Groundwater最新文献

Physics-based groundwater flow modeling is a useful tool for the design and optimization of pump-and-treat systems for groundwater site cleanup. Numerical methods like finite differences and finite elements, and hybrid analytic elements, require boundary conditions (BC) to be assigned to the outer domain of the grid, mesh, or line elements. These outer BC do not always correspond with hydrogeologic features. Common practice in model setup is to either: (1) extend the model domain boundary outward such that introduced artificial outer BCs (e.g., first type head specified, second type flux specified) do not have undue influence on near-field scale simulations; or (2) assign outer BCs to capture the effective far-field influence (e.g., third type head-dependent flux). Groundwater flow modeling options for assigning BCs were demonstrated for the extensively documented Dual Site Superfund cleanup in Torrance, California. The existing MODFLOW models for the Dual Site scale and the Los Angeles basin scale document the current hydrogeologic conceptual site model. Simplified analytic element AnAqSim models at the LA Basin scale, West Coast Subbasin scale, and Dual Site scale, were used for mapping near-field domain velocity vector fields and pathline envelopes. The pump-treat-inject system demonstrated hydraulic containment and showed pathline envelopes relatively insensitive to BC choices. However, the near-field domain boundary groundwater flow fields were sensitive to BC choices. The Los Angeles basin case study demonstrated the use of analytic element groundwater modeling for testing stress dependent boundaries during site pump-treat-inject design.

Monitoring of a seasonal-use, on-site wastewater disposal system (septic system) in Canada, over a 33-year period from 1988 to 2021, showed that during recent sampling the groundwater plume had TIN (total inorganic nitrogen) averaging 12.2 mg/L that was not significantly different than early values, representing 80% removal, whereas SRP (soluble reactive phosphate), although higher than early values averaging 0.08 mg/L, was still 99% lower than the effluent concentration. Evidence suggests that the anammox reaction and possibly also denitrification contribute to TIN removal, whereas SRP removal is primarily the result of mineral precipitation. Most of the removal occurs in close proximity to the drainfield infiltration pipes (within about 1 m) demonstrating that reaction rates are relatively fast in the context of typical groundwater plume residence times. This long-term consistency demonstrates that sustainable nutrient treatment can be achieved with conventional on-site wastewater disposal systems that have low capital costs and require minimal energy input and maintenance.

Globally, it has been reported that groundwater contains elevated levels of Fe and Mn. However, the risk of prolonged exposure to groundwater with elevated Fe and Mn was often ignored due to their much lower carcinogenic risk. To assess the human health risk of elevated Fe and Mn intake in groundwater, 1863 groundwater samples from the Yangtze catchment, a densely populated and economically prosperous area of China, were collected in this study. The spatial distributions of Fe and Mn in groundwater were investigated by the geographic information system (GIS) and their health risk assessment was done. The results indicated that 38.6% and 50.3% of the groundwater samples were defined as “elevated/high” levels for Fe and Mn, respectively, exceeding 0.3 and 0.1 mg/L (World Health Organization guidelines). Moreover, in the groundwater of Yangtze Catchment, the order of Fe and Mn contents is followed by upper< middle< lower. Based on the calculated hazard index (HI), HI_adult and HI_child were in a range of 0-4.91 and 0-11.07, respectively. There was an area of 3,483 and 35,523 km² with a non-carcinogenic risk from Fe and Mn, correspondingly. The numbers of affected adults and children were about 3,018,066 and 2,775,007, respectively. It means that 0.20% and 2.00% of the study area or 0.64% and 0.59% of the total population will suffer health risks from Fe and Mn intake in groundwater, respectively. Therefore, a significant basis for groundwater safety in the Yangtze catchment and similar areas was provided in this study.

Nuclear magnetic resonance (NMR) logging is a promising method for estimating hydraulic conductivity (K). During the past ∼60 years, NMR logging has been used for petroleum applications, and different models have been developed for deriving estimates of permeability. These models involve calibration parameters whose values were determined through decades of research on sandstones and carbonates. We assessed the use of five models to derive estimates of K in glacial aquifers from NMR logging data acquired in two wells at each of two field sites in central Wisconsin, USA. Measurements of K, obtained with a direct push permeameter (DPP), K_DPP, were used to obtain the calibration parameters in the Schlumberger-Doll Research, Seevers, Timur-Coates, Kozeny-Godefroy, and sum-of-echoes (SOE) models so as to predict K from the NMR data; and were also used to assess the ability of the models to predict K_DPP. We obtained four well-scale calibration parameter values for each model using the NMR and DPP measurements in each well; and one study-scale parameter value for each model by using all data. The SOE model achieved an agreement with K_DPP that matched or exceeded that of the other models. The Timur-Coates estimates of K were found to be substantially different from K_DPP. Although the well-scale parameter values for the Schlumberger-Doll, Seevers, and SOE models were found to vary by less than a factor of 2, more research is needed to confirm their general applicability so that site-specific calibration is not required to obtain accurate estimates of K from NMR logging data.

Managed aquifer recharge has become a standard water resources management practice to promote the development of locally sustainable water supplies and combat water scarcity. However, installation of injection wells for replenishment purposes in urban areas with complex hydrogeology faces many challenges, such as limited land availability, potential impacts on municipal production wells and known subsurface contamination plumes, and complex spatially variable hydraulic connections between aquifer units. To assess the feasibility and cost-effectiveness of injecting advanced treated water (ATW) into a complex urban aquifer system, a Simulation-Optimization (SO) model was developed to automate a systematic search for the most cost-effective locations to install new wells for injecting various quantities of ATW, if feasible. The generalized workflow presented here uses an existing MODFLOW groundwater model—along with advanced optimization routines that are publicly available—to flexibly accommodate a multiobjective function, complex constraints, and specific project requirements. The model successfully placed wells for injection of 1 to 4 MGD of ATW in aquifers underlying the study area. The injection well placement was primarily constrained by avoiding excessive impact on environmental sites with underlying groundwater plumes. The largest costs were for well installation and piping to the wells from the existing ATW pipes. This workflow is readily adaptable to other sites with different complexities, decision variables, or constraints.

In Spinti and Panthi (2022; 2023a; 2023b), the affiliation for Rachel A. Spinti is corrected as follows:

Montgomery & Associates, 1550 E. Prince Rd., Tucson, AZ 85719

Many methods to evaluate temporal trends in monitoring data focus on univariate techniques that account for changes in the response variable (e.g., concentration) by means of a single variable, namely time. When predictable site-specific factors, such as groundwater-surface water interactions, are associated with or may cause concentration changes, univariate methods may be insufficient for characterizing, estimating, and forecasting temporal trends. Multiple regression methods can incorporate additional explanatory variables, thereby minimizing the amount of unexplained variability that is relegated to the “error” term. However, the presence of sample results that are below laboratory reporting limits (i.e., censored) prohibits the direct application of the standard least-squares method for multiple regression. Maximum likelihood estimation (MLE) for multiple regression analysis can enhance temporal trend analysis in the presence of censored response data and improve characterizing, estimating, and forecasting of temporal trends. Multiple regression using MLE (or censored multiple regression) was demonstrated at the U.S. Department of Energy Hanford Site where analyte concentrations in groundwater samples are negatively correlated with the stage of the nearby Columbia River. Incorporating a time-lagged stage variable in the regression analysis of these data provides more reliable estimates of future concentrations, reducing the uncertainty in evaluating the progress of remediation toward remedial action objectives. Censored multiple regression can identify significant changes over time; project when maxima and minima of interest are likely to occur; estimate average values and their confidence limits over time periods relevant to regulatory compliance; and thereby improve the management of remedial action monitoring programs.

In this issue paper, the authors refine the definition of water sustainability to account for temporal dynamics and spatial variability, identify specific challenges that must be resolved in the very near future to avoid catastrophic outcomes on levels ranging from economic disruption to survival of mankind, discuss related policy changes and potential effectiveness, and describe several technologies available to achieve water security and sustainability. While water quality certainly poses formidable challenges, in this piece we emphasize and address challenges associated with dynamic water supply availability. Our future as a society will depend upon how well and how rapidly we navigate these challenges in the coming years. As such, the main objective is to encourage private and public sector practitioners to consider revising existing programs, and to update current industry business models in a manner that promotes expedited solutions, alignment of beneficial goals, and motivates the biggest consumers of water to adopt modern data collection and decision support technologies.