Groundwater Pollution Source Identification Research Articles

Shallow Groundwater Quality Assessment and Pollution Source Apportionment: Case Study in Wujiang District, Suzhou City

Groundwater serves as a crucial resource, with its quality significantly impacted by both natural and human-induced factors. In the highly industrialized and urbanized Yangtze River Delta region, the sources of pollutants in shallow groundwater are more complex, making the identification of groundwater pollution sources a challenging task. In this study, 117 wells in Wujiang District of Suzhou City were sampled, and 16 groundwater quality parameters were analyzed. The fuzzy synthetic evaluation method was used to assess the current status of groundwater pollution in the study area; the principal component analysis (PCA) was employed to discern the anthropogenic and natural variables that influence the quality of shallow groundwater; and the absolute principal component scores–multiple linear regression (APCS-MLR) model was applied to quantify the contributions of various origins toward the selected groundwater quality parameters. The results indicate that the main exceeding indicators of groundwater in Wujiang District are I (28%), NH4-N (18%), and Mn (14%); overall, the groundwater quality is relatively good in the region, with localized heavy pollution: class IV and class V water are mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Through PCA, five factors contributing to the hydrochemical characteristics of groundwater in Wujiang District were identified: water–rock interaction, surface water–groundwater interaction, sewage discharge from the textile industry, urban domestic sewage discharge, and agricultural non-point source pollution. Additionally, the APCS-MLR model determined that the contributions of the three main pollution sources to groundwater contamination are in the following order: sewage discharge from the textile industry (10.63%) > urban domestic sewage discharge (8.69%) > agricultural non-point source pollution (6.26%).

Identification of groundwater pollution sources based on optimal layout of groundwater pollution monitoring network

Identification of groundwater pollution sources based on optimal layout of groundwater pollution monitoring network

Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy

Groundwater pollution source identification (GPSI) is an important prerequisite for pollution remediation and risk assessment. However, an accurate GPSI is usually difficult to achieve due to the need to simultaneously consider practical problems and theoretical research. For practical problems, the boundary conditions, especially the concentration boundary, are often given as known constants through prior information. However, in most practical situations, the boundary conditions are complex and cannot be accurately estimated in advance, which leads to the distortion of the final identification results. Therefore, this study focused on the concentration boundary, and first proposed to jointly identify three types of unknown variables (source information, model parameters, and boundary conditions) to ensure that the identification results had more practical value. For theoretical research, as the number of unknown variable types increases, the difficulty of solving the inverse problem often increases, which may lead to inaccurate inversion results. Thus, a novel ensemble smoother with multiple data assimilation (ES-MDA) with a wheel battle strategy was proposed, enhancing the identification accuracy. We designed two cases to verify the effectiveness and practicality of the above ideas: a low-dimensional Case 1 (including four different synthetic scenarios: three scenarios with unknown boundary conditions under three concentration boundary modes and a scenario with known boundary conditions) and a high-dimensional complex Case 2. Identifying the boundary conditions in GPSI was found to be of great significance. Compared to the standard ES-MDA method, the ES-MDA with a wheel battle strategy proposed here could improve the inversion accuracy, and had certain effectiveness and practicality.

Identification of groundwater pollution sources and health risk assessment in the Fengshui mining area of Central Shandong, China.

The crux of groundwater protection lies in a profound understanding of the sources of pollutants and their impacts on human health. This study selected 47 groundwater samples from the Fengshui mining area in central Shandong Province, China, employing advanced hydrogeochemical techniques, positive matrix factorization (PMF), and Monte Carlo analysis methods, aimed at unveiling the characteristics, origins, and health risks of water pollutants. The results indicated that the majority of samples exhibited a slightly alkaline nature. Notably, the concentrations of fluoride (F-) and nitrate (NO3-) exceeded China's safety standards in 40.43% and 23.40% of the samples, respectively. Moreover, a water quality index (WQI) below 50 was observed in approximately 68.09% of the sites, suggesting that the water quality in these areas generally met acceptable levels. However, regions with higher WQI values were predominantly located in the northern and southern parts of the mining area. PMF analysis revealed that regional geological and industrial activities were the primary factors affecting water quality, followed by mining discharges, fundamental geological and agricultural processes, and leachate enrichment activities. The health risk assessment highlighted the heightened sensitivity of the youth demographic to fluoride, with a more pronounced non-carcinogenic risk compared to nitrate, affecting about 31.89% of the youth population. Hence, it is imperative for local authorities and relevant departments to take prompt actions to remediate groundwater contamination to minimize public health risks.

Identification of groundwater pollution sources and health risk assessment in the Songnen Plain based on PCA-APCS-MLR and trapezoidal fuzzy number-Monte Carlo stochastic simulation model

The source of inorganic pollutants in the groundwater of the Songnen Plain are complex; therefore, it is urgent to analyze the pollution sources and their contribution rates, and to determine the effects of pollutants on human health. In this study, the solution for the insufficient extraction of characteristic information and subjective weight in principal component analysis (PCA) was found using log-centralization and the entropy weight method. Based on 896 groundwater samples, the types and distribution characteristics of pollution sources were identified, and the APCS-MLR receptor model was used to quantitatively characterize the contribution of pollution sources to water quality. Considering the uncertainty of the health risk assessment process, a trapezoidal fuzzy number-Monte Carlo stochastic simulation model (TFN-MCS) was constructed. Overcoming the inapplicability of triangular fuzzy numbers in dealing with highly discrete water quality data. The results showed that: (1) The five potential pollution sources in the Songnen Plain were salinity factor (34.086 %), agricultural pollution factor (15.854 %), geological background factor (13.11 %), industrial pollution factor (10.382 %) and organic pollution factor (7.382 %). The degree of groundwater pollution in the study area is controlled by the terrain, hydrogeological conditions, land use type, and industrial layout. (2) The APCS-MLR receptor model was suitable for the allocation of potential sources of groundwater pollution. The average contributions rate of the five common factors to groundwater pollution were 38.86, 14.12, 15.73, 14.6, and 6.77 %. Salinity factor plays a major role in groundwater quality. (3) The THI of children and adults were 2.15–15.03 and 0.86–7.261, respectively, much higher than the safety threshold of one. In general, the THI of children from ingesting contaminated groundwater is twice that to adults. NO3– and F- are the primary risk control indicators for groundwater in the study area. These results support the formulation of effective groundwater pollution control strategies.

Explicit K-Means Clustering Assisted Direct Model for Identification of Groundwater Pollution Sources

A computationally inexpensive and data parsimonious direct model for identification of groundwater pollution sources that captures their location, spatial extent and flux intensities at a chosen reference time is presented in this study. The model is based upon instantaneous micro-level mass balance that is depicted by a pseudo steady state solute transport equation. Clusters of polluting locations were identified by finite differencing the solute transport equation and applying K-means clustering algorithm. The model is validated by the problem taken from Singh, Datta, and Jain (2004) and is also illustrated by applying it to an area lying between river Krishni and Hindon. Validation results showed that the model captured closely the pollution source locations; their corresponding spatial extents and flux intensities. Model captured the sources even when the concentration plumes are far away from the source locations due to advection dominated contaminant transport. Illustration results showed that the model identified the leachate flux intensity from irrigation in the form of a regional source. Further, it established a few local sources arising possibly out of localized industrial and/or municipal waste water disposal.

Groundwater pollution source identification using Metropolis-Hasting algorithm combined with Kalman filter algorithm

Increasing the precision of groundwater pollution source identification (GPSI) is crucial for groundwater pollution control and risk management. Bayesian theory based on the Markov Chain Monte Carlo (MCMC) method is a useful strategy of solving the GPSI problem. However, because of the nonlinear and uncertainty characteristics of GPSI, the Metropolis-Hasting (MH) algorithm, one of the most well-known MCMC algorithms, has the disadvantage of relatively low precision and is time-consuming. To address this problem, the Kalman filter (KF) algorithm was combined with the MH algorithm and referred to as the Kalman filter Metropolis-Hasting (KF–MH) algorithm. The algorithm generates a new initial distribution that is close to the true value through a prior distribution, and the new initial distribution is used to perform subsequent iterations of the calculation. The viability and superiority of the proposed KF-MH algorithm were assessed in three hypothetical GPSI cases under different conditions. In the inversion process, a surrogate model was constructed using a temporal convolutional network (TCN) to reduce the computational pressure imposed by the numerical simulation model. The results of the TCN surrogate model in the cases illustrate the high accuracy of the TCN surrogate model in fitting the groundwater numerical model. In the three cases, the normalized errors between the identification results and the true values of the source features obtained with the KF–MH algorithm were significantly lower than those of the MH algorithm. The results indicate that the proposed KF–MH algorithm has higher inversion accuracy than the MH algorithm.

Laboratory observations of solute transport in groundwater basins

This paper introduces a method of groundwater pollution source identification obtained from laboratory by a physical instrument of Flow System Sand-Box Model. A Unit basin of a simple flow system and a Tóthian basin of a hierarchically nested flow structure are introduced as case studies. We add step-input solutes at recharge zones, separately or simultaneously, to obtain the breakthrough curves (BTCs) at each discharge zone. For the Unit basin, its BTC shows a gradual upward trend with a decreasing slope but with two more pronounced steps, which differs a lot from previous results of analytical and numerical solutions. These steps are caused by the settings of the current physical instrument as no solutes are input at the upper two discharge ports. For the Tóthian basin, internal steps may exist due to the late-time arrival of intermediate and regional groundwater flow systems. Although the Unit basin and Tóthian basin can both have internal steps in the BTCs, they are due to different reasons. By jointly solving solute mass balance equations with water mass balance equations, the circulation rate for each flow system is calculated, which quantitatively explains the phenomena encountered in the experiment and highlights the important contributions of intermediate and regional flow systems to the entire groundwater circulation. We indirectly obtain the pulse-input BTCs by deriving the step-input BTCs respective to time, which is also the residence time distribution (RTD) of the groundwater basin, that the groundwater pollution sources can be identified. The pulse-input BTC is characterized by peaks and sloping tails. The peaks correspond to the intermediate and regional groundwater flow systems. The sloping tails indicate the rate at which solutes are flushed out or contaminants fade away. By inputting tap water into the groundwater basin to purify the contaminations, we conduct self-purification experiments. The shapes of the solute-input curves and the self-purification curves are systematically symmetrical. As the self-purification experiment has less external interference, it is possible to use the self-purification curves to characterize and identify the groundwater flow system. This study proposes a physical method for identifying sources of groundwater contamination, and achieves a comprehensive understanding of the law of solute transport in groundwater basins, which could guide the prevention of non-point contamination at the basin scale.

Groundwater pollution source identification and health risk assessment in the North Anhui Plain, eastern China: Insights from positive matrix factorization and Monte Carlo simulation

Groundwater contaminants from natural and anthropogenic sources pose a serious threat to the ecological environment and public health. In this study, 30 groundwater samples were collected from shallow wells at a large central water source in the North Anhui Plain, eastern China. Hydrogeochemical methods, positive matrix factorization (PMF) model, and Monte Carlo simulation were used to determine the characteristics, sources, and human health risks of inorganic and organic analytes in groundwater. The groundwater was weakly alkaline with high total hardness and was dominated by HCO3-Mg·Ca, HCO3-Ca·Mg, and HCO3-Ca·Mg·Na hydrochemical facies. The concentration of naphthalene was at a safe level, while the concentrations of F−, NO3− and Mn in 16.7 %, 26.7 % and 40 % of the samples, respectively, exceeded threshold risk-based values based on Chinese groundwater quality standards. Hydrogeochemical methods revealed that water-rock interactions (including weathering of silicate minerals, dissolution of carbonates, and cation exchange), acidity, and runoff conditions control the migration and enrichment of these analytes in groundwater. The PMF model indicated that local geogenic processes, hydrogeochemical evolution, agricultural activities, and petroleum-related industrial sources were the main factors affecting groundwater quality, with contributions of 38.2 %, 33.7 %, 17.8 %, and 10.3 %, respectively. A health risk evaluation model based on Monte Carlo simulation indicated that 77.9 % of children were exposed to a total noncarcinogenic risk above safe thresholds, about 3.4 times higher than the risk to adults. The main contributor to human health risk was F− originating from geogenic processes; thus, F− was identified as a priority for control. This study demonstrates the feasibility and reliability of combining source apportionment techniques and health risk assessment to evaluate groundwater quality.

Identification of Clandestine Groundwater Pollution Sources in Aquifer Polluted with BTEX

One of the biggest challenges in characterizing clandestine groundwater pollution sources is correctly estimating the number of such sources and their physical locations. Indiscreet underground disposal of toxic waste leading to groundwater pollution is widespread, and it is extremely difficult to detect the number of such sources and their locations. In this study, a real-life scenario of groundwater pollution in suburban New South Wales, Australia, is solved using a simulated annealing (SA)–based linked simulation optimization (LSO) technique with Benzene, Toluene, Ethylbenzene and Xylene (BTEX). The developed LSO considers the number of pollutant sources and their locations as explicit unknowns. This overcomes a crucial limitation in the earlier methods that limited the search to a few known potential source locations. The results show the applicability of the developed methodology to real-life groundwater pollution problems without making limiting assumptions about the number of sources or potential source locations being known, which has not been addressed to date.

Hydrogeochemical analysis and groundwater pollution source identification based on self-organizing map at a contaminated site

Groundwater contamination at the site has become a very serious problem. A clear understanding of the hydrogeochemical characteristics of groundwater is indispensable for pollution remediation. It requires taking a number of samples and continuous monitoring. However, it is challenging to interpret hydrogeochemical datasets with diverse compositions and wide range of concentration by linear method. In this work, combination of self-organizing map (SOM) and K-means clustering was applied to investigate the hydrogeochemical characteristics at a contaminated site. The results showed that shallow groundwater hydrogeochemical characteristics were performed by 42 neurons and were classified into 5 clusters. The NO3– in cluster 1 widely distributed in the site. The application of fertilizers led to high NO3– concentration in groundwater. Cluster 2 was dominated by Ca2+, Mg2+, Cr(Ⅵ) and NO2– and cluster 3 was characterized by TDS, Na+, Cl−, HCO3– and SO42−. Pollutants were mainly from the migration of components at the chromium slag heap under the effect of convection and dispersion. Cluster 4 was dominated by pH, As and CO32–. Furthermore, the pH with the minimum of 8.3 and the presence of CO32– in groundwater provided a favorable opportunity for arsenic enrichment. Pollutants in cluster 4 originated from rainfall leaching on the chromium slag. Moreover, the migration of components from cluster 4 to cluster 2 was also observed by SOM and numerical simulation. Cluster 5 was mainly dominated by Mn and Fe. Reduced environment and anthropogenic activities caused Fe and Mn to exceed standards. The deep groundwater characteristics were performed using 20 neurons and were identified into 4 clusters. Its contamination was due to the leakage of shallow groundwater. Finally, the Gibbs diagram and the saturation index method performed the chemistry control mechanisms of different clusters. This study demonstrated that SOM could be used to interpret nonlinear and complex contamination datasets.

Study on simultaneous inversion groundwater pollution sources and aquifer parameters based on bilevel programming

Groundwater pollution source identification is the first step of groundwater remediation planning and an important link of groundwater pollution source identification. In order to make the inversion result more accurate, the bilevel programming is proposed to test its availability. In this paper, iterative time, optimization algorithm and noise level are used to test the accuracy of bilevel programming. The advantages and disadvantages of genetic algorithm, multi-objective genetic algorithm and simulated annealing algorithms are compared in searching optimal decision variables. The results show that the combination of multi-objective genetic algorithm and bilevel programming has better accuracy and robustness. The bilevel programming can be used to identify the groundwater pollution source and aquifer parameters.

Groundwater Pollution Source Identification by Combination of PMF Model and Stable Isotope Technology

The pollution source identification methods based on traditional water quality monitoring and pollutant discharge loading typically require a high frequency of monitoring and generate a level of uncertainty in the identification results, owing to their limitations on the accurate and quantitative assessment of pollution source identification, migration, and transformation. This study combined multivariate statistical analysis and stable isotope technology to identify groundwater pollution sources in a typical multiple land-use area of the Chengdu Plain. A positive matrix factorization (PMF) model was adopted to reduce the interference of mass environmental factors on source identification and to determine the main factors influencing groundwater quality. Subsequently, a Bayesian stable isotope mixing model was developed to quantify the apportionment of each pollution source to groundwater nitrate (NO3-) with the consideration of hydro-chemical and land-use information. The results showed that the concentrations of NO3-, NO2-, NH4+, Mn, Fe, SO42-, and Cl- in groundwater of the study area exceeded the standard to different extents, presenting spatial variation. The main form of inorganic nitrogen in groundwater was NO3-. In general, concentrations of groundwater NO3- were the highest in vegetable fields (9.29 mg·L-1 on average), followed by livestock and poultry breeding farms (7.66 mg·L-1) and arable land (7.09 mg·L-1), whereas concentrations of groundwater NO3- in industrial areas were the lowest (2.20 mg·L-1). Groundwater quality in the study area was affected by geological processes, agricultural activities, hydrogeochemical evolution, and domestic and industrial discharges. Agricultural activities were the main contributor to the increase in groundwater NO3- in the study area. Chemical fertilizer (32%) and soil nitrogen (25%) contributed greatly to groundwater NO3- in agricultural areas, whereas sewage (28%) and atmospheric precipitation (27%) contributed most groundwater NO3- in industrial areas. Thus, the combination of multivariate statistical analysis and stable isotope technology could identify groundwater pollution sources and their apportionment effectively, providing scientific support for the prevention and control of groundwater pollution.

Machine learning for groundwater pollution source identification and monitoring network optimization.

The identification of the source in groundwater pollution is the only way to drastically deal with resulting environmental problems. This can only be achieved by an appropriate monitoring network, the optimization of which is prerequisite for the solution of the inverse modeling problem, i.e., identifying the source of the pollutant on the basis of measurements taken within the pollution field. For this reason, a theoretical confined aquifer with two pumping wells and six suspected sources is studied. Simulations of combinations of possible source locations, and hydraulic parameters, produce sets of measurement features for a 29 × 29 grid representing potential monitoring wells. Three sets of simulations are conducted to produce synthetic datasets, representing different groundwater pollution modeling methods. Features (input-X variables) coupled with respective sources (output-Y variables) are formulated in two different dataset formats (Types A, B) in order to train classification (random forests, multilayer perceptron) and computer vision (convolutional neural networks) algorithms, respectively, to solve the inverse modeling problem. In addition, appropriate feature selection and trial-and-error tests are employed for supporting the optimization of monitoring wells’ number, locations and sampling frequency. The methodology can successfully produce various sub-optimal monitoring strategies for various budgets.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00521-022-07507-8.

Hydrochemical characteristics and identification of groundwater pollution sources in tropical savanna.

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic processes. In this study, classic chemical and multivariate statistical methods were utilized to assess the groundwater quality and ascertain the potential contamination sources affecting the groundwater quality of Galma sub-watershed in a tropical savanna. For this purpose, the data set of 18 groundwater quality variables covering 57 different sampling boreholes (BH) was used. The groundwater samples essentially contained the cations in the following order of dominance: Ca2+ > Na+ > Mg2+ > K+. However, the anions had HCO3- > Cl- > SO4-2 > NO3- respectively. The hydrochemical facies classified the groundwater types of the sub-watershed into mixed Ca-Mg-Cl type of water, which means no cations and anions exceeds 50%. The second dominant water type was Ca-Cl. The Mg-HCO3 water type was found in BH 9, and Na-Cl water type in BH 29 of the studied area. The weathering of the basement rocks was responsible for the concentrations of these ions in the groundwater chemistry of the sub-watershed. Hierarchical cluster analysis (HCA) grouped the groundwater samples (boreholes) into five clusters that are statistically significant regarding the similarities of groundwater quality characteristics. The principal component analysis (PCA) extracted two major principal components explained around 65% of the variance and suggested the natural and anthropogenic processes especially the agricultural pollutants including synthetic fertilizers, and leaching of agricultural waste as the main factors affecting the groundwater quality. The integrated method proved to be efficient and robust for groundwater quality evaluation, as it guaranteed the precise assessment of groundwater chemistry in the sub-watershed of the tropical savanna. The findings of this investigation could be useful to the policy makers for developing effective groundwater management plans for the groundwater resources and protection of the sub-watershed.

Groundwater pollution source identification problems with unknown aquifer parameters by ADGA approach

High-cost remediation of groundwater pollution makes it important to obtain exact information about the source. This is quite difficult to achieve in naturally ill-posed inverse problems of this kind. If the aquifer parameters are also unknown, the problem becomes even more challenging. To address this difficulty, we propose the alternating direction genetic algorithm (ADGA) approach, together with modification of the order of magnitude of the decision variables, to increase the accuracy of the results and computational efficiency. Seven scenarios were designed to test the accuracy of the proposed approach in aquifer with different properties, number of pollution sources, parameters and measurement errors. The results show that combining the ADGA approach with modification of the order of magnitude of the decision variables for identifying both groundwater pollution source and aquifer parameters significantly increases the accuracy of estimated results. The NE value for the estimated results decreased from 9.81% to 58.44% for different cases, and computation time is about half decreased. In addition, the approach is applicable in situations where concentrations of observational data with measurement error, and for multiple source locations and non-uniform media.

"Accelerating a Geostatistical Approach to Groundwater Pollution Source Identification with GPU Computing"

"Accelerating a Geostatistical Approach to Groundwater Pollution Source Identification with GPU Computing"

Groundwater Pollution Source Identification and Apportionment Using PMF and PCA-APCS-MLR Receptor Models in Tongchuan City, China.

Potential sources of groundwater pollution in Tongchuan City, China, were qualitatively identified based on 14 key water quality indicators of 59 groundwater samples, and the contribution of each source to groundwater quality was quantitatively evaluated. Groundwater pollution sources were analyzed using PMF and PCA-APCS-MLR models, and their applicability in groundwater pollution assessment in Tongchuan City was tested. Results indicate that both models identified four sources of groundwater contamination. Natural evolution was the main cause of groundwater pollution in the study area, followed by the coal industry, agriculture, and urbanization. Although the spatial distribution of pollution sources in the two models differed, the urbanized area to the east of the study area was more severely affected by sewage discharge, the west was more obviously affected by the coal industry, and the north was mainly polluted by agriculture. Both of the fitting results of the two models are good, but R2 values obtained by the PMF model (0.4440-0.9991) were generally higher than those obtained by the PCA-APCS-MLR model (0.5180-0.9530), indicating that PMF model results were more accurate than the PCA-APCS-MLR model.

Hybrid homotopy-PSO global searching approach with multi-kernel extreme learning machine for efficient source identification of DNAPL-polluted aquifer

Groundwater pollution source identification (GPSI), which is critical for taking effective measures to protect groundwater resources, assess risks, and design remediation strategies, typically involves the solution of a nonlinear and ill-posed inverse problem. Regarding the inversion of dense non-aqueous phase liquid (DNAPL) sources, the special characteristics of pollutants render related research more complex. In the present study, homotopy-based optimization inverse theory and multi-kernel extreme learning machine (MK-ELM) were combined for efficiently solving GPSI problem while estimating aquifer parameters at a DNAPL-polluted site. The extreme learning machine incorporating multi kernels and whose parameters are obtained by means of a genetic algorithm (GA) was embedded in an optimization model for GPSI to replace the multiphase flow simulation model and to mitigate the considerable computational burdens of inversion iteration. The hybrid homotopy-particle swarm optimization (PSO) algorithm was constructed as a more efficient method for segmentally searching the global optimum in wide areas with low dependence on initial values. Results showed that the application of GA-based MK-ELM and hybrid homotopy-PSO effectively accomplish the simultaneous identification of source characteristics and aquifer parameters. The MK-ELM approximate the outputs of multiphase flow simulation model sufficiently with the certainty coefficient (R2) increased to 0.9982, whereas the mean relative error was limited to 1.5168%. Compared to the widely used PSO algorithm, the hybrid homotopy-PSO algorithm significantly reduced the mean relative error of identification results from 6.77% to 2.89%.

Application of stochastic programming in groundwater pollution source identification

Groundwater pollution is a serious threat to the ecological environment and human life. It is necessary to determine the characteristics of pollution sources accurately and efficiently after the occurrence of pollution. The main basis for determining the characteristics of pollution sources is the pollutant concentration data of each observation well. However, due to the layout of the monitoring wells, the noise intensity of observation well concentration and unknown aquifer parameters, inversion results of groundwater pollution sources will be influenced significantly. Therefore, this article focuses on how to reduce its influence on the inversion results. In this article, a stochastic programming optimization model is introduced to explore its ability to control errors in complex situations. Results show that: in homogeneous aquifer, the normalized error (NE%) produced by simulation-optimization method is stable at about 2%, and the NE generated by stochastic programming method in confidence interval of 60%∼95% is between 0.20% and 5.42%. Moreover, stochastic programming model can effectively control the influence of noise. In the simulation-optimization model, when the noise intensity is 0.1 ∼ 0.5, the NE value is 2.01%∼12.68%, and the corresponding NE of stochastic programming model is 0.12%∼11.87%. Finally, this article considers the case that aquifer parameters are unknown (simultaneous identification of aquifer parameters and groundwater pollution sources). The results show that with the increase of the number of unknown aquifer parameters, the NE of the simulation-optimization model gradually increases from 1.16% to 8.75%. The NE value of the stochastic programming model decreases by 30% compared with the simulation-optimization model when the confidence level is 80%.