behavior patterns and biological effects and to groundwater extraction and distribution networks. A joint uncertainty and variability (JUV) analysis is used to generate a two-dimensional distribution or risk surface that spans both transport uncertainty as well as individual variability. Cuts in this distributional surface (fractiles of variability and percentiles of uncertainty) are presented and discussed. Comparisons with alternative approaches based upon deterministic transport models are also made. In addition, important distinctions are made between the case where household water is derived from the nearest well and the case where household water is mixed from many wells in a distribution system. IntroductionGroundwater is an important natural resource in the United States, comprising 40% of all public supply water and over This paper develops a new integrated methodology of linkedgroundwater contaminant transport and human exposure that includes uncertainty and variability. This methodology explicitly addresses the variability in multipathway exposure and treats groundwater transport in regional scale, heterogeneous aquifers. Important physical features (such as wells and resultant well distribution systems) are included, as is the uncertainty in geophysical parameters. The methodology is also used to study parameter effects over a wide range of heterogeneity. An idealized groundwater contamination problem is then analyzed using the methodology, and insights regarding different water management strategies are described. BackgroundUnder the Safe Drinking Water Act (SDWA) of 1974, 1986, and 1995, the U.S. Environmental Protection Agency (USEPA) is required to regulate levels of contaminants in water supplies in order to protect public health. As part of this protection, the USEPA establishes a nonenforceable maximum contaminant goal level (MCGL) and an enforceable maximum contaminant level (MCL) for contaminants considered to have adverse health effects [Pontius, 1990a, b]. In addition, states may have their own standards. In planning the restoration or remedial action for contaminated groundwater, target cleanup levels may be established either in terms of 833

CO2 Leakage-Induced Contamination in Shallow Potable Aquifer and Associated Health Risk Assessment

“…Except for (the concentration of arsenic species obtained from the result of numerical simulation), individual parameters considered in the quantification of total risk were randomly sampled within each intrinsic distribution (Table 5) [31,75]. Finally, the calculated total health risks were plotted as a cumulative density function, which enables the estimation of the probability exceeding the risk level of concern (10 −4 ) [76,77].…”

“…Following (4), oxidative dissolution of arsenopyrite consumed 0. 75 (Figures 8(a) and 8(b)); generally, ∑As concentration within the CO 2 plume was greater than that outside, while the rear margin revealed the highest concentration. However, the other associated species such as Fe 2+ , HS − , and O 2 (aq) revealed relatively uniform distribution ( Figures 8(c)-8(e)).…”

Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water

“…The equation used to predict the human lifetime excess cancer risk for a single-stage carcinogenic effect from exposure to organic contaminants is based on the Poisson model for individual cancer occurrence, with probability of at least one cancer occurrence of interest (Maxwell et al, 1998). The cancer risk, R, is given as…”

Improving the accuracy of risk prediction from particle‐based breakthrough curves reconstructed with kernel density estimators

“…For example, Andričević and Cvetković [1996] showed that geologic heterogeneity and uncertainty in the sorption estimate are important factors in determining risk. Maxwell et al [1998] showed that risk decreased as subsurface heterogeneity increased. Enzenhoefer et al [2012] explained how the time between peak and bulk solute breakthrough, important in risk analysis, increase in the presence of fractured media, or as aquifer heterogeneity increases.…”