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In this work, column experiments were conducted to investigate the transport characteristics of graphene oxide (GO) nanoparticles in limestone media under various electrolytes, solution pH, and humic acid (HA) concentration conditions. In the limestone media, GO exhibited relatively low mobility with the mass recovery rate lower than 65.2%, even when solution ionic strength was low. The presence of HA enhanced its mobility. In addition, the presence of S 2- , a divalent anion, also promoted GO transport in limestone media compared to Cl − under similar ionic strength conditions through neutralizing more positive charge and thus diminishing the cation bridging. Solution pH showed slight effect on the transport of GO in limestone with the mass recovery range from 40.3% to 51.7%. Over all, decreases in solution pH, HA concentration and increases in solution ionic strength reduced the mobility of GO in the limestone media under the tested conditions. These results indicated both environmental conditions and media characteristics played important roles in controlling GO fate and transport in porous media. The one-site kinetic deposition model was applied to describe the interactions between the GO and limestone media and model simulations fitted the observed experimental data very well. As limestone is an important component of aquiferous media in subsurface, findings from this study elucidated the key factors and processes controlling the fate of GO particles in limestone media, which can inform the prediction and assessment of the risks of GO in groundwater environment. [ABSTRACT FROM AUTHOR]

• Surface water recharge enhances bioavailability of DOM in groundwater. • Tyrosine-like peak only occurs in IGW, representing recent microbial activities. • Microbial indicators are more sensitive to surface water recharge. • Strong correlations of DOM with microbials drives organic matter conversion. Dissolved organic matter (DOM) plays a crucial role in driving biogeochemical processes and determining water quality in shallow groundwater systems, where DOM could be susceptible to dynamic influences of surface water influx. This study employed fluorescence excitation-emission matrix (EEM) spectroscopy combined with principal component coefficients, parallel factor analysis (PARAFAC), co-occurrence network analysis and structural equation modeling (SEM) to examine changes of DOM fractions from surface water to shallow groundwater in a mesoscale lowland river basin. Combining stable isotope and hydrochemical parameters, except for surface water (SW), two groups of groundwater samples were defined, namely, deeply influenced by surface water (IGW) and groundwater nearly non-influenced by surface water (UGW), which were 50.34 % and 19.39 % recharged by surface water, respectively. According to principal component coefficients, reassembled EEM data of these categories highlighted variations of the tyrosine-like peak in DOM. EEMs coupled with PARAFAC extracted five components (C1-C5), i.e. C1, protein-like substances, C2 and C4, humic-like substances, and C3 and C5, microbial-related substances. The abundance of the protein-like was SW > IGW > UGW, while the order of the humic-like was opposite. The bacterial communities exhibited an obvious cluster across three regions, which hinted their sensitivity to variations in environmental conditions. Based on co-occurrence, SW represented the highest connectivity between bacterial OTUs and DOM fractions, followed by IGW and UGW. SEM revealed that microbial activities increased bioavailability of the humic-like in the SW and IGW, whereas microbial compositions promoted the evolution of humic-like substances in the UGW. Generally, these results could be conducive to discern dissimilarity in DOM fractions across surface water and shallow groundwater, and further trace their interactions in the river watershed. [Display omitted] [ABSTRACT FROM AUTHOR]

Per‐ and polyfluoroalkyl substances (PFASs), also known as "forever chemicals," are manmade chemicals that have been increasingly detected in various geological settings since the early 2000s. The soil and subsurface environments are the geological media commonly affected by PFAS. We conducted a comprehensive review of peer‐reviewed articles published from 2010 through 2022 concerning the fate and transport of PFAS in subsurface environments. This review is organized into different subsections, covering the basics of PFAS properties and how they affect the occurrence, fate, and transport of PFAS, the fundamental processes affecting subsurface transport and fate of PFAS, and mathematical models for describing and predicting PFAS transport behaviors. Mechanisms governing PFAS transport in the subsurface environment, including the sorption of PFAS at the air‐water interface, solid‐water interface, and nonaqueous phase liquids‐water interface, were explored in detail. Challenges and future research priorities are identified to better mitigate the global challenges of PFAS contamination. Plain Language Summary: Per‐ and polyfluoroalkyl substances (PFASs) are a group of manmade chemicals used in a multitude of applications and commercial products (clothing, cookware, cosmetics, personal care products, fire extinguishers, etc.). Their widespread use has, however, lead to increasing release and distribution in terrestrial and aquatic ecosystems. PFAS, due to their unique properties, are very persistent in the environment as they do not degrade easily. Unfortunately, they are also toxic to organisms, including humans. In this review, we discuss the omnipresence of PFAS in water and soil and explain how PFAS move through subsurface media. There are thousands of different PFAS compounds, and only few of them have been studied in more detail and their transport behavior is known. PFAS tend to accumulate at the air‐water interface in the vadose zone. Generally, PFAS with long carbon chains are retained in soils more strongly than short‐chain compounds. Negatively charged PFAS are more mobile in soils than neutral and positively charged PFAS, and thus can readily pollute groundwater. Nonetheless, due to their persistence and slow desorption, even more strongly sorbed PFAS can be a sustained source for groundwater pollution. We finally identify challenges and future research needs to address the emerging threat of PFAS contamination of the environment. [ABSTRACT FROM AUTHOR]

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Many deterministic and stochastic approaches have been applied for groundwater contaminant source identification (GCSI) in recent decades. Usually, these implementations are based on a single groundwater model or fixed model structure and ignore the uncertainty of model structure. However, model structure uncertainty is inevitable for groundwater modeling, especially for complex geological environments and limited observations. This study evaluated the impact of model structure uncertainty on GCSI, and proposes an approach for GCSI based on Bayesian model selection. In the framework of multiple model analysis, a set of alternative model structures are used to represent the unknown groundwater system. Then, a novel nested sampling algorithm, POLYCHORD, is used for model selection and source identification. This algorithm is capable of estimating the model's marginal likelihood and inferring the posterior distribution of the contaminant source's characteristics simultaneously. Finally, this proposed approach is verified through two GCSI case studies, which include a synthetic groundwater contamination problem and a groundwater transport column experiment. The results demonstrated that GCSI could be inconsistent when using different model structures. Models with higher marginal likelihoods tend to have better performance on the predictions of the contaminant source's characteristics. It was concluded that POLYCHORD is efficient in marginal likelihood estimation and GCSI. [ABSTRACT FROM AUTHOR]

• Development of a novel device and method for studying multiphase migration and transformation of organic pollutants. • Comparative analysis of DNAPL multiphase migration and transformation in unconfined and confined groundwater systems. • Validation of experimental results through numerical simulation of contaminated site. To explore the migration and transformation process of dense non-aqueous liquid (DNAPL) pollutants' multiphase flow, specifically nitrobenzene (NB), in confined groundwater (CG) versus unconfined groundwater (UG), a two-dimensional sandbox experimental device was designed and constructed. This involved constructing a vadose zone-UG- aquitard-CG structure, which was then subjected to different scenarios. Real-time analysis and numerical simulation methods were established and employed, with a particular focus on the detailed investigation results of actual contaminated site. The study found that when the same amount of NB was injected, the special structure of the CG layer resulted in a more pronounced reverse diffusion of NB in both the dissolved and NAPL phases. This was especially true for the dissolved phase, which was more likely to diffuse reversely. Meanwhile, CG did not directly interact with the vadose zone, and there was no loss of gas phase NB after the leakage in CG. As a result, higher concentrations of dissolved phase NB were generated, leading to the emergence of a larger area of NB contaminant plumes with CG flow. Importantly, the simulation study of the actual site and the laboratory experimental results were found to be validated, further validating the conclusion that direct leakage of NB into CG results in a higher concentration and larger area of dissolved phase contaminant plume, causing more serious pollution to the groundwater environment. [Display omitted] [ABSTRACT FROM AUTHOR]

Marginal likelihood (or Bayesian evidence) estimation is the basis for model evaluation and model selection, which is the key research content of the uncertainty quantification of groundwater model structure. Recently, the nested sampling estimator (NSE) has been widely used as an efficient method to estimate the marginal likelihood. The Metropolis-Hasting (M-H) algorithm is usually used to explore the model likelihood space for NSE. However, M-H is inefficient in searching complex likelihood functions, especially for the multimodal distribution functions, because of the sampling structure of M-H algorithm. The multi-try differential evolution adaptive Metropolis (MT-DREAMzs) algorithm is effective and robust in searching complex probability space, and it is incorporated into NSE to improve the performance of marginal likelihood estimation in this study. In addition, the developed MT-DREAMzs-based NSE (NSE_MT) is evaluated by comparing with several other marginal likelihood estimators including DREAMzs-based and M-H-based NSEs (NSE_DR, NSE_MH), Gaussian mixture importance (GAME) sampling and a new nested sampling algorithm (PolyChord). Based on three analytical functions (up to 100 dimensions) and one groundwater solute transport case studies, the results showed that NSE_MT outperforms NSE_MH in the convergence, efficiency and accuracy of marginal estimation, and NSE_MT is slightly more efficient and accurate than NSE_DR. The accuracy of NSE_MH, NSE_DR and NSE_MT deteriorates with increasing dimensionality of target distributions/model parameters. In addition, GAME sampling and PolyChord sampling outperform other three NSEs in the accuracy and stability of estimation for high dimensional (up to 100-variate) distributions, and PolyChord is slightly less accurate and stable than GAME. Compared with GAME and PolyChord, NSE_MT and NSE_DR are competent for less than 10-dimensional distributions with higher computational efficiency. [ABSTRACT FROM AUTHOR]

• Stacked GPR can reduce error propagation and improve model's prediction apparently. • Performing error-corrections for submodels is more feasible than for the coupled model. • Error propagation among submodels can increase the complexity of predictive error. The complex groundwater solute transport model can generally be divided into several coupled submodels, the systematic error is inevitable in each submodel due to the unreasonable simplification or biased representation of the true groundwater environment system. The submodels' systematic errors and their propagation lead to great challenge for reliable model predictions. To improve the predictive performance of groundwater solute transport model, the Stacked Gaussian processes regression (GPR) method was proposed to quantify and reduce the error propagation among the submodels in this study. Stacked GPR corrects the structure errors of groundwater model submodel by submodel gradually. Two case studies are performed to demonstrate the effectiveness of Stacked GPR method, including a synthetical groundwater transport case and a laboratory NaCl solute transport experiment. The results demonstrated that Stacked GPR can reduce the error propagation among submodels and improve model's predictive performance apparently. Moreover, it is more feasible to correct the structure error of each submodel in sequence by Stacked GPR than to correct the lumped structure error by traditional GPR for a component-based groundwater solute transport model. The errors in prior submodels could aggravate the complexity of the error of the posterior submodel though error propagation, which increases the difficulty of constructing an effective error-correcting model. [ABSTRACT FROM AUTHOR]

Dissolved surface active agents such as surfactant and natural organic matter can affect the distribution and fate of dense nonaqueous liquids (DNAPLs) in soil and groundwater systems. This work investigated how two common groundwater surface active agents, humic acid (HA) and Tween 80, affected tetrachloroethylene (PCE) migration and source zone architecture in saturated porous media under environmentally relevant conditions. Batch experiments were first conducted to measure the contact angles and interfacial tensions (IFT) between PCE and quartz surface in water containing different amount of surface active agents. Results showed that the contact angle increased and IFT decreased with concentration of surface active agent increasing, and Tween 80 was much more effective than HA. Five 2-D flow cell experiments were then conducted. Correspondingly, Tween 80 showed strong effects on the migration and distribution of PCE in the porous media due to its ability to change the medium wettability from water-wet into intermediate/NAPL-wet. The downward migration velocities of the PCE in three Tween 80 cells were slower than those in the other two cells. In addition, the final saturation of the PCE in the cells containing surface active agents was higher than that in the water-only cell. Results from this work indicate that the presence of surface active agents in groundwater may strongly affect the fate and distribution of DNAPL through altering porous medium wettability. [ABSTRACT FROM AUTHOR]

• Nine GPR kernels are evaluated in improving groundwater model prediction. • GPR kernel has apparent influence on capturing systematic prediction error. • The single kernel RQ has the best performance in improving groundwater prediction. Systematic model error is caused by the unreasonable simplification of real groundwater system, which damages the reliability of groundwater model prediction. Gaussian process regression (GPR) is a popular data-driven method used to build a statistical complementary model to correct systematic prediction error and improve model prediction. Kernel function is a crucial component of GPR, it represents the assumptions on systematic prediction error that we want to learn. In order to evaluate the influence of GPR kernel on capturing systematic prediction error, we compared the performances of GPR with nine kernels in groundwater predictions. These kernels include three commonly used single kernels, i.e., squared exponential (SE), Matérn class (MT), and rational quadratic (RQ) kernels, and six new kernels formed by combining these single kernels as sum or product of pairs (SE_MT_s, SE_RQ_s, MT_RQ_s, SE_MT_p, SE_RQ_p, and MT_RQ_p). Moreover, three groundwater case studies, including two analytical cases and one real case, are used to test these kernels, and four metrics are applied to measure the performances of these kernels. The evaluation results show that kernel functions have apparent influence on the ability of the GPR-based complementary model to capture the systematic prediction error of groundwater model. The single kernel RQ-based GPR yields the best performance in improving groundwater predictions among the nine kernels for all case studies. The combined kernels MT_RQ_s, SE_RQ_s also perform well in groundwater predictions, and the commonly used SE kernel has mediocre performance in predicting groundwater observations. [ABSTRACT FROM AUTHOR]

Abstract: Suzhou is located at the lower reaches of the Yangtze River in southeastern Jiangsu, China. It is part of the Su-Xi-Chang area including Suzhou, Wuxi and Changzhou. As one of the most developed areas in China, this region has suffered from severe land subsidence caused by extensive groundwater exploitation since 1980s. The land subsidence was controlled by prohibition of groundwater exploration in the past several years. However, the surface water pollution prompted a new task of how to sustainably utilize the groundwater resource, especially to satisfy the emergency demands of water supply. In this paper, we took Suzhou as a representative case to discuss how to develop groundwater resources while controlling the land subsidence. The relationship between the deformation and the groundwater level was analyzed, with focus on the deformation features after the period of groundwater exploitation ban. The results confirmed the conclusion by Shi et al. (2007, 2008a): even in the period of rising groundwater level, same units may manifest different deformation characteristics, such as elasticity, elasto-plasticity, and visco-elasto-plasticity, at different locations of the cone of depression. A land subsidence model that couples a 3-D groundwater model and a 1-D deformation model was developed to simulate the groundwater level and deformation. A high-resolution local grid (child model) for Suzhou was built based on the regional land subsidence model of Su-Xi-Chang area by Wu et al. (2009). The model was used for a number of predictive scenarios up to the year of 2012 to examine how to develop sustainable use of groundwater resources under the conditions of land subsidence control. Our results indicated that about 3.08×107 m3/a groundwater could be provided as emergency and standby water source while meeting the land subsidence control target of 10mm/a. [Copyright &y& Elsevier]

• HHR assessment may be misleading if model structure error is not considered. • Ignoring model structure error can cause bias in parameter calibration and prediction. • GPR technique is adequate to simulate the structure error of DNAPL transport model. The contamination of groundwater with dense non-aqueous phase liquids (DNAPL) can pose long-term risks to human health. Due to the complexity of geological environments and the properties of DNAPL, a key step in human health risk (HHR) assessment is the establishment of a reliable DNAPL transport model. In this study, we evaluate the impact of the model structure uncertainty on DNAPL transport modeling for HHR assessment. First, Gaussian process regression (GPR) was used to learn the structure error of the DNAPL transport model through statistical simulations, and then, Markov chain Monte Carlo (MCMC) simulation was used to calibrate the parameters of the physical model and GPR. On the basis of two case studies, namely, a sandbox experiment and a synthetic DNAPL transport model investigation, the results demonstrated that ignoring the model structure error will lead to deviations during parameter calibrations and model predictions. This is because the model parameters could be overfitted to compensate for the model structure error during the calibration process. Results of variance decomposition verified that the model structure error was the main uncertainty source in DNAPL transport modeling for both of the case studies. The DNAPL transport modeling and HHR assessment conducted by quantifying the uncertainties of the model parameters and structure simultaneously was found to be more reliable than that by calibrating only the model parameters. In addition, the HHR assessment of groundwater DNAPL contamination without considering the model structure error could lead to misleading depictions of the key risk zone. We propose that GPR is an effective technique to quantitatively describe the structure error of DNAPL transport models. [ABSTRACT FROM AUTHOR]

The widespread presence of entrapped dense non-aqueous phase liquid (DNAPL) in the subsurface poses a continuing challenge to groundwater remediation. Cost-effective and high-resolution subsurface characterization is a critical issue for further DNAPL recovery due to the complexity of DNAPL source zone architecture (SZA). Geophysical techniques provide a noninvasive, spatially continuous and cost-effective way for monitoring the DNAPL remediation process. In particular, the spectral induced polarization (SIP) method has shown great potential in environmental problems. In this study, we performed real-time SIP measurements on DNAPL contaminated soil in columns to quantitatively assess the ability of SIP method for monitoring surfactant-enhanced DNAPL remediation process. Chemical data was simultaneously collected during the remediation process to verify the results obtained by SIP method. Taking account into the variations of subsurface environment, we conducted a series of column flushing experiments under different flow rate, surfactant concentrations and fluid salinities. The results highlight that SIP method is able to effectively monitor the DNAPL remediation process, as well as to evaluate the remediation efficiency under different conditions. The variations in the flow rate, the concentration of surfactant and the salinity of pore water not only affect remediation effectiveness, but also have an impact on the SIP signatures. This study shows that SIP performs better for monitoring DNAPL remediation at a relatively low flow rate of ~ 0.4 m/d, low surfactant concentration of 5000 mg/L and high salinity of 1.0 S/m, with an error of saturation estimation (RMSE S) <0.1. • The SIP method was effectively applied to monitor the surfactant-enhanced DNAPL remediation process in column experiments. • The in-phase conductivity increases while the phase decreases during the continuous surfactant flushing. • The DNAPL saturation and remediation efficiency estimated by SIP method was in good accordance with the chemical analysis. • The SIP method performs better under the condition of low flow rate, low surfactant concentration and high fluid salinity. [ABSTRACT FROM AUTHOR]