Differences and implications of strontium distribution coefficient on various granite compositional materials.

The distribution coefficient (Kd) of radionuclides is a crucial parameter in assessing the safety of high-level radioactive waste (HLW) geological repository. It is determined in the laboratory through batch and column experiments. However, differences in obtained Kd values from distinct experiments have not been thoroughly assessed and compared. This study evaluated strontium (Sr) sorption on different granite materials using static batch and dynamic experiments (column and core-flooding experiments). The results from batch sorption experiments showed higher Sr sorption on granite under acidic and strongly alkaline conditions, low solid-liquid ratios, and low ionic strength. In column experiments, a two-site sorption model was used to simulate Sr transport in crushed granite and mixed pure minerals. The sorption of Sr on crushed granite exhibited a higher affinity than that of mixed pure minerals. The dual-porosity transport model was employed to investigate Sr transport behavior in fractured granite in the core-flooding experiment. Kd obtained from batch sorption experiments are four to twenty times higher than those from column experiments, and two to three orders of magnitude higher than that from a core-flooding experiment. The results of this study provide valuable insights into safety assessment for the HLW geological repository.

Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence.

Groundwater models are essential for understanding aquifer systems behavior and effective water resources spatio-temporal distributions, yet they are often hindered by challenges related to model assumptions, parametrization, uncertainty, and computational efficiency. Machine intelligence, especially deep learning, promises a paradigm shift in overcoming these challenges. A critical examination of existing machine-driven methods reveals the inherent limitations, particularly in terms of the interpretability and the ability to generalize findings. To overcome these challenges, we develop a ternary framework that synergizes the valuable insights from multisource data, human expertise, and machine intelligence. This framework capitalizes on the distinct strengths of each element: the value and relevance of multisource data, the innovative capacity of human expertise, and the analytical efficiency of machine intelligence. Our goal is to conceptualize sustainable water management practices and enhance our understanding and predictive capabilities of groundwater systems. Unlike approaches that rely solely on abundant data, our framework emphasizes the quality and strategic use of available data, combined with human intellect and advanced computing, to overcome current limitations and pave the way for more realistic groundwater simulations.

Quantifying the impact of upscaled parameters on radionuclide transport in three-dimensional fracture-matrix systems.

Assessing the long-term safety of geological repositories for high-level radioactive waste is critically dependent on understanding radionuclide transport in multi-scale fractured rocks. This study explores the influence of upscaled parameters on radionuclide movement within a three-dimensional fracture-matrix system using a discrete fracture-matrix (DFM) model. The developed numerical simulation workflow includes creating a random discrete fracture network, meshing of the fractures and matrix, assigning upscaled parameters, and conducting finite element simulations. We simulated the spatiotemporal evolution of radionuclide concentrations in the fractures and matrix over a century, revealing significant spatial heterogeneity driven by a heterogeneous seepage field. Employing geostatistics-based upscaling methods, we predicted the effective ranges of crucial solute transport parameters at the field scale. The matrix diffusion coefficient, matrix distribution coefficient, and longitudinal dispersivity were upscaled by factors of 2.0-3.0, 2.5-4.0, and 10-104, respectively, based on laboratory-scale measurements. Incorporating these upscaled parameters into the DFM model, we analyzed their impact on radionuclide transport. Our findings demonstrate that an upscaled matrix diffusion coefficient and matrix distribution coefficient result in a delayed transport of radionuclides in fractures by enhancing mass transfer between the fractures and rock matrix, while an upscaled longitudinal dispersivity accelerates transport by advancing the positions of concentration peaks. Sensitivity analysis revealed that the matrix distribution coefficient is the most impactful, followed by dispersivity and matrix diffusion coefficient. These insights are important for minimizing parameter uncertainties and enhancing the accuracy of predictions concerning radionuclide transport in multi-scale fractured rocks.

Skin-Integrated Wireless Odor Message Delivery Electronics for the Deaf-blind.

Deaf-blindness limits daily human activities, especially interactive modes of audio and visual perception. Although the developed standards have been verified as alternative communication methods, they are uncommon to the nondisabled due to the complicated learning process and inefficiency in terms of communicating distance and throughput. Therefore, the development of communication techniques employing innate sensory abilities including olfaction related to the cerebral limbic system processing emotions, memories, and recognition has been suggested for reducing the training level and increasing communication efficiency. Here, a skin-integrated and wireless olfactory interface system exploiting arrays of miniaturized odor generators (OGs) based on melting/solidifying odorous wax to release smell is introduced for establishing an advanced communication system between deaf-blind and non-deaf-blind. By optimizing the structure design of the OGs, each OG device is as small as 0.24 cm3 (length × width × height of 11 mm × 10 mm × 2.2 mm), enabling integration of up to 8 OGs on the epidermis between nose and lip for direct and rapid olfactory drive with a weight of only 24.56 g. By generating single or mixed odors, different linked messages could be delivered to a user within a short period in a wireless and programmable way. By adopting the olfactory interface message delivery system, the recognition rates for the messages have been improved 1.5 times that of the touch-based method, while the response times were immensely decreased 4 times. Thus, the presented wearable olfactory interface system exhibits great potential as an alternative message delivery method for the deaf-blind.

Investigating key drivers of N2O emissions in heterogeneous riparian sediments: Reactive transport modeling and statistical analysis.

Nitrous oxide (N2O) is a potent greenhouse gas that also contributes to ozone depletion. Recent studies have identified river corridors as significant sources of N2O emissions. Surface water-groundwater (hyporheic) interactions along river corridors induce flow and reactive nitrogen transport through riparian sediments, thereby generating N2O. Despite the prevalence of these processes, the controlling influence of physical and geochemical parameters on N2O emissions from coupled aerobic and anaerobic reactive transport processes in heterogeneous riparian sediments is not yet fully understood. This study presents an integrated framework that combines a flow and multi-component reactive transport model (RTM) with an uncertainty quantification and sensitivity analysis tool to determine which physical and geochemical parameters have the greatest impact on N2O emissions from riparian sediments. The framework involves the development of thousands of RTMs, followed by global sensitivity and responsive surface analyses. Results indicate that characterizing the denitrification reaction rate constant and permeability of intermediate-permeability sediments (e.g., sandy gravel) are crucial in describing coupled nitrification-denitrification reactions and the magnitude of N2O emissions. This study provides valuable insights into the factors that influence N2O emissions from riparian sediments and can help in developing strategies to control N2O emissions from river corridors.

Investigating the influence of bentonite colloids on strontium sorption in granite under various hydrogeochemical conditions.

The disposal of high-level radioactive waste in deep geological repositories is a critical environmental issue. The presence of bentonite colloids generated in the engineering barrier can significantly impact the transport of radionuclides, but their effect on radionuclide sorption in granite remains poorly understood. This study aimed to investigate the sorption characteristics of strontium (Sr) on granite as well as on the coexistence system of granite and colloids under various hydrogeochemical conditions, through batch experiments. Fourier transform infrared spectroscopy was employed to analyze the sorption forms of Sr on granite before and after sorption. Several hydrogeochemical factors were examined, including contact time, pH, ionic strength, coexisting ions, and bentonite and humic acid colloid concentration. Among these factors, the concentration of bentonite colloids exhibited a significant effect on Sr sorption. Within a specific range of colloid concentration, the sorption of Sr on the solid system increased linearly with the bentonite colloid concentration. pH and ionic strength were also found to play crucial roles in the sorption process. At low pH, Sr sorption primarily occurred through the outer sphere's surface complexation and Na+/H+ ion exchange. However, at high pH, inner sphere surface complexation dominated the process. As the ionic strength increased, electrostatic repulsion gradually increased, resulting in fewer binding sites for particle aggregation and Sr sorption on bentonite colloids. The results also indicate that with increasing pH, the predominant forms of Sr in the solution transitioned from SrHCO3+ and SrCl+ to SrCO3 and SrCl+. This was mainly due to the ion exchange of Ca2+/Mg2+ in plagioclase and biotite, forming SrCO3 precipitation. These findings provide valuable insights into the transport behavior of radionuclides in the subsurface environment of the repository and highlight the importance of considering bentonite colloids and other hydrogeochemical factors when assessing the environmental impact of high-level radioactive waste disposal.

Upscaling dispersivity for conservative solute transport in naturally fractured media.

Physical heterogeneities are prevalent features of fracture systems and significantly impact transport processes in aquifers across different spatiotemporal scales. Upscaling solute transport parameter is an effective way of quantifying parameter variability in heterogeneous aquifers including fractured media. This paper develops conceptual models for upscaling conservative transport parameters in fracture media. The focus is on upscaling dispersivity. Lagrangian-based transport model (LBTM) for dispersivity upscaling are derived for the solute transport in two-dimensional fractures surrounded by an impermeable matrix. The LBTM is validated against the random walk particle tracking (RWPT) model, which enables highly efficient and accurate predictions of conservative solute transport. The results show that the derived scale-dependent analytical expressions are in excellent agreement with RWPT model results. In addition, LBTM results are also compared to experimental results from the observed breakthrough curve of a conservative solute transport through a single natural fracture within a granite core. Comparing results from the LBTM and transport experiment shows that LBTM based estimated dispersivity is 10.55% higher than the measured value. Errors introduced by the experiments, the conceptual assumptions in deriving models, and the heterogeneities of fracture apertures not fully sampled by measuring instruments are main factor for such discrepancy. The sensitivity analysis indicates that the longitudinal and transverse dispersivities are positively related to the integral scale and the variance of the log-fracture aperture. The longitudinal dispersivity is strongly contolled by the variance of the log-fracture aperture. The LBTM may be useful for directly predicting solute transports, requiring only the acquisition of fractured geostatistical data. This work provides a better understanding of transport processes in fractured media which ultimately control water quality across scales.

Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage.

The utilization of carbon capture utilization and storage (CCUS) in unconventional formations is a promising way for improving hydrocarbon production and combating climate change. Shale wettability plays a crucial factor for successful CCUS projects. In this study, multiple machine learning (ML) techniques, including multilayer perceptron (MLP) and radial basis function neural networks (RBFNN), were used to evaluate shale wettability based on five key features, including formation pressure, temperature, salinity, total organic carbon (TOC), and theta zero. The data were collected from 229 datasets of contact angle in three states of shale/oil/brine, shale/CO2/brine, and shale/CH4/brine systems. Five algorithms were used to tune MLP, while three optimization algorithms were used to optimize the RBFNN computing framework. The results indicate that the RBFNN-MVO model achieved the best predictive accuracy, with a root mean square error (RMSE) value of 0.113 and an R2 of 0.999993. The sensitivity analysis showed that theta zero, TOC, pressure, temperature, and salinity were the most sensitive features. This research demonstrates the effectiveness of RBFNN-MVO model in evaluating shale wettability for CCUS initiatives and cleaner production.

Effect of chemicals on the phase and viscosity behavior of water in oil emulsions.

Due to population growth, the need for energy, especially fossil fuels, is increased every year. Since the costs of exploring new reservoirs and drilling new wells are very high, most reservoirs have passed their first and second periods of life, and it is necessary to use EOR methods. Water-based enhanced oil recovery (EOR) methods are one of the popular methods in this field. In this method, due to the possibility of emulsion formation is high, and by creating a stable emulsion, viscosity and mobility improved. In this study, the parameters affecting the stability and viscosity of the emulsion have been investigated step by step. In the first step, 50% (v/v) of water has been selected as the best water cut. The type of salt and its best concentration was evaluated in the second step by measuring the average droplets size. The third step investigated the effect of SiO2 nanoparticles and surfactant (span80) on emulsion stability and viscosity. According to the results, the best amount of water cut was 50% due to the maximum viscosity. In salts the yield was as follows: MgCl2 > CaCl2 > MgSO4 > Na2SO4 > NaCl. The best yield was related to MgCl2 at a concentration of 10,000 ppm. Finally, it was shown that the synergy of nanoparticles and surfactants resulted in higher stability and viscosity than in the case where each was used alone. It should be noted that the optimal concentration of nanoparticles is equal to 0.1% (w/w), and the optimal concentration of surfactant is equal to 200 ppm. In general, a stable state was obtained in 50% water-cut with MgCl2 salt at a concentration of 10,000 ppm and in the presence of SiO2 nanoparticles at a concentration of 0.1% and span 80 surfactants at a concentration of 200 ppm. The results obtained from this study provide important insights for optimal selection of the water-based EOR operation parameters. Viscosity showed a similar trend with stability and droplet size. As the average particle size decreased (or stability increased), the emulsion viscosity increased.

Application of robust deep learning models to predict mine water inflow: Implication for groundwater environment management.

Traditional mine water inflow prediction is characterized by a high degree of uncertainty in model parameters and complex mechanisms involved in the water inflow process. Data-driven models play a key role in predicting inflow mechanisms without considering physical changes. However, the existing models are limited by nonlinearity and non-stationarity. Thus, the principal objective of this study was to propose two robust models, the DIFF-TCN model and the DIFF-LSTM model, for predicting the average water inflow per day. The models consist of three methods, namely Difference Method (DIFF), Temporal Convolutional Neural Network (TCN), and Long Short-Term Memory Neural Network (LSTM). When applied to the Tingnan Coal Mine, Shanxi Province, China, the DIFF-TCN performs better in predicting the average daily water inflow, the model has a MAE of 5.88 m3/h, RMSE of 6.85 m3/h and R2 of 0.96 in the test stage of the water inflow event. Comparison with the other deep learning models (with similar complex structures) and traditional time series model shows the superiority of our proposed DIFF-TCN model. The SHAP value is used to explain the contribution of each model input to the predicted values, and it indicates that the historical time of water inflow data are the most important input, and the advance distance and the groundwater level data also contribute to the model predictions, but groundwater level data for some periods in the past may have a detrimental effect on the model. The findings of this study can provide better understanding about potential of robust deep learning models for smart hydrological forecasting, and they can also provide technical guidance for mining safety production and protection of water resources and water environment around the mining area.

Uncertainty and sensitivity analysis of radionuclide migration through fractured granite aquifer.

The radionuclide migration in the high-level radioactive waste (HLW) disposal is usually predicted by numerical simulations for risk analysis of radionuclide contamination in a large scale of time and space. However, the uncertainties in radionuclide migration models and their associated parameters significantly affect the simulation results. In the present study, we first selected certain parameters and output data as independent parameters and risk metrics and performed a series of radionuclide transport models at a research site in Northwestern China. The models considered radionuclide migration in the equivalent porous medium with the mechanism of nuclide decay in an arbitrary-length decay chain, adsorption, advection, diffusion, and dispersion. Then 3000 Monte Carlo (MC) simulations were performed to carry out a set of uncertainty and global sensitivity analysis by coupling an uncertainty quantification tool with a radionuclide migration simulator. The results indicated that both hydraulic gradient and hydraulic conductivity significantly influenced the risk metrics. Thus, it is critical to obtain hydraulic gradient and hydraulic conductivity data under the same economic conditions. We applied the multivariate adaptive regression spline (MARS) method to generate response surface models representing the relationships among independent parameters and risk metrics. Calculations of the risk metric distribution ranges revealed that the peak release doses would appear at 0.40 and 0.79 million years, and their values will be in the range of 4.7 × 10-7-1.93 × 10-6 Sv/a. Uncertainty and sensitivity analysis results of radionuclide contamination in the fractured granite upon which HLW is disposed can improve simulation and prediction accuracy for radionuclide migration.

Plutonium reactive transport in fractured granite: Multi-species experiments and simulations.

Plutonium (Pu) in the subsurface environment can transport in different oxidation states as an aqueous solute or as colloidal particles. The transport behavior of Pu is affected by the relative abundances of these species and can be difficult to predict when they simultaneously exist. This study investigates the concurrent transport of Pu intrinsic colloids, Pu(IV)(aq) and Pu(V-VI)(aq) through a combination of controlled experiments and semi-analytical dual-porosity transport modeling. Pu transport experiments were conducted in a fractured granite at high and low flow rates to elucidate sorption processes and their scaling behavior. In the experiments, Pu(IV)(aq) was the least mobile of the Pu species, Pu(V-VI)(aq) had intermediate mobility, and the colloidal Pu, which consisted mainly of precipitated and/or hydrolyzed Pu(IV), was the most mobile. The semi-analytical modeling revealed that the sorption of each Pu species was rate-limited, as the sorption could not be described by assuming local equilibrium in the experiments. The model was able to describe the sorption of the different Pu species that occurring either on fracture surfaces, in the pores of the rock matrix, or simultaneously in both locations. While equally good fits to the data could be achieved using any of these assumptions, a fracture-dominated process was considered to be the most plausible because it provided the most reasonable estimates of sorption rate constants. Importantly, a key result of this work is that the sorption rate constant of all Pu species tends to decrease with increasing time scales, which implies that Pu will tend to be more mobile at longer time scales than observations at shorter time scales suggest. This result has important implications for predicting the environmental impacts of Pu in the safety assessments of geologic repositories for radioactive waste disposal, and we explore potential mechanistic bases for upscaling the sorption rate constants to time and distance scales that cannot be practically evaluated in experiments.

Underground sources of drinking water chemistry changes in response to potential CO2 leakage.

The purpose of this study was to quantify changes to underground sources of drinking water (USDW) quality in response to potential CO2 leakage from geologic CO2 sequestration (GCS) reservoirs. We developed a framework of combined laboratory experiments and reactive transport simulations and used this framework to evaluate the Ogallala aquifer overlying the Farnsworth Unit (FWU), an active GCS site, as a case study. Using chemical reaction parameters obtained from laboratory experiments and numerical simulations, site-specific mechanisms of CO2-water-sediment interactions at the USDW aquifer were interpreted. Long-term risks of potential CO2 leakage were then evaluated with field-scale numerical models using the regional hydrogeological characteristics and reaction parameters obtained from our experiments and simulations. Results suggest that carbonate mineral impurity and cation exchange are key mechanisms for interactions between CO2 and the aquifer sediment. Additionally, for a large leakage rate of 0.1 % injection from one leaky well, the leakage plume might impact an area of 300 m in diameter and significantly affect the local water quality by changing pH and cation concentrations (e.g., Zn, Ba and Sr). After leakage ceases, the zone of impacted fluids would not migrate significantly in subsequent decades due to a low regional groundwater flowrate (for this case study). The relatively small area of impact might not be detected in a monitoring well given the broader spacing in a typical field scenario. Effective early leakage detection may require additional tools, e.g., borehole CO2 movement, four-dimensional seismicity, CO2 soil flux, samples from deeper aquifers, etc., to ensure effective leakage detection and long-term safety of GCS projects.

Stretchable Sweat-Activated Battery in Skin-Integrated Electronics for Continuous Wireless Sweat Monitoring.

Wearable electronics have attracted extensive attentions over the past few years for their potential applications in health monitoring based on continuous data collection and real-time wireless transmission, which highlights the importance of portable powering technologies. Batteries are the most used power source for wearable electronics, but unfortunately, they consist of hazardous materials and are bulky, which limit their incorporation into the state-of-art skin-integrated electronics. Sweat-activated biocompatible batteries offer a new powering strategy for skin-like electronics. However, the capacity of the reported sweat-activated batteries (SABs) cannot support real-time data collection and wireless transmission. Focused on this issue, soft, biocompatible, SABs are developed that can be directly integrated on skin with a record high capacity of 42.5 mAh and power density of 7.46 mW cm-2 among the wearable sweat and body fluids activated batteries. The high performance SABs enable powering electronic devices for a long-term duration, for instance, continuously lighting 120 lighting emitting diodes (LEDs) for over 5 h, and also offers the capability of powering Bluetooth wireless operation for real-time recording of physiological signals for over 6 h. Demonstrations of the SABs for powering microfluidic system based sweat sensors are realized in this work, allowing real-time monitoring of pH, glucose, and Na+ in sweat.

Radionuclide transport in multi-scale fractured rocks: A review.

Significant progress has been achieved on radionuclide transport in fractured rocks due to worldwide urgent needs for geological disposal of high-level radioactive waste (HLW). Transport models designed with accurately constrained parameters are a fundamental prerequisite to assess the long-term safety of repositories constructed in deep formations. Focusing on geological disposal systems of HLW, this study comprehensively reviews the behavoir of radionuclides and transport processes in multi-scale fractured rocks. Three issues in transport modeling are emphasized: 1) determining parameters of radionuclide transport models in various scales from laboratory- to field-scale experiments, 2) upscaling physical and chemical parameters across scales, and 3) characterizing fracture structures for radionuclide transport simulations. A broad spectrum of contents is covered relevant to radionuclide transport, including laboratory and field scale experiments, analytical and numerical solutions, parameter upscaling, and conceptual model developments. This paper also discusses the latest progress of radionuclide migration in multi-scale fractured rocks and the most promising development trends in the future. It provides valuable insights into understanding radionuclide transport and long-term safety assessment for HLW geological repository.

Extracellular Polymeric Substances Facilitate the Adsorption and Migration of Cu2+ and Cd2+ in Saturated Porous Media.

Heavy metal contamination in groundwater is a serious environmental problem. Many microorganisms that survive in subsurface porous media also produce extracellular polymeric substances (EPS), but little is known about the effect of these EPS on the fate and transport of heavy metals in aquifers. In this study, EPS extracted from soil with a steam method were used to study the adsorption behaviors of Cu2+ and Cd2+, employing quartz sand as a subsurface porous medium. The results showed that EPS had a good adsorption capacity for Cu2+ (13.5 mg/g) and Cd2+ (14.1 mg/g) that can be viewed using the Temkin and Freundlich models, respectively. At a pH value of 6.5 ± 0.1 and a temperature of 20 °C, EPS showed a greater affinity for Cu2+ than for Cd2+. The binding force between EPS and quartz sand was weak. The prior saturation of the sand media with EPS solution can significantly promote the migration of the Cu2+ and Cd2+ in sand columns by 8.8% and 32.1%, respectively. When treating both metals simultaneously, the migration of Cd2+ was found to be greater than that of Cu2+. This also demonstrated that EPS can promote the co-migration of Cu2+ and Cd2+ in saturated porous media.

Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR.

The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS.

Spectrophotometric Determination of p-Nitrophenol under ENP Interference.

Engineered nanoparticles (ENPs) have been widely developed in various fields in recent years, resulting in an increasing occurrence of nanoparticles in the natural environment. However, the tiny substances have created unexpected confusion in environmental sample testing due to the negative nanoeffect of ENPs. In this paper, a novel technique of spectrophotometric determination of p-nitrophenol (PNP) was developed under the interfering impact of nano-Fe(OH)3, widely distributed in the natural environment as a typical example of ENPs. Because of the strong absorption at the two characteristic peaks of PNP, namely, 317 nm and 400 nm, nano-Fe(OH)3 interfered with the colorimetric determination of PNP. Thus, the developed testing method, with HCl acidification at 60°C and ascorbic acid (AA) masking FeCl3, was proposed and successfully realized the accurate determination of PNP in water samples by ultraviolet spectrophotometry with 317 nm as the absorption wavelength. The final colorimetric system of 5% HCl, 10% CH3OH, and 1% ascorbic acid was confirmed by optimized batch experiments, and the optimum condition of acidification pretreatment was heating at 60°C for 20 min. Further results demonstrated that the proposed novel method had good accuracy and reproducibility even in high-salinity natural water bodies such as groundwater and surface water. The testing technique presented in this paper provided an interesting and useful tool for problem solving of PNP surveys under ENPs' interference and practically supported water quality assessment for a better environment.

Experimental investigations on scale-dependent dispersivity in three-dimensional heterogeneous porous media.

The relationship between the scale-dependent dispersivity and heterogeneous sedimentary structures is investigated through conducting non-reactive tracer experiments in a three-dimensional heterogeneous sand tank. The heterogeneous porous media consists of three sedimentary facies of silty, fine, and medium sands collected from the west of the Songnen Plain, China. Moreover, several corresponding individual facies soil columns were constructed for comparison. A conservative tracer was continuously injected from an upstream source. The effective parameters were estimated by inverse modeling of a one-dimensional transport model. The results show that the scale dependence of the estimated dispersivities was discovered in the individual facies column (with relatively weaker effect) and the heterogeneous porous media (with more significant effect). With increasing transport distances, the dispersivities of the individual facies tend to reach an asymptotic value, while those of the heterogeneous media increase continuously. Furthermore, the results show that a power function can describe the relationship between effective dispersivities and transport distances. The exponent of the function is greater than one for the heterogeneous media, but less than one for the individual facies. The results also indicate that the dispersion plume is macroscopically dominated by the distribution of facies. The heterogeneity of hydraulic conductivity causes the variations of flow velocity, which further enhances the scale dependence of dispersivities. The tracer experiment in heterogeneous media provides the fundamental insight into the understanding of contaminant transport processes.

Bacterial community variations with salinity in the saltwater-intruded estuarine aquifer.

Bacterial community has been significantly enrolled in the biogeochemical cycling of the coastal subsurface ecosystem. The bacterial community variations with salinity have been extensively investigated in the surface environment, such as lake, soil, and estuary, but not in the subsurface environment. Here we explore the responses of bacterial populations to the salinity and other environmental factors (EFs) by considering both the abundant and rare sub-community in a coastal Holocene groundwater system. Our study results indicate that the bacterial diversity was independent of the salinity in both the abundance and rare sub-community. Besides diversity, no flourishing of abundant bacteria relative abundance is observed with increasing or decreasing salinity. Yet the rare taxa exhibit a bio-growth with salinity, which has a significant correlation (p < 0.001) with sulfate concentration. The responses of the abundant sub-community taxa to nutrients, temperature, pH, and dissolved oxygen are insensitive. However, the correlation between δ18O, δD and the entire community diversity is significant, which demonstrates the bacterial community is affected by the groundwater origin. Besides, not all the species in one class or order are necessarily shaped by the same factor. To quantify the impact of EFs on the community properties, analyses in different taxonomic levels is suggested. These findings imply that the spatial organization of microbial communities is complicated and influenced by multiple factors on a regional scale. The investigated results are useful for understanding biogeochemical processes in the coastal groundwater.