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"Groundwater Pollution."
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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
Temporal and spatial assessment of groundwater contamination with nitrate using nitrate pollution index (NPI), groundwater pollution index (GPI), and GIS (case study: Essaouira basin, Morocco)
Groundwater aquifers in Morocco’s coastal regions are under serious threat as a result of climate change. This study was conducted to evaluate and map the quality of water resources, by evaluating the level of pollution of the groundwater in the Meskala-Ouazzi sub-basin, a coastal area of Essaouira based on the physico-chemical analysis of 58 samples using a geographic information system (GIS) technique, analytical analysis, nitrate pollution index (NPI), and groundwater pollution index (GPI). The diagram piper of the study area is dominated by Cl-Ca-Mg, Cl-Na, HCO3-Ca-Mg, and SO4-Ca types. The concentrations of nitrate ranged from 2 to 175 mg/L. It was discovered that 22% of the groundwater samples had nitrate amounts greater than the World Health Organization’s recommended maximum allowable level of 50 mg/L. The NPI ranged between − 0.9 and 7.8. According to the classification of NPI, 44.8% of the total groundwater samples represent clean water, indicating that the groundwater in the study area is suitable for irrigation. GPI values ranging from 0.6 to 3.7, with an average of 1.7, identifies 37.9% of all groundwater samples as low polluted. The inverse distance weighting (IDW) approach was used to generate a spatial distribution map, which indicates that appropriate groundwater is present in the sub-upstream basin’s part. Overall, the forte concentration in groundwater samples detected in western and central areas showed that the nitrate originated from large amounts of nitrogen fertilizer used by humans in agricultural activities during periods of irrigation. The low tritium (δ3H) content shows that the aquifer recharge is stale water and excessive use of fertilizers leads to groundwater pollution faster over time.
Journal Article
Application of set pair analysis and uncertainty analysis in groundwater pollution assessment and prediction: a case study of a typical molybdenum mining area in central Jilin province, China
This study evaluated groundwater pollution from a molybdenum mining site in Jilin province, China. First, groundwater pollution in the study area was evaluated using set pair analysis (SPA), which addresses the uncertainties of the evaluation factors and the complex nonlinear relationship among them. Second, groundwater pollution after 3 years was predicted. In this process, the influence of parameter uncertainty on simulation results was analyzed using Monte Carlo simulation, and the results of the analysis were explained from the perspective of pollution risk assessment. Monte Carlo simulation requires repeated invocation of a simulation model, which creates a large computational load. To solve this problem while ensuring high simulation accuracy, sensitivity analysis was used to select the more sensitive model parameters as random variables, and establish a surrogate model based on the Kriging method, thus facilitating the Monte Carlo simulation. We found that: (1) according to the SPA evaluation results, areas of extremely serious pollution were distributed downstream of the tailings and spoil bank. The groundwater in the study area was mainly affected by the tailings reservoir leakage and the spoil bank leachate. (2) Based on the uncertainty analysis, the serious pollution risks for observation wells 1, 2, 3, 4, 5, 6 and 7 were 100%, 100%, 64%, 79%, 52%, 100% and 23%, respectively. Groundwater in the southeast and northwest of the study area had a higher risk of pollution. In addition, pollution risk in the area near the spoil bank and tailings was higher than that of other areas. (3) The surrogate model established using the Kriging method not only had high accuracy and could fully approximate the input–output relationships of the simulation model, but also significantly reduced computing load and computation time. The results can provide a scientific basis for the prevention and control of groundwater pollution.
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
Geochemical evaluation and the mechanism controlling groundwater chemistry using chemometric approach and groundwater pollution index (GPI) in the Kishangarh city of Rajasthan, India
This study is primarily focused on delving into the geochemistry of groundwater in the Kishangarh area, located in the Ajmer district of Rajasthan, India. In pursuit of this research goal, the sampling locations were divided into three parts within the Kishangarh region: Badgaon Rural (KSGR), Kishangarh Urban (KSGU), and the Kishangarh RIICO marble industrial area (KSGI). Various analytical methods have been executed to assess the suitability of groundwater for various purposes based on pH, electric conductivity, total dissolved solids, hardness, salinity, major anions, and cations. The ionic trend of anions and cations was found as HCO
3
−
> Cl
−
> SO
4
2−
> NO
3
−
> Br
−
> NO
2
−
> F
−
and Na
+
> Ca
2+
> Mg
2+
> K
+
, respectively. Applying statistical techniques such as principal component analysis (PCA) and Pearson correlation matrix analysis (PCMA) makes it evident that the physicochemical attributes of water sourced from the aquifers in the study area result from a blend of diverse origins. In addition, Gibbs, Piper, Durov, and scatter plots were used to assess groundwater’s geochemical evolution. Piper plot demonstrated the two types of groundwater facies, Na-HCO
3
−
and Na-Cl, implying significant contributions from evaporitic dissolution and silicate weathering. Also, the scatter plots have evaluated the impression of mine acid leachate, evaporitic dissolution, and silicate weathering to upsurge salt formation in the groundwater. The pollution risk evaluation within the study area was conducted using the groundwater pollution index (GPI). This index revealed a prominent concern for pollution, particularly in the northern segment of the study region. As a result, it can be inferred that the fine aeolian sand and silt formations in the northern part are relatively more vulnerable to contamination.
Journal Article