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"Groundwater Pollution Computer simulation."
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Groundwater vulnerability assessment and mapping using DRASTIC model
This book shows the effectiveness of DRASTIC model in a geographical setting for validation of vulnerable zones and presents the optimization of parameters for the development of precise maps highlighting several zones with varied contamination. Impact of vadose zone has also been assessed by considering every sub-surface layer.
Book
Groundwater vulnerability
The Chernobyl Nuclear Power Plant (NPP) disaster that occurred in Ukraine on April 26, 1986, was one of the most devastating in human history. Using this as a case study, the AGU monograph Groundwater Vulnerability: Chernobyl Nuclear Disaster is devoted to the problem of groundwater vulnerability, where the results of long-term field and modeling investigations of radionuclide transport in soil and groundwater, within the Ukrainian part of the Dnieper River basin (Kyiv region of Ukraine), are discussed. The authors provide a comprehensive review of existing literature on the assessment of groundwater vulnerability and then describe an improved methodology, which is developed based on integration of the methods of hydrogeological zonation and modeling of anomalously fast migration of radioactive contaminants from the land surface toward groundwater. This volume also includes the evaluation of the effect of preferential and episodic flow on transport of radionuclides toward the aquifers and risk assessment of groundwater vulnerability, which can further assist future researchers in developing remediation technologies for improving drinking water quality. Further, this volume sheds light on the consequences of groundwater contamination from nuclear disasters and assists with assessing the risks associated with contamination and developing effective remediation technologies. Volume highlights include discussions of the following:
- Assessment of groundwater vulnerability to contamination from the Chernobyl nuclear disaster
- Novel analytical results of the 25-year investigations of groundwater contamination caused by Chernobyl-born radionuclides
- The wealth of data on different modes of radioactive transport in the atmosphere, water, and soils, and along the food chains
- The hydrogeological and physico-chemical processes and factors in groundwater contaminated zones
- The applicability of commonly used methods of the evaluation of groundwater vulnerability
- A unique method of fluid dynamics that involves an anomalously fast migration of contaminants through zones of preferential flow from the land surface toward groundwater
- Building confidence in the assessment of migration pathways of radionuclides in the biosphere
- Assessment and prediction of the consequences of the nuclear accident, which can shed light on protection from global nuclear accidents
- Analogue information for different nuclear waste disposal and environmental projects around the world
eBook
Groundwater Vulnerability Assessment and Mapping
This volume presents the contemporary issues surrounding groundwater pollution risk assessment and the application of vulnerability and risk assessment maps for the effective protection and management of aquifers. Numerous new and improved approaches to intrinsic and specific vulnerability assessment (modified DRASTIC, GOD, VULK, VURAAS) are described, some coupled with geophysical and hydrological surveys and hydrodynamic and transport modelling. Widespread use is made of GIS format.
eBook
Groundwater vulnerability assessment and mapping : selected papers from the Groundwater Vulnerability Assessment and Mapping International Conference : Ustroń, Poland, 2004
This volume presents the contemporary issues surrounding groundwater pollution risk assessment and the application of vulnerability and risk assessment maps for the effective protection and management of aquifers. Numerous new and improved approaches to intrinsic and specific vulnerability assessment (modified DRASTIC, GOD, VULK, VURAAS) are described, some coupled with geophysical and hydrological surveys and hydrodynamic and transport modelling. Widespread use is made of GIS format.
eBook
Risk of groundwater contamination widely underestimated because of fast flow into aquifers
Groundwater pollution threatens human and ecosystem health in many regions around the globe. Fast flow to the groundwater through focused recharge is known to transmit short-lived pollutants into carbonate aquifers, endangering the quality of groundwaters where one quarter of the world’s population lives. However, the large-scale impact of such focused recharge on groundwater quality remains poorly understood. Here, we apply a continental-scale model to quantify the risk of groundwater contamination by degradable pollutants through focused recharge in the carbonate rock regions of Europe, North Africa, and the Middle East. We show that focused recharge is the primary reason for widespread rapid transport of contaminants to the groundwater. Where it occurs, the concentration of pollutants in groundwater recharge that have not yet degraded increases from <1% to around 20 to 50% of their concentrations during infiltration. Assuming realistic application rates, our simulations show that degradable pollutants like glyphosate can exceed their permissible concentrations by 3 to 19 times when reaching the groundwater. Our results are supported by independent estimates of young water fractions at 78 carbonate rock springs over Europe and a dataset of observed glyphosate concentrations in the groundwater. They imply that in times of continuing and increasing industrial and agricultural productivity, focused recharge may result in an underestimated and widespread risk to usable groundwater volumes.
Journal Article
Groundwater Pollution Source Identification via an Integrated Surrogate Model and Multiobjective Heuristic Optimization Algorithms
Simulation‐optimization methods are commonly used in groundwater pollution source identification. Traditional simulation‐optimization methods require multiple calls to the numerical model, which leads to a considerable computational burden. Surrogate models based on machine learning can replace numerical models while maintaining accuracy. Previous studies have focused on the fitting accuracy of surrogate models, this study emphasizes the importance of the precision of surrogate models for the inversion process. We use the analytic hierarchy process to integrate ConvLSTM, convolutional neural network, and BiLSTM to improve the precision of the surrogate model. GMS is used to construct numerical models of two hypothetical cases and a practical case. Compared with the best results of the single deep learning methods, the integrated surrogate model improves the precision of the solution of the two hypothetical cases by 90% and 26%, respectively. In addition, the accuracy of the pollution source information obtained by incorporating the integrated surrogate model into the optimization model is higher than that obtained by ConvLSTM as the surrogate model. The inversion results of 7 metaheuristic optimization algorithms are compared through two hypothetical cases, and then the optimization algorithm with higher accuracy is applied to the solution of the practical case. To obtain more accurate results, we reobtain a batch of training data by resampling and training the integrated surrogate model. The results show that constructing an integrated surrogate model and selecting an optimization algorithm can improve the solution accuracy of the simulation‐optimization method. This research provides a new perspective for the construction of simulation‐optimization methods.
Journal Article
Groundwater sustainability and groundwater/surface-water interaction in arid Dunhuang Basin, northwest China
The Dunhuang Basin, a typical inland basin in northwestern China, suffers a net loss of groundwater and the occasional disappearance of the Crescent Lake. Within this region, the groundwater/surface-water interactions are important for the sustainability of the groundwater resources. A three-dimensional transient groundwater flow model was established and calibrated using MODFLOW 2000, which was used to predict changes to these interactions once a water diversion project is completed. The simulated results indicate that introducing water from outside of the basin into the Shule and Danghe rivers could reverse the negative groundwater balance in the Basin. River-water/groundwater interactions control the groundwater hydrology, where river leakage to the groundwater in the Basin will increase from 3,114 × 104 m3/year in 2017 to 11,875 × 104 m3/year in 2021, and to 17,039 × 104 m3/year in 2036. In comparison, groundwater discharge to the rivers will decrease from 3277 × 104 m3/year in 2017 to 1857 × 104 m3/year in 2021, and to 510 × 104 m3/year by 2036; thus, the hydrology will switch from groundwater discharge to groundwater recharge after implementing the water diversion project. The simulation indicates that the increased net river infiltration due to the water diversion project will raise the water table and then effectively increasing the water level of the Crescent Lake, as the lake level is contiguous with the water table. However, the regional phreatic evaporation will be enhanced, which may intensify soil salinization in the Dunhuang Basin. These results can guide the water allocation scheme for the water diversion project to alleviate groundwater depletion and mitigate geo-environmental problem.
Journal Article
Simultaneous identification of groundwater pollution source and important hydrogeological parameters considering the noise uncertainty of observational data
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.
Journal Article
Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm
The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation–optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution.
Journal Article