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Groundwater pollution source recognition (GPSR) is a prerequisite for subsequent pollution remediation and risk assessment work. The actual observed data are the most important known condition in GPSR, but the observed data can be contaminated with noise in real cases. This may directly affect the recognition results. Therefore, denoising is important. However, in different practical situations, the noise attribute (e.g., noise level) and observed data attribute (e.g., observed frequency) may be different. Therefore, it is necessary to study the applicability of denoising. Current studies have two deficiencies. First, when dealing with complex nonlinear and non-stationary situations, the effect of previous denoising methods needs to be improved. Second, previous attempts to analyze the applicability of denoising in GPSR have not been comprehensive enough because they only consider the influence of the noise attribute, while overlooking the observed data attribute. To resolve these issues, this study adopted the variational mode decomposition (VMD) to perform denoising on the noisy observed data in GPSR for the first time. It further explored the influence of different factors on the denoising effect. The tests were conducted under 12 different scenarios. Then, we expanded the study to include not only the noise attribute (noise level) but also the observed data attribute (observed frequency), thus providing a more comprehensive analysis of the applicability of denoising in GPSR. Additionally, we used a new heuristic optimization algorithm, the collective decision optimization algorithm, to improve the recognition accuracy. Four representative scenarios were adopted to test the ideas. The results showed that the VMD performed well under various scenarios, and the denoising effect diminished as the noise level increased and the observed frequency decreased. The denoising was more effective for GPSR with high noise levels and multiple observed frequencies. The collective decision optimization algorithm had a good inversion accuracy and strong robustness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Swarm intelligence organized hybrid-kernel extreme learning machine (SIO-HKELM) was proposed to approximate the forward and inverse input-output correlation with a high accuracy using the DNAPL transport numerical simulation model. An adaptive inverse-HKELM was established for preliminary estimation of the source characteristics and contaminant transport parameters to correct prior information and generate high-quality initial starting points of parallel searching. A local accurate forward-HKELM surrogate of the numerical model was embedded in the searching system for avoiding repetitive CPU-demanding likelihood evaluations. A sensitivity-based Metropolis criterion (MC), incorporating the dynamic particle swarm optimization (SD-PSO) algorithm, was developed for improving the search ergodicity and realizing precise inversion of all the unknown variables with drastic variations in sensitivity to the likelihood function. Results showed that the generalization capability and robustness of SIO-HKELM were superior to those of the traditional machine learning methods, including KELM and support vector regression (SVR), and it sufficiently approximated the forward and inverse input-output mapping of the numerical model with testing determination coefficients of 0.9944 and 0.6440, respectively. With high-quality prior information and initial starting points generated by the adaptive inverse-HKELM feed approach, the uncertainty in the inversion outputs was reduced, and the searching process rapidly converged to reasonable posterior distributions in around 60 iterations. Compared with the widely used multichain Markov chain Monte Carlo (MCMC) approach, the parallel searching lines generated by SD-PSO-MC adequately covered the searching space, and the "equifinality" effect was more effectively restrained by reducing the relative errors of all the point estimations to less than 8%. Therefore, the real source information reflected by the statistical characteristics of the SD-PSO-MC inversion outputs was more precise than that obtained using the multichain MCMC approach., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In the optimal design of groundwater pollution monitoring network (GPMN), the uncertainty of the simulation model always affects the reliability of the monitoring network design when applying simulation-optimization methods. To address this issue, in the present study, we focused on the uncertainty of the pollution source intensity and hydraulic conductivity. In particular, we utilized simulation-optimization and Monte Carlo methods to determine the optimal layout scheme for monitoring wells under these uncertainty conditions. However, there is often a substantial computational load incurred due to multiple calls to the simulation model. Hence, we employed a back-propagation neural network (BPNN) to develop a surrogate model, which could substantially reduce the computational load. We considered the dynamic pollution plume migration process in the optimal design of the GPMN. Consequently, we formulated a long-term GPMN optimization model under uncertainty conditions with the aim of maximizing the pollution monitoring accuracy for each yearly period. The spatial moment method was used to measure the approximation degree between the pollution plume interpolated for the monitoring network and the actual plume, which could effectively evaluate the superior monitoring accuracy. Traditional methods are easily trapped in local optima when solving the optimization model. To overcome this limitation, we used the grey wolf optimizer (GWO) algorithm. The GWO algorithm has been found to be effective in avoiding local optima and in exploring the search space more effectively, especially when dealing with complex optimization problems. A hypothetical example was designed for evaluating the effectiveness of our method. The results indicated that the BPNN surrogate model could effectively fit the input-output relationship from the simulation model, as well as significantly reduce the computational load. The GWO algorithm effectively solved the optimization model and improved the solution accuracy. The pollution plume distribution in each monitoring yearly period could be accurately characterized by the optimized monitoring network. Thus, combining the simulation-optimization method with the Monte Carlo method effectively addressed the optimal monitoring network design problem under uncertainty., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Heavy computational load inhibits the application of groundwater contaminant numerical model to groundwater pollution source identification, remediation design, and uncertainty analysis, since a large number of model runs are required for these applications. Machine learning-based surrogate models are an effective approach to enhance the efficiency of the numerical models, and have recently attracted considerable attention in the field of groundwater contaminant modeling. Here, we review 120 research articles on machine learning-based surrogate models for groundwater contaminant modeling that were published between 1994 and 2022. We outline the state of the art method, identify the most significant research challenges, and suggest potential future directions. The six major applications of machine learning-based surrogate models are groundwater pollution source identification, groundwater remediation design, coastal aquifer management, uncertainty analysis of groundwater, groundwater monitoring network design, and groundwater transport parameters inversion. Together, these account for more than 90% of the studies we review. Latin hypercube sampling (LHS) is the most widely used sampling method, and artificial neural networks (ANNs) and Kriging are the two most widely used methods for constructing surrogate model. No method is universally superior, the advantages and disadvantages of different methods, as well as the applicability of these methods for different application purposes of groundwater contaminant modeling were analyzed. Some recommendations on the method selection for various application fields are given based on the reviews and experiences. Based on our review of the state-of-the-art, we suggest several future research directions to enhance the feasibility of the machine learning-based surrogate models of groundwater contaminant modeling: the alleviation of the curse of dimensionality, enhancing transferability, practical applications for real case studies, multi-source dada fusion, and real-time monitoring and prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Groundwater contaminant source identification (GCSI) has practical significance for groundwater remediation and liability. However, when applying the simulation-optimization method to precisely solve GCSI, the optimization model inevitably encounters the problems of high-dimensional unknown variables to identify, which might increase the nonlinearity. In particular, to solve such optimization models, the well-known heuristic optimization algorithms might fall into a local optimum, resulting in low accuracy of inverse results. For this reason, this paper proposes a novel optimization algorithm, namely, the flying foxes optimization (FFO) to solve the optimization model. We perform simultaneous identification of the release history of groundwater pollution sources and hydraulic conductivity and compare the results with those of the traditional genetic algorithm. In addition, to alleviate the massive computational load caused by the frequent invocation of the simulation model when solving the optimization model, we utilized the multilayer perception (MLP) to establish a surrogate model of the simulation model and compared it with the method of backpropagation algorithm (BP). The results show that the average relative error of the results of FFO is 2.12%, significantly outperforming the genetic algorithm (GA); the surrogate model of MLP can replace the simulation model for calculation with fitting accuracy of more than 0.999, which is better than the commonly used surrogate model of BP., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Groundwater pollution identification is an inverse problem. When solving the inverse problem using regular methods such as simulation-optimization or stochastic statistical approaches, requires repeatedly calling the simulation model for forward calculations, which is a time-consuming process. Currently, the problem is often solved by building a surrogate model for the simulation model. However, the surrogate model is only an intermediate step in regular methods, such as the simulation-optimization method that also require the creation and solution of an optimization model with the minimum objective function, which adds complexity and time to the inversion task and presents an obstacle to achieving fast inversion. In the present study, the extreme gradient boosting (XGBoost) method and the back propagation neural network (BPNN) method were used to directly establish the mapping relationships between the output and input of the simulation model, which could directly obtain the inversion results of the variables to be identified (pollution sources release histories and hydraulic conductivities) based on actual observational data for fast inversion. In addition, to consider the uncertainty of observation data noise, the inversion accuracy of the two machine learning methods was compared, and the method with higher precision was selected for the uncertainty analysis. The results indicated that both the BPNN and XGBoost methods could perform inversion tasks well, with a mean absolute percentage error (MAPE) of 4.15% and 1.39%, respectively. Using the BPNN, with better accuracy for uncertainty analysis, when the maximum probabilistic density value was selected as the inversion result, the MAPE was 2.13%. We obtained the inversion results under different confidence levels and decision makers of groundwater pollution prevention and control can choose different inversion results according to their needs., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In the traditional linked simulation-optimization method, solving the optimization model requires massive invoking of the groundwater numerical simulation model, which causes a huge computational load. In the present study, a surrogate model of the origin simulation model was developed using a bidirectional long and short-term memory neural network method (BiLSTM). Compared with the surrogate models built by shallow learning methods (BP neural network) and traditional LSTM methods, the surrogate model built by BiLSTM has higher accuracy and better generalization performance while reducing the computational load. The BiLSTM surrogate model had the highest R 2 of the three with 0.9910 and the lowest RMSE with 3.7732 g/d. The BiLSTM surrogate model was linked to the optimization model and solved using the sparrow search algorithm based on Sobol sequences (SSAS). SSAS enhances the diversity of the initial population of sparrows by introducing Sobol sequences and introduces nonlinear inertia weights to control the search range and search efficiency. Compared with SSA, SSAS has stronger global search ability and faster search efficiency. And SSAS identifies the contamination source location and release intensity stably and reliably. The average relative error of SSAS for the identification of source location is 9.4%, and the average relative error for the identification of source intensity is 1.83%, which are both lower than that of SSA at 11.12% and 3.03%. This study also applied the Cholesky decomposition method to establish a Gaussian field for hydraulic conductivity to evaluate the feasibility of the simulation-optimization method., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

An ultrasensitive electrochemical immunosensor based on signal amplification of the deposition of the electroactive ferrocene-tyramine (Fc-Tyr) molecule, catalyzed by horseradish peroxidase (HRP), was constructed for the detection of the liver cancer marker Glypican-3 (GPC3). Functional electroactive molecule Fc-Tyr is reported to exhibit both the enzymatic cascade catalytic activity of tyramine signal amplification (TSA) and the excellent redox properties of ferrocene. In terms of design, the low matrix effects inherent in using the magnetic bead platforms, a quasi-homogeneous system, allowed capturing the target protein GPC3 without sample pretreatment, and loading HRP to trigger the TSA, which induced a large amount of Fc-Tyr deposited on the electrode surface layer by layer as a signal probe for the detection of GPC3. The concept of Fc-Tyr as an electroactive label was validated, GPC3 biosensor exhibited high selectivity and sensitivity to GPC3 in the range of 0.1 ng mL -1 -1 μg mL -1 . Finally, the sensor was used simultaneously with ELISA to assess GPC3 levels in the serum of clinical liver cancer patients, and the results showed consistency, with a recovery of 98.33-105.35% and a relative standard deviation (RSD) of 4.38-8.18%, providing a theoretical basis for achieving portable, rapid and point of care testing (POCT) of tumor markers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The simulation optimization method was used to the identification of light nonaqueous phase liquid (LNAPL) groundwater contamination source (GCS) with the help of a hypothetical case in this study. When applying the simulation optimization method to identify GCS, it was a common technical means to establish surrogate model for the simulation model to participate in the iterative calculation to reduce the calculation load and calculation time. However, it was difficult for a single modeling method to establish surrogate model with high accuracy for the LNAPL contamination multiphase flow simulation model (MFSM). To give full play to advantages of single surrogate model and improve the accuracy of the surrogate model to the MFSM, a combination of deep belief neural network (DBNN) and long short-term memory (LSTM) neural network was used to establish artificial intelligence ensemble surrogate model (AIESM) for the MFSM. At the same time, to reduce the influence of noise in observed concentrations on the accuracy of the identification results, empirical mode decomposition (EMD) and wavelet analysis methods were used to denoise the observed concentrations, and their noise reduction effects were compared. The observed concentrations with better noise reduction effect and the observed concentrations without denoising were used to construct the objective function, and constraints of the optimization model were determined meanwhile. Then, the objective function and the constraints were integrated to build the optimization model to identify GCS and simulation model parameters. Applying the AIESM instead of the MFSM to embed in the optimization model and participate in the iterative calculation. Finally, the genetic algorithm (GA) was used to solve the optimization model to obtain the identification results of GCS and simulation model parameters. The results showed that compared with the single DBNN and LSTM surrogate models, AIESM obtained the highest accuracy and could replace the MFSM to participate in the iterative calculation, thereby reducing the calculation load and calculation time by more than 99%. Comparing with the wavelet analysis, EMD could reduce the noise in the concentrations more effectively, improved the accuracy of the approximated concentrations to the actual values, and increased the accuracy of the GCSs identification results by 1.45%., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Seawater intrusion is a common groundwater pollution problem, which has a great impact on ecological environment and economic development. In this paper, a numerical simulation model of variable density groundwater was constructed to simulate and predict the future seawater intrusion in Longkou city, Shandong Province of China. The influence of the sensitive parameter uncertainty of the model on the simulation results was evaluated by using the Monte Carlo method. In order to reduce the computational load from repeatedly calling the simulation model, the surrogate model was established by using the support vector regression (SVR) method. After training, the correlation coefficient R 2 of the input-output relationship between the SVR surrogate model and the seawater intrusion simulation model reached 0.9957, with an average relative error of 0.2%, indicating that the surrogate model has a high fitting accuracy. Stochastic simulations of seawater intrusion showed that the seawater intrusion in the Longkou area will gradually aggravate at a slow rate, and the increase of seawater intrusion in the study area after 30 years was expected to range from - 6.03% to 7.37% at the 80% confidence level., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The location and release history of groundwater contaminant sources (GCSs) are usually unknown after groundwater contamination is detected, thereby greatly hindering the design of contamination remediation schemes and contamination risk assessments. Many previous studies have used prior information such as the observed contaminant concentrations (OCC) to obtain information of GCSs, and various methods have been proposed for identifying GCSs, including simulation optimization (S/O) and ensemble Kalman filter (EnKF) methods. For the first time, the present study compared the suitability of the S/O and EnKF methods for GCSs identification based on two case studies by specifically considering the calculation time and effectiveness of GCS identification. The results showed that EnKF could reduce the calculation time required by more than 62% compared with S/O. However, the time saved did not compensate for the poor accuracy of the GCSs identification results. When the simulated contaminant concentrations (SCC) were used for GCSs identification, the MRE of the identification results with the S/O and EnKF methods were 2.79% and 5.09% in case one, respectively, and were 4.75% and 6.72% in case two. When the OCC were used for GCSs identification, the MRE of the identification results with the S/O and EnKF methods were 27.77% and 110.74% in case one, respectively, and 27.53% and 60.61% in case two. The identification results obtained using the EnKF method were not credible and the superior performance of the S/O method was obvious, thereby indicating that the EnKF method is much less suitable for actual GCSs identification compared with the S/O method., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In this study, it is shown for the first time that a reduced graphene oxide (rGO) carrier has a 20-fold higher catalysis rate than graphene oxide in Ag + reduction. Based on this, a tumor microenvironment-enabled in situ silver-based electrochemical oncolytic bioreactor (SEOB) which switched Ag + prodrugs into in situ therapeutic silver nanoparticles with and above 95% transition rate is constructed to inhibit the growths of various tumors. In this SEOB-enabled intratumoral nanosynthetic medicine, intratumoral H 2 O 2 and rGO act as the reductant and the catalyst, respectively. Chelation of aptamers to the SEOB-unlocked prodrugs increases the production of silver nanoparticles in tumor cells, especially in the presence of Vitamin C, which is broken down in tumor cells to supply massive amounts of H 2 O 2 . Consequently, apoptosis and pyroptosis are induced to cooperatively contribute to the considerably-elevated anti-tumor effects on subcutaneous HepG2 and A549 tumors and orthotopic implanted HepG2 tumors in livers of nude mice. The specific aptamer targeting and intratumoral silver nanoparticle production guarantee excellent biosafety since it fails to elicit tissue damages in monkeys, which greatly increases the clinical translation potential of the SEOB system., (© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Groundwater contamination source recognition involves the recovery of contamination source time series release histories from observation data. In the present study, a linear source contamination recognition task was addressed. When using a simulation-optimization inverse framework to solve the recognition task, high calculated expense and high dimensional search space always hinder the task efficiency. Moreover, traditional surrogate methods face obstacle of handling with time-sequence data. Therefore, a novel stacked chaos gate recurrent unit (SCGRU) neural network was proposed as a surrogate model to precisely emulate the sequence to sequence mapping relationship of a high computational running simulation model. To address the challenge of high dimensional search, a mixed-integer programming strategy was employed to reduce the dimension of unknown variables. Furthermore, a hybrid sparrow search algorithm (HSSA) was implemented to alleviate being trapped into local optimum. In particular, the proposed SCGRU-HSSA framework was utilized to determine the length and release intensities during the stress period of a linear source. Based on the results obtained, the following conclusions were derived: (1) SCGRU can replace the origin simulation model with high accuracy and fast running speed; (2) when using chaos sine mapping and a Cauchy mutation strategy, the SSA escaped from the local optimum, improving the search efficiency of the recognition task; and (3) SCGRU-HSSA methodology is stable and reliable in recognizing features of linear source contamination., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The groundwater contamination source identification (GCSI) can provide important bases for the design of pollution remediation plans. The Bayesian theory is commonly used in the GCSI problem. Usually, we use the Markov chain Monte Carlo (MCMC) method to realize the Bayesian framework. However, due to the ill-posed nature of the GCSI and the system model's complexity, the conventional MCMC algorithm is time-consuming and has low accuracy. In this study, we proposed an adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm. In this algorithm, the Kent mapping chaotic sequence method, combined with differential evolution (DE) algorithm, was used to generate the initial population. In the iteration process, we introduced a hybrid mutation strategy to generate the candidate vectors. Moreover, we adaptively adjust the essential parameter F of the AM-DEMC algorithm according to the individual fitness value. For further improving the efficiency of solving the GCSI problem, the Kriging method was used to establish a surrogate model to avoid the enormous computational load associated with the numerical simulation model. Finally, a hypothetical groundwater contamination case was given to verify the effectiveness of the AM-DEMC algorithm. The results indicated that the proposed AM-DEMC algorithm successfully identified the contamination sources' characteristics and simulation model's parameters. It also exhibited stronger search-ability and higher accuracy than the MCMC and DE-MC algorithms., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper first proposed a parallel heuristic search strategy for simultaneous identification of groundwater contamination source and aquifer parameters. As identification results are influenced by many factors, such as noisy contamination concentration data, data denoising is necessary. The existing wavelet threshold denoising method has unavoidable shortcomings; therefore, this paper first proposed a new weighted-average wavelet variable-threshold denoising (WWVD) method to improve the denoising effect for concentration data, which further enhanced the subsequent identification accuracy. However, frequent calls to the simulation model could produce high computational cost during likelihood calculation. Hence, single surrogate model of the simulation model was developed to reduce cost; however, it presented limitation. Thus, this paper first developed a differential evolution-tabu search (DE-TS) hybrid algorithm to construct an optimal ensemble surrogate model, which assembled Gaussian process, kernel extreme learning machine, and support vector regression. The first proposed DE-TS algorithm also improved the approximation accuracy of surrogate model to simulation model. This paper first proposed and implemented a parallel heuristic search iterative process for simultaneous identification, and the identification results were obtained when the iteration process terminated. The accuracy and efficiency of these newly proposed approaches were tested through a hypothetical case. Results showed that the WWVD method not only improved the denoising effect for concentration data but also enhanced the subsequent identification accuracy. The OES model using DE-TS hybrid algorithm improved the approximation accuracy of surrogate model to simulation model, and the parallel heuristic search strategy is helpful for simultaneous identification of groundwater contamination source and aquifer parameters., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Simultaneous identification of various features of groundwater contamination sources and hydraulic parameters, such as hydraulic conductivities, can result in high-nonlinear inverse problem, which significantly hinders identification. A surrogate model was proposed to relieve computational burden caused by massive callings to simulation model in identification. However, shallow learning surrogate model may show limited fitting ability to high nonlinear problem. Thus, in this study, a simulation-optimization method based on Bayesian regularization deep neural network (BRDNN) surrogate model was proposed to efficiently solve high-nonlinear inverse problem. This method identified eight variables including locations and release intensities of two pollution sources and hydraulic conductivities of two partitions. Three hidden layers were employed in the BRDNN surrogate model, which profoundly improved the fitting capacity of nonlinear mapping relationship to the simulation model. Furthermore, Bayesian regularization was applied in the training process of neural network to solve overfitting problem. The results indicated that BRDNN was capable of establishing input-output interplay of high nonlinear inverse problem, which substantially reduced computational cost while ensuring a desirable level of accuracy. The utility of simulation-optimization on the basis of BRDNN surrogate model provided stable and reliable inversion results for groundwater contamination sources and hydraulic parameters.

In this study, a heuristic search strategy based on stochastic-simulation statistic (S-S) approach was developed for groundwater contaminant source characterization (GCSC) with simulation model parameter estimation. First, single kernel extreme learning machine (KELM) was built as surrogate system of the numerical simulation model to reduce huge computational load while evaluating the likelihood. However, compared with single KELM, multi-kernel extreme learning machine (MK-ELM) is more flexible for large amounts of data. To improve the approximation accuracy of the surrogate system to numerical simulation model, the MK-ELM surrogate system was first developed. Then, a heuristic search iterative process was first designed for GCSC with simulation model parameter estimation. The self-adaptive sampling method was proved to be more efficient than one-time sampling. Based on this idea, a self-adaptive feedback correction step was inserted into the heuristic search iterative process to ameliorate the training samples of the surrogate system in the posterior region, which further improved accuracy of simultaneous identification results. Finally, the identification results were obtained when the iteration terminated. The proposed approaches were tested in a hypothetical case study. It was shown that the heuristic search strategy can be used to assist in groundwater contaminant source characterization with simulation model parameter estimation., Competing Interests: Declaration of Competing Interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the submitted manuscript entitled “Groundwater contaminant source characterization with simulation model parameter estimation utilizing a heuristic search strategy based on the stochastic-simulation statistic method”., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In this study, we develop a parallel heuristic search strategy based on a Bayesian approach for simultaneously recognizing groundwater contaminant sources and aquifer parameters (unknown variables) at sites contaminated with dense non-aqueous phase liquids (DNAPLs). The parallel search strategy is time-consuming because thousands of simulation models must run in order to calculate the likelihood. Various stand-alone surrogate systems for the simulation models have been established, but they also have unavoidable limitations. Thus, we develop an optimal combined surrogate system by combining Gaussian process, kernel extreme learning machine, and support vector regression methods using a differential evolution algorithm with a variable mutation rate based on the rand-to-best/1/bin strategy, thereby improving the approximation accuracy of the surrogate system to the simulation model and significantly decreasing the high computational cost. Utilizing the optimal combined surrogate system reduced the CPU time by more than 400 times. In the iterative parallel heuristic search process, each round of iteration involves determining the candidate points and state transitions. The Monte Carlo approach is used widely for selecting candidate point, but this approach does not readily converge to the posterior distribution for unknown variables when the probability density function types are complex with weak search ergodicity. In order to improve the search ergodicity, we develop a particle swarm optimization algorithm with a non-linear decreasing inertia weight and Metropolis criterion, which is more suitable for unknown variables with complex probability density functions. The recognition results are obtained simultaneously when the iterative process terminates. We assess our proposed approaches based on a hypothetical case study at a three-dimensional site contaminated with DNAPLs. The results demonstrate that the parallel heuristic search strategy is helpful for the simultaneous recognition of DNAPL contaminant sources in groundwater and aquifer parameters.

Measurements of contaminant concentrations inevitably contain noise because of accidental and systematic errors. However, groundwater contamination sources identification (GCSI) is highly dependent on the data measurements, which directly affect the accuracy of the identification results. Thus, in the present study, the wavelet hierarchical threshold denoising method was employed to denoise concentration measurements and the denoised measurements were then used for GCSI. A 0-1 mixed-integer nonlinear programming optimization model (0-1 MINLP) based on a kernel extreme learning machine (KELM) was applied to identify the location and release history of a contamination source. The results showed the following. (1) The wavelet hierarchical threshold denoising method was not very effective when applied to concentration measurements observed every 2 months (the number of measurements is small and relatively discrete) compared with those obtained every 2 days (the number of measurements is large and relatively continuous). (2) When the concentration measurements containing noise were employed for GCSI, the identifications results were further from the true values when the measurements contained more noise. The approximation of the identification results to the true values improved when the denoised concentration measurements were employed for GCSI. (3) The 0-1 MINLP based on the surrogate KELM model could simultaneously identify the location and release history of contamination sources, as well reducing the computational load and decreasing the calculation time by 96.5% when solving the 0-1 MINLP.

Effective coastal aquifer management typically relies on numerical models to analyze the seawater intrusion (SI) process. Before using groundwater simulation models to predict the extent of SI in the future, preparing input data is an extremely necessary and important step. For precipitation and sea-level rise (SLR), which are two of the most influential factors for SI, it is difficult to precisely forecast their variations. Current studies of using numerical models to predict future SI often overlook the uncertainty of these two factors. This can result in compromised predictions of SI. In this study, a three-dimensional variable-density groundwater simulation model was established for a coastal area in Longkou, China. Then, the Monte Carlo method was applied to perform uncertainty analysis for the input data of precipitation and SLR of the SI model. In order to reduce the huge computational load brought by repeated invocation of the SI model during the process of Monte Carlo simulation, a surrogate model based on a multi-gene genetic programming (MGGP) method was developed to replace the SI simulation model for calculation. A comparison between the MGGP surrogate model and the Kriging surrogate model was carried out, and the results show that the MGGP surrogate model has a distinct advantage over the Kriging surrogate model in approximating the excitation-response relationship of the variable-density groundwater simulation model. Through statistical analysis of Monte Carlo simulation results, an object and reasonable risk assessment of SI for the study area was obtained. This study suggests that it is essential to take the uncertainty of precipitation and SLR into account when modeling and predicting the extent of SI.

The simulation-optimization method is widely used in the design of the groundwater pollution monitoring network (GPMN). The uncertainty of the simulation model will significantly affect the design results of GPMN. When the Monte Carlo method is used to consider the influence of model uncertainty on the optimization results, the simulation model needs to be invoked many times, which will cause a huge amount of calculation. To reduce the calculation load, the study proposed to use the support vector regression (SVR) method to construct the surrogate model to couple the simulation model and the optimization model in the optimal design of GPMN. The optimization goal is to maximize the accuracy of the spatial description of pollution plume in each monitoring period. The study also considered the dynamic changes in the migration and morphological of pollution plumes in the optimization of GPMN. Finally, the West Shechang coal gangue pile in Fushun of China was used as a case study to verify the effectiveness of the above method. The results demonstrate that the SVR surrogate model can fit the input-output relationship of the simulation model to a high degree with less computation. The optimized monitoring network can reveal essential and comprehensive information about pollution plumes. The study provides a stable and reliable method for the design of GPMN.

Seawater intrusion is a common problem in coastal areas. The rational distribution of groundwater exploitation can minimize the scope of seawater intrusion and maximize groundwater exploitation. In this study, an optimization method for the groundwater exploitation layout in coastal areas was proposed. Based on the numerical simulation model of variable-density groundwater, a multiobjective groundwater management model was constructed with the objectives of maximizing groundwater exploitation and minimizing seawater intrusion. The optimization model was solved by nondominated sorted genetic algorithm-II (NSGA-II). To improve the computational efficiency of the optimization model, the surrogate models of the groundwater simulation model were built by using three different methods: kriging, support vector regression (SVR), and kernel extreme learning machines (KELM). Finally, the above methods were tested in Longkou City of China. The results show that the use of surrogate models can greatly reduce the computing time for solving seawater intrusion management problems. The surrogate model of the variable-density groundwater simulation model based on the SVR method has the best performance. The groundwater exploitation layout optimized by the above method is reasonable and can reflect the actual hydrogeological conditions in the study area. This study provides a reliable way to optimize the groundwater exploitation layout in coastal areas.

When using a simulation model to study seawater intrusion (SI), uncertainty in the parameters directly affects the results. The impact of the rise in sea levels due to global warming on SI cannot be ignored. In this paper, the Monte Carlo method is used to analyze the uncertainty in modeling SI. To reduce the computational cost of the repeated invocation of the simulation model as well as time, a surrogate model is established using a radial basis function (RBF)-based neural network method. To enhance the accuracy of the substitution model, input samples are sampled using the Latin hypercube sampling (LHS) method. The results of uncertainty analysis had a high reference value and show the following: (1) The surrogate model created using the RBF method can significantly reduce computational cost and save at least 95% of the time needed for the repeated invocation of the simulation model while maintaining high accuracy. (2) Uncertainty in the parameters and the magnitude of the rise in sea levels have a significant impact on SI. The results of prediction were thus highly uncertain. In practice, it is necessary to quantify uncertainty to provide more intuitive predictions.

Panax notoginseng is a well-known traditional Chinese medicine, and dried flower buds of P. notoginseng (FBP) have also been used as a medicine or tea for a long time. The pharmacological effects of FBP include antihypertensive, anticancer, hepatoprotective, and cardiovascular protective effects. The compounds in FBP include saponins, flavonoids, volatile oils, and polysaccharides. The total saponins are the principal bioactive components. In modern applications, FBP is used to treat hypertension and tinnitus. There have been many studies on FBP and its effects in recent years, and it has attracted much attention in the medical field. This review summarizes the chemical components, pharmacological action, and clinical uses of FBP comprehensively to provide the references of deeper studies., (© 2018 John Wiley & Sons, Ltd.)

It is necessary to identify the hydrogeochemical processes and analyze the causes of groundwater pollution due to the lack of knowledge about the groundwater chemical characteristics and the endemic diseases caused by groundwater pollution in the northern Ordos Cretaceous Basin. In this paper, groundwater chemical facies were obtained using the piper trilinear diagram based on the analysis of 190 samples. The hydrogeochemical processes were identified using ionic ratio coefficient, such as leaching, evaporation and condensation. The causes and sources of groundwater pollution were analyzed by correspondence analysis, and the spatial distribution and enrichment reasons of fluoride ion were analyzed considering the endemic fluorosis emphatically. The results show that leaching, evaporation and condensation, mixing, and anthropogenic activities all had significant impact on hydrogeochemical processes in the study area. However, cation exchange and adsorption effects were strong in the S2 and S3 groundwater flow systems, but weak in S1. Groundwater is mainly polluted by Mn and COD Mn in the study area. The landfill leachate, domestic sewage, and other organic pollutants, excessive use of pesticides and fertilizers in agriculture, and pyrite oxidation from long-term and large-scale exploitation of coal are the sources of groundwater pollution. The S1 has the highest degree of groundwater pollution, followed by S2 and S3. High concentration of fluoride ion is mainly distributed in the north and west of study area. Evaporation and condensation and groundwater chemistry component are the most important causes of fluoride ion enrichment. The results obtained in this study will be useful for understanding the groundwater quality for effective management and utilization of groundwater resources and assurance of drinking water safety.

In this study, we aimed to develop an optimal groundwater remediation design for sites contaminated by dense non-aqueous phase liquids by using an ensemble of surrogates and adaptive sequential sampling. Compared with previous approaches, our proposed method has the following advantages: (1) a surrogate surfactant-enhanced aquifer remediation simulation model is constructed using a Gaussian process; (2) the accuracy of the surrogate model is improved by constructing ensemble surrogates using five different surrogate modelling techniques, i.e., polynomial response surface, radial basis function, Kriging, support vector regression, and Gaussian process; (3) we conducted comparisons and analyses based on 31 surrogate models derived from different combinations of the five surrogate modelling techniques; and (4) the reliability of the optimal solution was improved by implementing adaptive sequential sampling. The two proposed methods were applied to a hypothetical perchloroethylene-contaminated site in order to demonstrate their performance. The results showed that the best surrogate model integrated all five of the surrogate modelling methods, with an R 2 value of 0.9913 and a root mean squared error of 0.0159, thereby demonstrating the advantage of using ensemble surrogates. In addition, the reliability of the optimization model solution was improved by adaptive sequential sampling, which avoided false solutions., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Simulation-optimization techniques are effective in identifying an optimal remediation strategy. Simulation models with uncertainty, primarily in the form of parameter uncertainty with different degrees of correlation, influence the reliability of the optimal remediation strategy. In this study, a coupled Monte Carlo simulation and Copula theory is proposed for uncertainty analysis of a simulation model when parameters are correlated. Using the self-adaptive weight particle swarm optimization Kriging method, a surrogate model was constructed to replace the simulation model and reduce the computational burden and time consumption resulting from repeated and multiple Monte Carlo simulations. The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were employed to identify whether the t Copula function or the Gaussian Copula is the optimal Copula function to match the relevant structure of the parameters. The results show that both the AIC and BIC values of the t Copula function are less than those of the Gaussian Copula function. This indicates that the t Copula function is the optimal function for matching the relevant structure of the parameters. The outputs of the simulation model when parameter correlation was considered and when it was ignored were compared. The results show that the amplitude of the fluctuation interval when parameter correlation was considered is less than the corresponding amplitude when parameter estimation was ignored. Moreover, it was demonstrated that considering the correlation among parameters is essential for uncertainty analysis of a simulation model, and the results of uncertainty analysis should be incorporated into the remediation strategy optimization process.

Surrogate-based simulation-optimization technique is an effective approach for optimizing the surfactant enhanced aquifer remediation (SEAR) strategy for clearing DNAPLs. The performance of the surrogate model, which is used to replace the simulation model for the aim of reducing computation burden, is the key of corresponding researches. However, previous researches are generally based on a stand-alone surrogate model, and rarely make efforts to improve the approximation accuracy of the surrogate model to the simulation model sufficiently by combining various methods. In this regard, we present set pair analysis (SPA) as a new method to build ensemble surrogate (ES) model, and conducted a comparative research to select a better ES modeling pattern for the SEAR strategy optimization problems. Surrogate models were developed using radial basis function artificial neural network (RBFANN), support vector regression (SVR), and Kriging. One ES model is assembling RBFANN model, SVR model, and Kriging model using set pair weights according their performance, and the other is assembling several Kriging (the best surrogate modeling method of three) models built with different training sample datasets. Finally, an optimization model, in which the ES model was embedded, was established to obtain the optimal remediation strategy. The results showed the residuals of the outputs between the best ES model and simulation model for 100 testing samples were lower than 1.5%. Using an ES model instead of the simulation model was critical for considerably reducing the computation time of simulation-optimization process and maintaining high computation accuracy simultaneously., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model., (Copyright © 2017 Elsevier B.V. All rights reserved.)

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Different gold mining and smelting processes can lead to distinctive heavy metal contamination patterns and results. This work examined heavy metal pollution from a large-scale cyanidation gold mining operation, which is distinguished from artisanal and small-scale amalgamation gold mining, in Jilin Province, China. A total of 20 samples including one background sample were collected from the surface of the mining area and the tailings pond in June 2013. These samples were analyzed for heavy metal concentrations and degree of pollution as well as sources of Cr, Cu, Zn, Pb, Ni, Cd, As, and Hg. The mean concentrations of Pb, Hg, and Cu (819.67, 0.12, and 46.92 mg kg -1 , respectively) in soil samples from the gold mine area exceeded local background values. The mean Hg content was less than the first-class standard of the Environmental Quality for Soils, which suggested that the cyanidation method is helpful for reducing Hg pollution. The geochemical accumulation index and enrichment factor results indicated clear signs that enrichment was present for Pb, Cu, and Hg, with the presence of serious Pb pollution and moderate presence to none of Hg and Cu pollution. Multivariate statistical analysis showed that there were three metal sources: (1) Pb, Cd, Cu, and As came from anthropogenic sources; (2) Cr and Zn were naturally occurring; whereas (3) Hg and Ni had a mix of anthropogenic and natural sources. Moreover, the tailings dam plays an important role in intercepting the tailings. Furthermore, the potential ecological risk assessment results showed that the study area poses a potentially strong risk to the ecological health. Furthermore, Pb and Hg (due to high concentration and high toxicity, respectively) are major pollutants on the risk index, and both Pb and Hg pollution should be of great concern at the Haigou gold mines in Jilin, China.

As the incidence frequency of groundwater pollution increases, many methods that identify source characteristics of pollutants are being developed. In this study, a simulation-optimization approach was applied to determine the duration and magnitude of pollutant sources. Such problems are time consuming because thousands of simulation models are required to run the optimization model. To address this challenge, the Kriging surrogate model was proposed to increase computational efficiency. Accuracy, time consumption, and the robustness of the Kriging model were tested on both homogenous and non-uniform media, as well as steady-state and transient flow and transport conditions. The results of three hypothetical cases demonstrate that the Kriging model has the ability to solve groundwater contaminant source problems that could occur during field site source identification problems with a high degree of accuracy and short computation times and is thus very robust., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Background: This study is a retrospective case analysis of 143 patients who suffered from severe intracranial hemorrhage and underwent a fast and simple procedure of cranial drilling followed with external ventricle drain treatment (referred as Fast-D here after) during 2003-2013 to evaluate the clinical effectiveness of the treatment., Methods: Fast-D procedure was conducted on 143 patients with severe acute craniocerebral diseases. Those patients were evaluated using activities of daily living (ADL) scales at hospital discharge and after 6-month of physical therapy, and were compared to 36 patients with similar craniocerebral diseases but received the traditional Dandy's surgical treatment., Results: At discharge, 11% (16 cases) was classified as ADL I (fully functional for physical and social activities); 26% (37 cases) had ADL II (fully functional for physical activities but partially impaired for social activities); 34% (49 cases) was ADL III (require assistance performing physical activities); 9% (13 cases) was ADL IV (being conscious, but completely lost ability of physical activities); 27% (10 cases) was ADL V (vegetative stage); and 13% (18 cased) was ADL VI (died) among the 143 patients. Six-month physical therapy improved ADL in 88% of the patients. Those outcomes are equal or better than the more complicated Dandy's procedure probably due to the time-saving factor., Conclusion: Fast-D procedure is much faster (6.7 min vs. 53.6 min of the Dandy's procedure) and can be performed outside operating rooms (computed tomography room or bedside). This technique could serve as a tool to rapidly release intracranial pressure and reduce subsequent morbidity and mortality of severe craniocerebral diseases when resource and condition are limited and more elaborate operating room procedures are not possible.

This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately.

The impacts of climate change on streamflow and non-point source pollutant loads in the Shitoukoumen reservoir catchment are predicted by combining a general circulation model (HadCM3) with the Soil and Water Assessment Tool (SWAT) hydrological model. A statistical downscaling model was used to generate future local scenarios of meteorological variables such as temperature and precipitation. Then, the downscaled meteorological variables were used as input to the SWAT hydrological model calibrated and validated with observations, and the corresponding changes of future streamflow and non-point source pollutant loads in Shitoukoumen reservoir catchment were simulated and analyzed. Results show that daily temperature increases in three future periods (2010-2039, 2040-2069, and 2070-2099) relative to a baseline of 1961-1990, and the rate of increase is 0.63°C per decade. Annual precipitation also shows an apparent increase of 11 mm per decade. The calibration and validation results showed that the SWAT model was able to simulate well the streamflow and non-point source pollutant loads, with a coefficient of determination of 0.7 and a Nash-Sutcliffe efficiency of about 0.7 for both the calibration and validation periods. The future climate change has a significant impact on streamflow and non-point source pollutant loads. The annual streamflow shows a fluctuating upward trend from 2010 to 2099, with an increase rate of 1.1 m(3) s(-1) per decade, and a significant upward trend in summer, with an increase rate of 1.32 m(3) s(-1) per decade. The increase in summer contributes the most to the increase of annual load compared with other seasons. The annual NH (4) (+) -N load into Shitoukoumen reservoir shows a significant downward trend with a decrease rate of 40.6 t per decade. The annual TP load shows an insignificant increasing trend, and its change rate is 3.77 t per decade. The results of this analysis provide a scientific basis for effective support of decision makers and strategies of adaptation to climate change.

Water storage depletion is a worsening hydrological problem that limits agricultural production in especially arid/semi-arid regions across the globe. Quantifying water storage dynamics is critical for developing water resources management strategies that are sustainable and protective of the environment. This study uses GRACE (Gravity Recovery and Climate Experiment), GLDAS (Global Land Data Assimilation System) and measured groundwater data products to quantify water storage in Western Jilin (a proxy for semi-arid wetland ecosystems) for the period from January 2002 to December 2009. Uncertainty/bias analysis shows that the data products have an average error <10% (p < 0.05). Comparisons of the storage variables show favorable agreements at various temporal cycles, with R(2) = 0.92 and RMSE = 7.43 mm at the average seasonal cycle. There is a narrowing soil moisture storage change, a widening groundwater storage loss, and an overall storage depletion of 0.85 mm/month in the region. There is possible soil-pore collapse, and land subsidence due to storage depletion in the study area. Invariably, storage depletion in this semi-arid region could have negative implications for agriculture, valuable/fragile wetland ecosystems and people's livelihoods. For sustainable restoration and preservation of wetland ecosystems in the region, it is critical to develop water resources management strategies that limit groundwater extraction rate to that of recharge rate.

This article discusses the generation and migration process of nitrate-N pollution in shallow groundwater caused by agricultural nonpoint source pollution in the catchment area of Shitoukoumen Reservoir in northeast China. By monitoring the shallow groundwater nitrate-N in the low-water period, the normal season, and high-flow period in the study area for a year, it was found that the nitrate-N concentration in the shallow groundwater of this area had a seasonal variation in both spatial and time distribution. In the time distribution, the peak value appeared in July, the high-flow period, and the valley value appeared in April, the low-water period, and showed a significant correlation with the time distribution of fertilization rate and rainfall. In the spatial distribution of nitrate-N pollution, when the distribution in shallow groundwater was analyzed separately in the three different periods (low-water period, the normal season, and high-flow period) and the discipline transference and enrichment of nitrate-N pollution in shallow groundwater was determined, this indicated that the region in the southeast study area where runoff conditions were better was less contaminated, and the region where runoff conditions were poor, as well as the region along the river were seriously polluted. The nitrate-N concentration in shallow groundwater was distributed mainly along the path of groundwater flow and was excreted in the drainage region. This showed that the spatial distribution of nitrate-N concentration in the shallow groundwater of the entire region was mainly controlled by the groundwater flow system. At the same time, in the middle and lower reaches of the study area, the seasonal changes in the recharged-excreted relationship between groundwater and river caused seasonal differences in the spatial distribution of nitrate-N pollution in groundwater. The combined effects of the groundwater mobility and the surface river resulted in a poor correlation between the groundwater nitrate-N concentration and land-use types. Only in the plain area where there was little influence from groundwater runoff and the surface river did the groundwater nitrate-N concentration correlate with land-use types. The spatial and time distribution of nitrate-N concentration in the shallow groundwater of the study area was impacted by agricultural nonpoint source pollution, the groundwater flow system, and the surface river and formed a concentration response system which uses basins as a unit.