Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Spatiotemporal variations in evapotranspiration and its influencing factors in the semiarid Hailar river basin, Northern China

Evapotranspiration (ET) is a critical variable in the world's water cycle, and plays a significant role in estimating the impact of environmental change on the regional hydrothermal cycle. Moreover, as an essential of eco-hydrological processes, changes in ET may exceptionally impact the local climate and provide indicative information on the eco-system's functioning. The Hailar River Basin (HRB), located in northern China, is one of the most sensitive areas to climate warming. Under the influence of climate change in recent years, the vegetation dynamics of the basin have been significant and have had profound effects on the regional water cycle conditions and hydrological processes. The HRB is located in a semiarid region and ET is the main mode of water consumption. The ET response to climate change and vegetation dynamics is the focus of research on ecohydrological processes in this basin. In this study, a distributed hydrological model, the BTOPMC model, is used to evaluate the actual ET in the HRB from 1981 to 2020, based on in situ meteorological data as well as LAI data obtained by satellite remote sensing. The seasonal, interannual and spatial dynamics of ET were characterized. The contribution of meteorological factors to ET was calculated by sensitivity analysis and multiple linear regression analysis, and the predominant elements influencing the difference in ET in the HRB were also discussed. The results show that: (1) estimated ET values can clarify over 85% of the seasonal variation in the observed values (R²= 0.79, P < 0.001; R²= 0.84, P < 0.001), which demonstrates that the model has a high precision. (2) Over the past 40 years, the annual ET has shown a clear increasing trend and a large spatial heterogeneity in its spatial distribution, which is consistent with the trend of vegetation. It mainly shows that the eastern forest area is larger than the central forest-grass transition area and the western meadow steppe area. (3) Sensitivity and influential factor contribution analyses show that the main factor driving interannual variability in ET is climate warming, followed by precipitation. At the same time, vegetation dynamics also play a crucial role in ET, especially in areas with different vegetation types and high coverage, while climatic factors also have a strong influence on ET indirectly through vegetation. Due to its special geographic location, the HRB is more sensitive to global climate change and is a typical ecologically fragile area. Therefore, this study has important scientific value and social significance for maintaining ecological security and the sustainable use of water resources.

Optimization of well field management to mitigate groundwater contamination using a simulation model and evolutionary algorithm

Groundwater represents the most important available freshwater reserves and is of critical importance to global water and food security. Old environmental burdens that have led to the spread of contaminants in groundwater limit its use, thus interventions to mitigate contamination must often be carried out to ensure a safe drinking water supply. This study presents the optimization of well field management designs to reduce the desethylatrazine (DEA) concentration in the deep wells of the Brest Water Works (Central Slovenia). It investigates artificial recharge by injection wells using water from the nearby river and elaborates five well field management scenarios prioritizing different objectives. A multi-objective simulation-optimization framework was developed. A transient groundwater flow and solute transport model was applied to simulate the effects of the proposed recharge and pumping regimes. The shuffled complex evolution method was used to identify optimal values of well field management variables (location of injection well(s), minimum required injection rate, maximum pumping rate from production well) in the proposed scenarios. Model simulations showed that optimized well field management designs can significantly reduce DEA concentration in production wells (below 0.05 μg/L), assure compliance with water quality standards with (26%) reduced injection rate, and, with the implementation of two injection wells, achieve lower DEA concentration and higher pumping rate (up to 27 L/s). The optimization solutions depend on the defined well field management priorities and reveal a trade-off between the objectives (reduction of DEA concentration, increase of pumping rate, and reduction of injection rate). The impact of management variables on mitigation efficiency is not uniform and largely depends on the location of the injection well(s), which increases the complexity of mitigation design. The study has shown that the presented approach can be efficiently used for finding optimal mitigation designs and supporting water managers with information for planning mitigation measures.

Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis-Hastings sampling

A quick and accurate identification of source information on sudden hazardous chemical leakage accident is crucial for early accident warning and emergency response. This study firstly regards source identification problem of sudden hazardous chemical leakage accidents as an inverse problem and presents a source identification model based on the Bayesian framework. Secondly, a new identification method is designed on the basis of particle swarm optimisation (PSO), differential evolution (DE) and the Metropolis-Hastings (M-H) sampling method. Lastly, the designed method, i.e. PSO-DE-MH, is verified by an outdoor experiment analyses in a section of the South-North Water Transfer Project. Results show that the number of iterations, the average absolute error, the average relative error and the average standard deviations of the identification results obtained by PSO-DE-MH are less than those of PSO-DE and DE-MH. Moreover, the relative error and the sampling relative error of the identification results under five different measurement errors (MEs) (σ = 0.01, 0.05, 0.1, 0.15, 0.2) are less than 9.5% and 0.2%, respectively. The designed method is effective even when the standard deviation of the ME increases to 0.2. Therefore, the designed method can effectively and accurately obtain the source information of sudden hazardous chemical leakage accidents. This study provides a new idea and method to solve the difficult problems of emergency management.

Identification of groundwater contamination sources and hydraulic parameters based on bayesian regularization deep neural network

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.

Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds

Generally, there is an inconsistency between the total regional crop area that was obtained from remote sensing technology and the official statistical data on crop areas. When performing scale conversion and data aggregation of remote sensing-based crop mapping results from different administrative scales, it is difficult to obtain accurate crop planting area that match crop area statistics well at the corresponding administrative level. This problem affects the application of remote sensing-based crop mapping results. In order to solve the above problem, taking Fucheng County of Hebei Province in the Huanghuaihai Plain of China as the study area, based on the Sentinel-2 normalized difference vegetation index (NDVI) time series data covering the whole winter wheat growth period, the statistical data of the regional winter wheat planting area were regarded as reference for the winter wheat planting area extracted by remote sensing, and a new method for winter wheat mapping that is based on similarity measurement indicators and their threshold optimizations (WWM-SMITO) was proposed with the support of the shuffled complex evolution-University of Arizona (SCE-UA) global optimization algorithm. The accuracy of the regional winter wheat mapping results was verified, and accuracy comparisons with different similarity indicators were carried out. The results showed that the total area accuracy of the winter wheat area extraction by the proposed method reached over 99.99%, which achieved a consistency that was between the regional remote sensing-based winter wheat planting area and the statistical data on the winter wheat planting area. The crop recognition accuracy also reached a high level, which showed that the proposed method was effective and feasible. Moreover, in the accuracy comparison of crop mapping results based on six different similarity indicators, the winter wheat distribution that was extracted by root mean square error (RMSE) had the best recognition accuracy, and the overall accuracy and kappa coefficient were 94.5% and 0.8894, respectively. The overall accuracies of winter wheat that were extracted by similarity indicators, such as Euclidean distance (ED), Manhattan distance (MD), spectral angle mapping (SAM), and spectral correlation coefficient (SCC) were 94.1%, 93.9%, 93.3%, and 92.8%, respectively, and the kappa coefficients were 0.8815, 0.8776, 0.8657, and 0.8558, respectively. The accuracy of the winter wheat results extracted by the similarity indicator of dynamic time warping (DTW) was relatively low. The results of this paper could provide guidance and serve as a reference for the selection of similarity indicators in crop distribution extraction and for obtaining large-scale, long-term, and high-precision remote sensing-based information on a regional crop spatial distribution that is highly consistent with statistical crop area data.

State, source and triggering mechanism of iron and manganese pollution in groundwater of Changchun, Northeastern China

The present state of iron (Fe) and manganese (Mn) concentration in groundwater of Changchun city located within the Songnen Plain of northeastern China was evaluated in this study. Heavy metal sources, as well as triggering mechanism, were analyzed using a physicochemical, statistical and spatial approach. Results revealed that out of the 2600 samples analyzed, 214 (representing 8.24%) for Fe and 606 wells (representing 23.34%) for Mn exceeded the water standard. Organic matter-rich sediments and Fe-Mn nodules in aquifer and soil serve as sources of Fe and Mn. Organic and inorganic complex formations, as well as long residence time, were found to foster the release of Fe and Mn into groundwater. Additionally, pH and well depth was important in triggering Mn dissolution while groundwater mineralization, depth to the water table and well proximity to the river were found to have minimal/negligible effect on heavy metal mobilization. The removal of Fe and Mn from the water before use was proposed along with the sinking of deeper wells for groundwater exploitation to limit the use of polluted water.

Integrated Modelling for Groundwater Contamination from Polluted Streams Using New Protection Process Techniques

Evaluating water quality indicators is a crucial issue in integrated water resource management, since potable water is an essential resource for the world's health and sustainable development. The current study was developed using a coupled model of MODFLOW and MT3DMS (Mass Transport 3-Dimension Multi-Species) to integrate two water supply systems, surface water (polluted drains and canals) and ground water, to identify the contamination process of the groundwater from drains as fresh water is polluted and the contamination level exceeds the standard limits. The study was applied to two cases: the first was a hypothetical simulation and the second was the real case of the Nile Delta Aquifer (NDA). Four different scenarios were simulated to first identify groundwater contamination by total dissolved solids (TDS), and then select the more efficient protection process. The first scenario involved changing polluted drain and canal boundary conditions regarding head and concentration; the second consisted of studying the location of the polluted drain in a low permeability layer or a confined aquifer; the third was based on installing a cut-off wall in the polluted drain sides; and the fourth investigated the use of lining materials for polluted drains. The results reveal that aquifer contamination was decreased by increasing the water head of canals by 50 cm and decreasing the drain head by 50 cm and concentration by 25%, whereby large quantities of groundwater were protected. The percentages of salt repulsion in the hypothetical case were +10.66, +12.89, and +24.99%, while in NDA they were +6.29, +8.71, and +25% respectively compared with the base case. Decreasing the aquifer hydraulic conductivity led to decrease in aquifer contamination, in which the confined aquifer pollution was less than the unconfined aquifers due to the clay cap, which plays a significant role in minimizing the solute transport into the groundwater reservoir, and to reduction of the aquifer salt variation by +19.01% for the hypothetical case. The results indicate that the cut-off wall is effective for contamination management in shallow aquifers (hypothetical case) and the reduction in aquifer salt was +28.49%, whereas it had no effect in the deep aquifer (NDA), where the salt was reduced by just +0.34%. Using the drain lining scenario prevented contamination from the polluted drains and protected the freshwater in the aquifer, so that the aquifer salt mass reductions were +91.02 and +70.13% for the hypothetical case and NDA respectively, indicating that this method is more effective for controlling groundwater contamination. Polluted drains should be located in a low permeability layer to minimize the water degradation. This study represents a new contribution to groundwater protection techniques by changing the boundary conditions, installing a cut-off wall and using linings for polluted drains, and shows the way forward for the future treatment of polluted stream networks.