Source Of Groundwater Contamination Research Articles

Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization.

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.

Biochar Promotes Arsenopyrite Weathering in Simulated Alkaline Soils: Electrochemical Mechanism and Environmental Implications.

Oxidation dissolution of arsenopyrite (FeAsS) is one of the important sources of arsenic contamination in soil and groundwater. Biochar, a commonly used soil amendment and environmental remediation agent, is widespread in ecosystems, where it participates in and influences the redox-active geochemical processes of sulfide minerals associated with arsenic and iron. This study investigated the critical role of biochar on the oxidation process of arsenopyrite in simulated alkaline soil solutions by a combination of electrochemical techniques, immersion tests, and solid characterizations. Polarization curves indicated that the elevated temperature (5-45 °C) and biochar concentration (0-1.2 g·L-1) accelerated arsenopyrite oxidation. This is further confirmed by electrochemical impedance spectroscopy, which showed that biochar substantially reduced the charge transfer resistance in the double layer, resulting in smaller activation energy (Ea = 37.38-29.56 kJ·mol-1) and activation enthalpy (ΔH* = 34.91-27.09 kJ·mol-1). These observations are likely attributed to the abundance of aromatic and quinoid groups in biochar, which could reduce Fe(III) and As(V) as well as adsorb or complex with Fe(III). This hinders the formation of passivation films consisting of iron arsenate and iron (oxyhydr)oxide. Further observation found that the presence of biochar exacerbates acidic drainage and arsenic contamination in areas containing arsenopyrite. This study highlighted the possible negative impact of biochar on soil and water, suggesting that the different physicochemical properties of biochar produced from different feedstock and under different pyrolysis conditions should be taken into account before large-scale applications to prevent potential risks to ecology and agriculture.

Efficient estimation of groundwater contaminant source and hydraulic conductivity by an ILUES framework combining GAN and CNN

In the inverse estimation of groundwater model parameters (e.g., contaminant source parameters and hydraulic conductivity fields), data assimilation is the most commonly used method for solving the inverse problem. However, data assimilation requires iterative calculation, which tends to increase the computational costs. This is particularly problematic when dealing with high-dimensional problems. In this study, we propose a hybrid inversion framework, which combines a generative adversarial network (GAN) and a convolutional neural network (CNN) for groundwater model parameter estimation. The GAN is equipped with gradient penalties (WGAN-GP) to achieve fast characterization of high-dimensional aquifer structures with low-dimensional vectors (200 dimensions). Meanwhile, this study proposes an enhanced convolutional neural network (OANW), which introduces two-layer weights near the contamination source to achieve an effective surrogate of the process-based groundwater model. Moreover, an iterative local update ensemble smoother (ILUES) is applied for simultaneous identification of the contaminant source parameters and hydraulic conductivity fields in the inversion stage. The results demonstrate that by integrating WGAN-GP and OANW, the ILUES framework accurately characterizes the aquifer parameters and significantly improves the efficiency of the computational process.

Application of the RAPS Method for Determining the Dependence of Nitrate Concentration in Groundwater on the Amount of Precipitation

Protecting groundwater from contamination is today’s most current environmental protection topic. What can man do in his environment to reduce the harmful impact of contamination on the environment, and thus the immediate effect on groundwater? Agricultural production is an ongoing source of groundwater contamination due to the increasingly frequent use of nitrates in fertilizers, which are washed out from the soil into groundwater due to precipitation. This paper investigates three wellfields in the north of the Republic of Croatia near the town of Varaždin. With the application of the RAPS method, the dependence of nitrate concentration in groundwater on the amount of precipitation was established. The analysis results show the connection of the observed parameters, especially in the upper aquifer layer. In this layer, the coefficients of correlation are greater than 0.80 at all locations, which shows a strong positive connection between the parameters. In the lower aquifer, the values of the coefficients of correlation are lower, and the results mostly indicate a weak correlation. The obtained results will serve as a starting point for future studies, which will aim to precisely determine the factors that influence groundwater quality in the observed area.

Spatial-temporal Variation in Groundwater Quality and the Contamination Source Analysis in Shihezi-Changji Area

To analyze spatial-temporal variation in groundwater quality and contamination sources in the Shihezi-Changji area in Xinjiang, a Bayesian water quality evaluation model based on Shannon entropy, Spearman rank correlation coefficient, principal component analysis, and an absolute principal component scores-multiple linear regression receptor model (APCS-MLR) were comprehensively used in this study. Groundwater samples in 23 in-situ wells were collected from 2016 to 2021 for quality analysis. The results showed that ① groundwater quality was generally good, with most samples having a phreatic water quality of Class Ⅰ and Class Ⅱ and most confined groundwater quality being of Class Ⅰ. ② Temporally, 2016 and 2017 were the key time nodes of water quality variation in phreatic water and confined groundwater, respectively. Class Ⅳ and Class Ⅴ groundwater was observed only before the key time nodes, whereas after those time nodes the groundwater quality fluctuated within Class Ⅰ to Class Ⅲ. ③ Spatially, the order of phreatic water quality in descending order was Shihezi City, Hutubi county, Manas county, and Changji City, whereas that of confined groundwater quality was:Shihezi City and Changji City, Hutubi county, and Manas county. ④ The spatial-temporal variations in groundwater quality and that in major related indices were basically similar and highly heterogeneous. ⑤ Phreatic water quality was mainly affected by leaching (67.30%), leaching-migration (10.89%), and agricultural-domestic pollution (9.44%); by contrast, unconfined groundwater quality was mainly affected by leaching-enrichment (52.08%), agricultural-domestic pollution (16.06%), and ion exchange under an alkaline environment (12.64%). Although groundwater quality was improved over the years, more attention should be paid to groundwater salinization in the 149 Regiment in northern Manas County.

Groundwater contamination source identification based on Sobol sequences-based sparrow search algorithm with a BiLSTM surrogate model.

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 R2 of the three with 0.9910 and the lowest RMSE with 3.7732g/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.

Groundwater Pollution Impact on Food Security

Global food security challenges have been burdened by a rapidly expanding population and its attendant food demands. Safer and higher-quality agriculture is one of the most essential solutions for addressing the growing problem. In agriculture that is safer, the quality of irrigation from a safer water source will boost food security. Groundwater is one of the most widely utilized water sources for agriculture. Safeguarding groundwater against contamination and preserving water resources is a rising global concern. Herein, previous literature studies were analyzed to determine the groundwater potential for food production of the various continents around the globe, as well as the various types of groundwater contamination, the sources of groundwater contamination, and the best methods for combating groundwater contamination in order to guarantee safe irrigation for agriculture and thus achieve food security. Consequently, the natural and anthropogenic activities that degrade the quality of the groundwater and transform it into contaminated water from harmful organisms, residues of organic and inorganic soluble and non-soluble salts of the groundwater from chemical, leachate from landfills, sewage systems, and biological contamination, are the major issues in safer agriculture, causing a number of problems in the growth of agricultural crops and leading to a negative impact on food production as well as on the health of the population. Proper identification of different sources of contamination and proper methods to prevent contamination from reaching groundwater, as well as governmental and institutional frameworks to combat contamination and treatment methods to treat contaminated groundwater, will contribute to the future achievement of food security by ensuring a safer irrigation method and agriculture.

The Influence Of Rock Mineralogy On Groundwater In The Kampungbaru Formation,Coal Mining Area, Anggana And Surrounding Area, District. Kutai Kartanegara

Coal mining activities using the open pit mining system method will have impacts, both positive and negative impacts, positive impacts will cause changes in improving socio-economic life, while negative impacts will affect changes in hydrogeological conditions and environmental changes such as increasing soil/rock acidity so that it will cause water contaminated. This condition has an impact on decreasing the quality and quantity of groundwater as a source of clean water. The purpose of this study was to determine the hydrogeological conditions around the study site, and the influence of rock mineralogy as a potential source of groundwater contamination, especially in the Kampungbaru Formation. The methods used to obtain data such as; pumping test, rock mineralogy analysis (knowing the characteristics) by conducting studies; petrography, microscopic analysis of ore, XRD analysis, XRF analysis and analysis of the potential of acid mine water, while knowing the chemical characterization of water by analyzing the concentration of anion cations, and classifying water classes. Knowing the characterization of rock mineralogy and water chemistry, the level/value of potential groundwater contamination in the study area will be known. The hope of this research is to know and understand the characteristics of mineralogy of rocks and groundwater chemistry, as well as hydrogeological conditions in Kampungbaru Formation, so that groundwater contamination can be known. Specific benefits of the research, which is to explore more detail hydrogeology of the mine and surrounding areas, to understand the characteristics of rock mineralogy and the characteristics of groundwater chemistry, and to know the source of groundwater contamination of the research area. Practical benefits can provide information to the public and government about the potential of good quality groundwater around the research area and know the groundwater location of good quality. The results of hydrogeological studies, study of mineralogy characterization and groundwater chemistry will be obtained conclusion such as: 1) Characteristics of aquifer in Kampungbaru Formation included in the category of productive aquifers. 2) Mineralogy of rocks affect the chemical concentration .. 3) Kampungbaru Formation has the content of metal mineral elements and water chemical concentrations that contain many sulphide compounds as acid mine formers. Stratigraphy of the study area is included in the Kampungbaru Formation and is part of the Kutai Basin (Kutai Basin). Lithology The Kampungbaru Formation consists of a rocky outcrop with sandstone with coal insertions and sandstone units.
 
 Keywords: Hydrogeology, characteristics of mineralogy and groundwater chemistry, contamination

Sensitivity-dependent dynamic searching approach coupling multi-intelligent surrogates in homotopy mechanism for groundwater DNAPL-source inversion

Equifinality and premature convergence can result in considerable errors when simultaneously characterizing groundwater contamination sources and estimating contaminant transport parameters. To resolve this problem, we design a sensitivity-dependent progressive optimization system embedding ensemble-learning technique. To avoid repetitive CPU-demanding model evaluations in Sobol’ global sensitivity analysis and swarm intelligence optimization inverse modeling, Kriging, support vector regression (SVR), kernel extreme learning machine (KELM), and deep convolutional neural network (DCNN) are compared and ensembled to build an accurate surrogate of the numerical model. In addition, the sensitivities of different source characteristics and contaminant transport parameters are set as important indicators to adjust the displacement vectors of the swarm in each iteration during the optimization process to achieve a balanced identification of sensitivity-varied elements. Moreover, a homotopy-based progressive searching mechanism approach to the global optimum in large areas is developed, with the aim of preventing premature convergence for multimodal search problems. The results indicate that the ensemble learning model efficiently captures the complex input-output relationship of the numerical model with an increased determination coefficient (R2 = 0.9988), while the mean relative error is limited to 0.9314%. Although the contribution of source characteristics and contaminant transport parameters to the spatial-temporal distribution of contaminants vary dramatically, the combined application of sensitivity analysis, homotopy theory, and swarm intelligence optimization provides a more stable and accurate estimation of all the elements. The mean relative error of the identification results significantly reduced from 7.2184% to 3.2718%, whereas the maximum relative error is limited to 9.9501%.

Analysis of Heavy Metal Sources in Groundwater and Assessment of Health Risks: An Example from the Southwest Sub-basin of the Shiqi River

In order to explore the sources of pollution and health risk profile of heavy metal elements in groundwater, 41 sets of representative groundwater samples from the southwest sub-basin of the Shiqi River were examined for 10 heavy metal elements (As, Cr, Cd, Al, Cu, Zn, Ni, Co, Mn, and Hg), and correlation analysis and principal component analysis were used to resolve the possible sources of heavy metal contamination in groundwater in the study area. The concentration characteristics and health risk levels of the 10 heavy metals were assessed using the single-factor contamination index (Pi), the Nemerow comprehensive contamination index (PN), and the health risk model. The results showed that:① the average values of heavy metal elements of the groundwater in the study area all met the limit of the class Ⅲ water standard in the quality standard for groundwater (GB/T 14848-2017); only the maximum value of Al was exceeded, followed by a large variation in the concentrations of Al, Mn, and Cr. The heavy metal element with the largest average contribution was Al (65.74%). ② The results of the single-factor contamination index evaluation showed that only the heavy metal element Al exceeded the cleaning level, and the results of the Nemerow comprehensive contamination index evaluation showed that the study area was basically at low pollution levels, and the quality of groundwater was good. ③ The results of the multivariate statistical analysis showed that Zn, Co, and Mn were from mixed sources consisting of geological formation and domestic waste; Al, As, and Cu were from agricultural sources; Cd, Cr, and Ni were from industrial sources; and Hg came from long-range atmospheric transport. ④ The health risk values for all heavy metals in the study area were within acceptable limits, with higher health risk values for children than for adults from the drinking water route, lower health risk values than in adults from the dermal route, and higher health risk values for heavy metals from the drinking water route than those from the dermal route, indicating that the drinking water route was the main route of exposure to heavy metals.

Impacts of Contaminants on Groundwater Quality in Rukpokwu, Delta Region of Nigeria

This study investigated the characterization of borehole sediment and the impact of contaminants on groundwater quality in the Rukpokwu Community in the delta of Nigeria. The investigation supported by spatial data supported by GIS techniques provides a data baseline for the monitoring of organo-contaminants in groundwater quality in Rukpokwu. This low pH of the groundwater and irritation of the throat and mouth became a concern to the residents. This study is necessary because of the limited data on the level of organic contamination in groundwater in this section of the delta of Niger in Nigeria. So, there was a need to track the source of contamination in this part of the delta of Niger in Nigeria. These previous studies have suggested a highly permeable open aquifer in similar deltaic terrain. Ten (10) samples of borehole sediment and water collected at 30 m depth were being analyzed for textural and physicochemical characteristics. The parameters analyzed include Iron (Fe), Total Petroleum Hydrocarbon &Total Hydrocarbon Content (i.e., TPH and THC respectively) of borehole sediment in association with groundwater quality. Ten (10) sediment samples collected at a depth of 30m and were being analyzed for mean, sorting, skewness and kurtosis, using standard laboratory procedures. Coarse silt, poorly sorted and negatively skewed were the characteristics of the borehole silt. The Kurtosis suggested the leptokurtic and platykurtic nature of the sediment. The TPH and THC range from 0.58 to 0.85 mg/L and 1.26 to 2.84 mg/L respectively. The Fe and pH range in the sediment were 0.85 to 2.46 mg/L and 4.43 to 6.02 respectively. However, Fe in the groundwater ranges from 0.12 to 0.21 mg/L. The results were above the World Health Organization's acceptable contaminant level. Based on the piece of evidence provided by spatial data mapping and textural parameters, the characteristics of the borehole sediment significantly controlled the ingress of contaminants into the groundwater. The source of groundwater contamination is the abnormal concentrations of organic contaminants. These results significantly impact human health in the food chain. This study provides information to the environmental agency for a clean-up process.

Groundwater Quality, Health Risk Assessment, and Source Distribution of Heavy Metals Contamination around Chromite Mines: Application of GIS, Sustainable Groundwater Management, Geostatistics, PCAMLR, and PMF Receptor Model.

Groundwater contamination by heavy metals (HMs) released by weathering and mineral dissolution of granite, gneisses, ultramafic, and basaltic rock composition causes human health concerns worldwide. This paper evaluated the heavy metals (HMs) concentrations and physicochemical variables of groundwater around enriched chromite mines of Malakand, Pakistan, with particular emphasis on water quality, hydro-geochemistry, spatial distribution, geochemical speciation, and human health impacts. To better understand the groundwater hydrogeochemical profile and HMs enrichment, groundwater samples were collected from the mining region (n = 35), non-mining region (n = 20), and chromite mines water (n = 5) and then analyzed using ICPMS (Agilent 7500 ICPMS). The ranges of concentrations in the mining, non-mining, and chromite mines water were 0.02-4.5, 0.02-2.3, and 5.8-6.0 mg/L for CR, 0.4-3.8, 0.05-3.6, and 3.2-5.8 mg/L for Ni, and 0.05-0.8, 0.05-0.8, and 0.6-1.2 mg/L for Mn. Geochemical speciation of groundwater variables such as OH-, H+, Cr+2, Cr+3, Cr+6, Ni+2, Mn+2, and Mn+3 was assessed by atomic fluorescence spectrometry (AFS). Geochemical speciation determined the mobilization, reactivity, and toxicity of HMs in complex groundwater systems. Groundwater facies showed 45% CaHCO3, 30% NaHCO3, 23.4% NaCl, and 1.6% Ca-Mg-Cl water types. The noncarcinogenic and carcinogenic risk of HMs outlined via hazard quotient (HQ) and total hazard indices (THI) showed the following order: Ni > Cr > Mn. Thus, the HHRA model suggested that children are more vulnerable to HMs toxicity than adults. Hierarchical agglomerative cluster analysis (HACA) showed three distinct clusters, namely the least, moderately, and severely polluted clusters, which determined the severity of HMs contamination to be 66.67% overall. The PCAMLR and PMF receptor model suggested geogenic (minerals prospects), anthropogenic (industrial waste and chromite mining practices), and mixed (geogenic and anthropogenic) sources for groundwater contamination. The mineral phases of groundwater suggested saturation and undersaturation. Nemerow's pollution index (NPI) values determined the unsuitability of groundwater for domestic purposes. The EC, turbidity, PO4-3, Na+, Mg+2, Ca+2, Cr, Ni, and Mn exceeded the guidelines suggested by the World Health Organization (WHO). The HMs contamination and carcinogenic and non-carcinogenic health impacts of HMs showed that the groundwater is extremely unfit for drinking, agriculture, and domestic demands. Therefore, groundwater wells around the mining region need remedial measures. Thus, to overcome the enrichment of HMs in groundwater sources, sustainable management plans are needed to reduce health risks and ensure health safety.

Joint Estimation of Adsorptive Contaminant Source and Hydraulic Conductivity Using an Iterative Local Updating Ensemble Smoother with Geometric Inflation Selection

The joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for reactive contaminant transport models in heterogeneous subsurface media. The accurate determination of the sorption parameters of such contaminants is also a key prerequisite for estimating the parameters of the groundwater system. In this study, to investigate the impact of the sorption parameter field on the accuracy of hydraulic conductivity and source characteristics estimation, numerical experiments were conducted in a synthetic aquifer considering the contaminant sorption process in groundwater models with varying sorption parameter settings. Iterative local updating ensemble smoother with geometric inflation selection (ILUES-GEO) was employed to assimilate hydraulic head and contaminant concentration data to jointly estimate the contaminant source information and hydraulic conductivity in a heterogeneous aquifer. The results indicated that the ILUES-GEO successfully recovers contaminant source information simultaneously with hydraulic conductivity, and its performance improves as more accurate sorption parameters are introduced. Furthermore, the influence of the ILUES algorithm parameters and ensemble size is investigated to improve the estimation accuracy. Additionally, the characterization of contaminant sources and hydraulic conductivity fields is influenced by the number and locations of measurements. This study can help to understand the significance of sorption parameter setting for the joint estimation of reactive contaminant source and hydraulic parameters.

Mass-flux Reduction and Mass Removal Relationships (MFR-MR) of Pool-dominated Multicomponent DNAPLs in Heterogeneous Geological System

<p>Contamination of groundwater by chlorinated solvents such as trichloroethylene (TCE, tetrachloroethylene (PCE), and 1,1-dichloroethylene (1,1DCE) is a widespread problem that can seriously threaten human health and the environment. These compounds are often present as dense nonaqueous phase liquid (DNAPL) mixtures in the subsurface that serve as a long-term groundwater contamination sources. The objective of this study is to test the effectiveness of different in-situ flushing agents (SDS, Tween 80, MCD, and water) for the recovery of DNAPL mixtures (TCE, PCE, 1,1DCE) entrapped in heterogeneous aquifer media. A series of batch tests were first performed to assess the multiphase behavior of water-DNAPL mixture-reagent system. Intermediate scale tank experiments were then performed to compare the performance of the reagent solutions. The flushing breakthrough curves were also used to investigate the impact of solubilization agents on mass-flux reduction/mass-removal behavior relationships. Performance of agents on DNAPL mixture source zone remediation was found to be in the following order: Tween 80>SDS>MCD>>Water. Individual DNAPL constituents exhibited transient effective solubilities that depend on the reagent contents and DNAPL compositions. Although high fractions of DNAPL source zones in heterogeneous porous media were removed through chemical flushing (SDS, Tween 80, MCD, and water), the heterogeneous system exhibited an extended multi-step concentration behavior. The results emphasized that in the early stage, some portion of the organic liquid is hydraulically accessible (matrix) whereas the later stage of mass removal was controlled by the more poorly accessible mass (pool) associated with higher-saturation zones. Our results also showed that the distribution and the emplacement of organic liquid, flow-field heterogeneity, sorption and desorption, and flushing solution all influenced the DNAPL mixture removal. The results from this study underline the need for understanding the multiphase behaviour of a system, particularly when DNAPL mixtures are present, prior to field-scale remediation implementation.</p>

Assessment of Groundwater Quality Using APCS-MLR Model: A Case Study in the Pilot Promoter Region of Yangtze River Delta Integration Demonstration Zone, China

Groundwater contaminant source identification is an endeavor task in highly developed areas that have been impacted by diverse natural processes and anthropogenic activities. In this study, groundwater samples from 84 wells in the pilot promoter region of the Yangtze River Delta integration demonstration zone in eastern China were collected and then analyzed for 17 groundwater quality parameters. The principal component analysis (PCA) method was utilized to recognize the natural and anthropogenic aspects impacting the groundwater quality; furthermore, the absolute principal component score-multiple linear regression (APCS-MLR) model was employed to quantify the contribution of potential sources to each groundwater quality parameter. The results demonstrated that natural hydro-chemical evolution, agricultural activities, domestic sewage, textile industrial effluent and other industrial activities were responsible for the status of groundwater quality in the study area. Meanwhile, the contribution of these five sources obtained by the APCS-MLR model were ranked as natural hydro-chemical evolution (18.89%) > textile industrial effluent (18.18%) > non-point source pollution from agricultural activities (17.08%) > other industrial activities (15.09%) > domestic sewage (4.19%). It is believed that this contaminant source apportionment result could provide a reliable basis to the local authorities for groundwater pollution management.

Study of Movement of Pollutants in Soils around the Onsite Sanitation Systems - A Case Study of Periurban area in Karnataka, India

Sitting onsite sanitation systems near ground water sources without following any Indian national building organization, guideline is common in the peri urban areas in India. Due to close location of them the contaminants infiltrates from onsite sanitation systems and causes contamination of groundwater sources. Contamination of groundwater sources by pathogenic bacteria and nitrate are two major public health risks due to onsite sanitation systems. Sufficient depths of unsaturated soil and adequate horizontal separation distances between an onsite system and water sources are required to protect public health from aquifer contamination. In the present study the assessment of movement of pollutants in sandy soils around the onsite sanitation systems has been carried out to study the impact of septic tank on nearby borewell groundwater. Soil samples were collected around the septic tank in five directions. In each direction, soil samples were collected at 1 m interval longitudinally up to 2 m from septic tank edge at 0.3 m and 0.5 m depth. The soil samples are analyzed for sulphates, chlorides, phosphates, nitrates. The soil samples for bacterial analysis (E. coli) were collected in both monsoon and non-monsoon seasons upto 10 m from septic tank edge. From analysis of soil samples it can be concluded that concentration of nitrate, chloride, phosphate and sulphates leached from septic tank are not able to effect quality of bore well water located downstream of septic tank. The E. coli movement in the soil is observed upto 5 meters from the edge of the septic tank. The study concludes that the present distance between public borewell and septic tank is sufficient to prevent groundwater contamination.

Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning.

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%.

Characterization and assessment of groundwater from Obafemi Awolowo University campus, southwestern Nigeria

This study deduced groundwater contaminant sources and the processes controlling groundwater chemistry using Principal Component Analysis (PCA), rock source deduction and Water Quality Index (WQI), all with a view to characterize and assess the groundwater. Groundwater sampling was performed at Obafemi Awolowo University (OAU), Ile-Ife, Southwestern Nigeria. A total of 41 groundwater samples were collected in dry season (November) and subjected to physicochemical analyses. Twelve (12) parameters (pH, TDS, electrical conductivity, temperature, Calcium, Magnesium, Sodium and Potassium, Bicarbonate, Chloride, Sulphate and Nitrate) were analysed. PCA analysis highlighted five principle components (PC–I, PC-II, PC-III, PC-IV and PC-V), which explained 82% of the total variance in the hydrogeochemical data. PC-I to PC-V had loading for Ca2+ and SO42−; Na+ and K+; EC and TDS; Mg2+ and HCO3−; and pH and temperature, respectively. The PCA and source rock deductions revealed that the groundwater chemistry is influenced by geogenic effects of weathering and rock-water interaction, and non-geogenic effects of precipitation and organic processes. Based on the WQI assessment, the groundwater quality was adjudged good to excellent.

Identifying groundwater contamination sources based on the hybrid grey wolf gradient algorithm and deep belief neural network

Identifying groundwater contamination sources based on the hybrid grey wolf gradient algorithm and deep belief neural network

A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification

Ensemble Kalman filter (EnKF) and optimization methods are two mainstream methods in groundwater contamination source identification, but most researchers usually only use or improve on one method. However, each method has strengths and weaknesses, and using one method alone may cause inaccuracy of the identification results for complex situations. Therefore, it is necessary to explore the combination of methods. In this paper, for the optimization method, to further enhance the solution precision of the optimization model for groundwater dense non-aqueous phase liquid (DNAPL) contamination source identification (GDCSI), we first constructed an improved butterfly optimization algorithm by introducing the dynamic switching probability mechanism in the previous butterfly optimization algorithm. Next, the EnKF method and optimization method (based on the improved butterfly optimization algorithm) were used respectively for GDCSI, to assess the strengths and weaknesses of the methods. Then, according to the strengths and weaknesses of the EnKF and optimization methods, the two methods were merged to build a more robust and practical combined search method (CSM) for GDCSI, to further enhance the identification accuracy and effectiveness. In addition, we innovatively applied the EnKF method to combine two single deep learning surrogate models: the deep belief neural network surrogate model and the deep residual neural network surrogate model. And a deep learning combined surrogate model was established, which further enhanced the approximation precision and applicability to the groundwater DNAPL contamination multiphase flow numerical simulation model. Finally, the results of CSM were compared with those of two single methods to analyze the former's effectiveness. The results showed that the CSM significantly improved the identification accuracy and effectiveness of GDCSI compared with any single method.