Remediation Of Groundwater Pollution Research Articles

Optimization of a Groundwater Pollution Monitoring Well Network Using a Backpropagation Neural Network-Based Model

Selecting representative groundwater monitoring wells in polluted areas is crucial to comprehensively assess groundwater pollution, thereby ensuring effective groundwater remediation. However, numerous factors can affect the effectiveness of groundwater monitoring well network optimizations. A local sensitivity analysis method was used in this study to analyze the hydrogeological parameters of a simulation groundwater solute transport model. The results showed a strong effect of longitudinal dispersion and transverse dispersion on the output results of the simulation model, and a good fit between the backpropagation neural network (BPNN)-based alternative model’s results and those obtained using the solute transport simulation model, accurately reflecting the input and output relationship of the simulation model. The optimized groundwater monitoring layout scheme consisted of four groundwater monitoring wells, namely no. 7, no. 16, no. 23, and no. 24. These wells resulted in a groundwater fluoride pollution rate of 98.44%, which was substantially higher than that obtained using the random layout scheme. In addition, statistical analysis of the fluoride groundwater pollution results obtained using the Monte Carlo random simulation highlighted continuous and high groundwater fluoride levels in the second and third pollution sources and their downstream groundwater. Therefore, more attention should be devoted to these sources to ensure the effective remediation of groundwater pollution in the study area.

Groundwater protection around Makkah sewage treatment plant using hydrological and transport models

Makkah is depicted environmentally by scarcity of rainfall, elevated temperatures, and increasing rates of evaporation. These ambience factors have driven the city to a reduction in the water balance. The main water resources in Makkah are renewable groundwater aquifers. Despite numerous studies concerned in the assessment of groundwater quality in Makkah, this paper presents the groundwater pollution remediation near Makkah sewage treatment plant. The methodology of this study depends on the simulation of four scenarios of groundwater remediation under the treated effluent of Makkah sewage plant. These scenarios are injection wells of clean water (with and without surface recharge of the effluent Makkah sewage plant), discharge wells for the contaminated aquifer, and slurry wall to constrict the spread of aquifer pollutants. The simulation is performed using the hydrological model (MODFLOW) and transport model in groundwater systems (MT3D). The results revealed that the case of discharge wells was the best one for cleaning the aquifer. In contrast, the worst case was the injection wells with surface recharge causing the pollution spread over the whole area. The case of using the slurry wall proved an efficient approach for controlling the pollution spread through the path of Makkah sewage plant effluent flow.

Spatiotemporal distribution patterns of iron, manganese, and arsenic within the river infiltration zone and the potential geochemical activity at key interfaces

The river infiltration zone is a mixed area of surface water and groundwater under the riverbed and along the riverbank, which is greatly affected by river infiltration. Currently, there is a lack of understanding regarding the migration and transformation processes of Fe, Mn, and As, along with their correlation with organic carbon in the river infiltration zone affected by riverbed scouring and siltation processes. In this study, based on techniques such as pore water sampling and the Tessier sequential extraction, the spatiotemporal variations in Fe, Mn, and As concentrations and possible geochemical processes at the riverbed sediment–water interface and along the river infiltration flow path were revealed. The results indicated that the potential geochemical activity of Fe, Mn, and As and the distribution of organic carbon in the riverbed sediment responded to riverbed scouring–siltation processes. Compared to the high water level period, the potential geochemical activity of Fe, Mn, As, and organic carbon in the riverbed sediment generally exhibited an upward trend during the low water level period, indicating more intense Fe and Mn biogeochemical reactions. During river infiltration, the labile organic carbon (LOC) was preferentially utilized, and with increasing depth, the sedimentary organic carbon (SOC) continuously transformed into LOC and dissolved organic carbon (DOC). Affected by riverbed scouring processes, the transition time from oxidizing to reducing conditions in the pore water increased, and the Fe, Mn, and As reduction zones moved deeper and farther from the riverbank, resulting in a significant expansion of the groundwater pollution area. The reductive dissolution of iron and manganese oxides/hydroxides and the oxidation of organic matter–bound Fe, Mn, and As were the main biogeochemical processes contributing to the release of Fe, Mn, and As in the river infiltration zone. These research results have crucial theoretical significance and practical application value for the sustainable utilization of groundwater resources and the remediation of groundwater pollution.

Early Warning of Regional Groundwater Pollution： A Case Study for Plain Area of Barkol-Yiwu Basin

Groundwater pollution early warning is an effective means for regional groundwater pollution prevention. The groundwater pollution early warning model coupled with the current situation of groundwater quality, groundwater quality variation trend, and groundwater pollution risk were applied to the plain area of Barkol-Yiwu Basin, and the regional scale groundwater pollution early warning was realized by combining the early warning of groundwater quality status and trend. The TOPSIS method based on comprehensive weight was used to evaluate the current situation of groundwater quality. The variation trend of groundwater quality was analyzed by calculating the trend interpolation results of 18 in-situ groundwater quality monitoring wells. The groundwater vulnerability map, groundwater pollution load map, and groundwater function value map were superimposed using the superposition index method to evaluate groundwater pollution risk. The results showed that the groundwater quality was good and relatively good, and the poor groundwater quality in some areas was mainly affected by the shallow groundwater depth and the large porosity of the vadose zone. Groundwater quality was stable from 2011 to 2022, mainly due to the leakage of wastewater generated by industries and agriculture into groundwater, resulting in the deterioration of groundwater quality in some areas. Groundwater pollution risk was generally low, and the dual effects of high vulnerability and high pollution load of groundwater led to local areas with high pollution risk. The early warning level of groundwater pollution was generally low, and the heavy and highly heavy warning areas accounted for 16.4% and 17.5% of the study area, respectively, mainly distributed in Xiamaya Township of Yiwu County and northern Santanghu Town, Dahongliuxia Township, and Dahe Town of Barkol County. The quaternary sediments exposed were mainly sandy pebbles, with developed pores and strong water permeability. The interception and adsorption capacity of pollutants were weak. Pollutants produced by industries, agriculture, and life easily leaked into groundwater aquifers, resulting in poor groundwater quality and high risk of groundwater pollution, which ultimately led to a high early warning level of groundwater pollution in some areas. The research on early warning of groundwater pollution provided an important theoretical basis for the development of groundwater pollution remediation.

A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution

4D microvelocimetry reveals multiphase flow field perturbations in porous media

Many environmental and industrial processes depend on how fluids displace each other in porous materials. However, the flow dynamics that govern this process are still poorly understood, hampered by the lack of methods to measure flows in optically opaque, microscopic geometries. We introduce a 4D microvelocimetry method based on high-resolution X-ray computed tomography with fast imaging rates (up to 4 Hz). We use this to measure flow fields during unsteady-state drainage, injecting a viscous fluid into rock and filter samples. This provides experimental insight into the nonequilibrium energy dynamics of this process. We show that fluid displacements convert surface energy into kinetic energy. The latter corresponds to velocity perturbations in the pore-scale flow field behind the invading fluid front, reaching local velocities more than 40 times faster than the constant pump rate. The characteristic length scale of these perturbations exceeds the characteristic pore size by more than an order of magnitude. These flow field observations suggest that nonlocal dynamic effects may be long-ranged even at low capillary numbers, impacting the local viscous-capillary force balance and the representative elementary volume. Furthermore, the velocity perturbations can enhance unsaturated dispersive mixing and colloid transport and yet, are not accounted for in current models. Overall, this work shows that 4D X-ray velocimetry opens the way to solve long-standing fundamental questions regarding flow and transport in porous materials, underlying models of, e.g., groundwater pollution remediation and subsurface storage of CO2 and hydrogen.

Remediation of groundwater pollution using photocatalytic membrane reactors

Recalcitrant pollutants in groundwater and industrial wastewater streams are very dangerous for the environment and human health. The conventional water treatment processes are not capable to degrade/removing them due to their very high chemical stability. The photocatalytic membrane reactors represent a possible route for overcoming the limits present in the conventional ones. In this study, photocatalyst loaded mixed matrix membranes (PMMM) were prepared using non solvent induced phase inversion process and titanium dioxide as a photocatalyst. SEM and EDX analyses, contact angle, and tensile strength measurements were used for characterizing the membranes. Their catalytic activity was assessed using the methyl orange as a recalcitrant pollutant. The photoactivity of the PMM membranes increased with the TiO2 content. The membrane loaded with the 2.5 wt % of titanium dioxide achieved better degradation and retained high degradation efficiency after 4 cycles, indicating the stability of the membrane.

Performance evaluation of surrogate models for simulating multiphase NAPL transport in heterogeneous aquifers

Abstract A better understanding of the distribution of nonaqueous phase liquid (NAPL) plumes is of great importance to groundwater pollution remediation and control. However, the efficiency of surrogate models in simulating the transport is still not well addressed. Selecting a leakage problem as an example, 50 sets of random permeability distributions are generated using the Monte Carlo method, and a numerical model is used to obtain benchmark data of NAPL transport. Four machine learning methods are employed to simulate dense NAPL transport under point leakage sources across spatiotemporal scales. The validation of the models demonstrate that the random forest model can also effectively capture the spatial-temporal distribution of the plume in heterogeneous aquifers, with a maximum mean absolute error and root mean square error smaller than 5.55 × 10−4 and 5.88 × 10−5, respectively. Meanwhile, the multiple phase outcome from the random forest model fits well with the numerical results under the scenarios of linear leakage sources and light NAPL transport. The total time consumed in the computation is reduced by over 150 times after using the surrogate models. The results suggest that surrogate models can provide a promising way to understand NAPL transport in heterogeneous aquifers.

Characteristics of Zinc Adsorption onto Biochars Derived from Different Feedstocks

Human activities such as the discharge of urban sewage, garbage, and industrial waste have seriously affected the quality of groundwater sources for human consumption. The potential for using biochar as a reactive medium in a permeable reactive barrier (PRB) was explored for Zn-contaminated groundwater treatment in this study. Four different types of biochar produced from wood, coconut shell, rice straw, and fruit shell were used. The production temperature of these biochars were 600 °C, 550 °C, 500 °C, and 500 °C, respectively. The samples were coded with the initials of the biochar source and the production temperature as WD600, CS550, RS500, and FS500. The results of various batch adsorption studies show that equilibrium solution pH has a great effect on the maximum adsorption capacity in the pH range of 2–7. The adsorption of Zn on biochars follows the Freundlich model and fits well with the pseudo-second-order model. The fixed-bed column test data were well fitted to the Dose–Response model. The adsorption capacities of WD600, CS550, RS500, and FS500 were 24.91, 15.87, 9.25, and 46.71 mg/g, respectively. The removal rate of FS500 can reach a maximum of 98.87%. FS500 is considered to be a potential reaction medium for treating Zn-contaminated groundwater in a PRB system. This work provides a new option for converting biomass waste into an adsorbent for zinc in wastewater. The results of this study are expected to provide a solid theoretical basis for the further application of biochar in groundwater pollution remediation.

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.

Adsorption of uranium(VI) by natural vermiculite: Isotherms, kinetic, thermodynamic and mechanism studies

Human activities such as mining uranium resources, hydrometallurgy, and nuclear fuel preparation inevitably produce wastewater sludge containing radionuclides, posing a severe threat to the environment around the production site. Natural clay minerals have been widely used in groundwater pollution remediation because of their high cation exchange capacity. Through static batch experiments, the optimal pH range of vermiculite for U(VI) adsorption was 6–8,the maximum adsorption capacity was 1.62 × 10−5 mol g−1. The kinetic adsorption results indicated that the adsorption mode was mainly multilayer non-homogeneous chemisorption. In addition, the adsorption of vermiculite on U(VI) was found to be a heat absorption process according to the thermodynamic model fitting, and the spontaneous reactivity of U(VI) adsorption on vermiculite surface was positively correlated with temperature and negatively correlated with the initial concentration of U(VI). Combined with SEM-EDS and FT-IR results, the adsorption process of vermiculite on U(VI) is mainly an ion exchange and complexation reaction, and U(VI) is removed in the form of ≡ SUOU22+ or ≡ SOUO2OH, etc., by XPS means. The results of this study not only investigated the adsorption behavior and mechanism of natural vermiculite in groundwater contaminated with simulated uranium but also provided theoretical support for its feasibility in remediating uranium-polluted groundwater.

Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter

Accurate groundwater pollution source estimation (GPSE) is the prerequisite for efficiently designing groundwater pollution remediation strategies and pollution risk assessment. However, deterministic point estimation of pollution source characteristics may not completely solve the problem for practical situations. Therefore, an interval estimation, capable of providing more comprehensive and abundant information for decision-makers, should also be considered. In GPSE, the particle filter (PF) method is a good means for the interval estimation. However, some inherent problems in the general PF method may compromise the estimation accuracy. Thus, this study made two improvements to the general PF method as a solution. First, to solve the problem of its weak local search ability when tackling complex problems, this study embedded the optimization method with strong local search ability into the importance sampling process, to strengthen the local search ability, better optimize the particle position, and guide the overall distribution of particles to move towards the true distribution. Secondly, in the resampling process, all particles need to be resampled in each iteration, which may lead to plunging into the local optima. Thus, this study introduced an intelligent resampling process that intelligently adjusted the number of resampling particles in each iteration to avoid plunging into the local optima. Through two improvements on the PF method, an intelligent PF method was innovatively constructed for GPSE, improving the estimation accuracy. Additionally, to alleviate the computational cost caused by the frequent simulation model invocation, we adopted a deep learning method (deep belief neural network method) to build the surrogate model of the simulation model, ensuring high approximation accuracy to the simulation model. Also, we applied the theories and methods mentioned above to two respective scenarios (inorganic and organic groundwater pollution). We simultaneously estimated the pollution source characteristics and simulation model parameters to verify the effectiveness. Finally, we obtained the point estimation, posterior probability distribution, and interval estimation results, capable of providing a more reliable and comprehensive reference for decision-makers.

Prediction and Remediation of Groundwater Pollution in a Dynamic and Complex Hydrologic Environment of an Illegal Waste Dumping Site

The characteristics of groundwater pollution caused by illegal waste dumping and methods for predicting and remediating it are still poorly understood. Serious 1,4-dioxane groundwater pollution—which has multiple sources—has been occurring at an illegal waste dumping site in the Tohoku region of Japan. So far, anti-pollution countermeasures have been taken including the installation of an impermeable wall and the excavation of soils and waste as well as the monitoring of contamination concentrations. The objective of this numerical study was to clarify the possibility of predicting pollutant transport in such dynamic and complex hydrologic environments, and to investigate the characteristics of pollutant transport under both naturally occurring and artificially induced groundwater flow (i.e., pumping for remediation). We first tried to reproduce the changes in 1,4-dioxane concentrations in groundwater observed in monitoring wells using a quasi-3D flow and transport simulation considering the multiple sources and spatiotemporal changes in hydrologic conditions. Consequently, we were able to reproduce the long-term trends of concentration changes in each monitoring well. With the predicted pollutant distribution, we conducted simulations for remediation such as pollutant removal using pumping wells. The results of the prediction and remediation simulations revealed the highly complex nature of 1,4-dioxane transport in the dumping site under both naturally occurring and artificially induced groundwater flows. The present study suggests possibilities for the prediction and remediation of pollution at illegal waste dumping sites, but further extensive studies are encouraged for better prediction and remediation.

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.

Further Discussion on the Influence Radius of a Pumping Well: A Parameter with Little Scientific and Practical Significance That Can Easily Be Misleading

To facilitate understanding and calculation, hydrogeologists have introduced the influence radius. This parameter is now widely used, not only in the theoretical calculation and reasoning of well flow mechanics, but also in guiding production practice, and it has become an essential parameter in hydrogeology. However, the reasonableness of this parameter has always been disputed. This paper discusses the nature of the influence radius and the problems of its practical application based on mathematical reasoning and analogy starting from the Dupuit formula and Thiem formula. It is found that the influence radius is essentially the distance in the time–distance problem in physics; therefore, it is a function of time and velocity and is influenced by hydrogeological conditions and pumping conditions. Additionally, the influence radius is a variable and is essentially different from the hydrogeological parameters reflecting the natural properties of aquifers such as the porosity, specific yield, and hydraulic conductivity. Furthermore, the parameterized influence radius violates the continuity principle of fluids. In reality, there are no infinite horizontal aquifers, and most aquifers are replenished from external sources, which is very different from theory. The stable or seemingly stable groundwater level observed in practice is simply a coincidence that occurs under the influence of various practical factors, which cannot be considered to explain the rationality of applying this parameter in production calculations. Therefore, the influence radius cannot be used to evaluate the sustainable water supply capacity of aquifers, nor can it be used to guide the design of groundwater pollution remediation projects, the division of water source protection areas, and the scheme of riverbank filtration wells. Various ecological and environmental problems caused by groundwater exploitation are related to misleading information from the influence radius theory. Generally, the influence radius does not have scientific or practical significance, but it can easily be misleading, particularly for non-professionals. The influence radius should not be used in the sustainable development and protection of groundwater resources, let alone in theoretical models. From the perspective of regional overall planning, the calculation and evaluation of sustainable development and the utilization of groundwater resources should be investigated in a systematic manner.

Simultaneous identification of groundwater pollution source spatial–temporal characteristics and hydraulic parameters based on deep regularization neural network-hybrid heuristic algorithm

The prerequisites for groundwater pollution remediation include providing accurate identification of hydraulic parameters of the contaminated site and retrospecting pollution source release history from sparse and discrete observation data. However, expensive time running and high nonlinearity of groundwater solute transport simulation model hinder precise and fast identification. To overcome expensive running cost, a deep regularization neural network (DRNN) was introduced to emulate the highly nonlinear simulation model. A hybrid heuristic algorithm, integrating a particle swarm optimization (PSO) and a differential evolution markov chain (DEMC) method was employed to provide reliable identification results of pollution source characteristics and hydraulic parameters. In particular, DEMC conducted a global search and PSO performed a local search. To evaluate transferability for various groundwater pollution cases, the proposed method was utilized to provide an identification for a point source pollution case and a non-point source pollution case. The results indicated that DRNN was capable of emulating input–output relationship of a highly nonlinear and high time expense simulation model of two cases with a R2 of 0.997 and 0.984, respectively, accelerating identification process. Furthermore, when the PSO was combined with DEMC, the optimization was accelerated while escaping from the local optimum, substantially improving the efficiency and accuracy of the identification task.

Source of Groundwater Nitrate in Luanping Basin Based on Multi-environment Media Nitrogen Cycle and Isotopes

The multi-environment media of water, surface soil and vadose zone soil samples were collected in the upstream of Miyun Reservoir, in the Luanping Basin of Chengde City, Hebei Province. The aim was to identify the pollution source, ratio, spatial distribution, migration, and transformation characteristics of nitrogen in groundwater. Hydrogeochemistry, soil total nitrogen, and dissolved nitrate nitrogen of vadose zone soil analysis and a multi isotope tracer technique of δ15 N-NO3 and δ18O-NO3, δ34S-SO4 and δ18O-SO4, δ14 C, combined with land-use type analysis and geostatistics, were used in the study. The results showed that nitrate was the main form of nitrogen in the groundwater of the Luanping Basin. The NO3- concentration of groundwater was significantly correlated with the land-use types of residential land and cultivated land where the nitrate pollution of shallow groundwater was mainly distributed. Of the groundwater samples, 13.79% exceeded the National Standard Ⅲ for Groundwater (GB/T 14848-2017) of NO3- concentration value, while the excess multiple was 1.04-3.86, and 37.93% of the groundwater samples exceeded the World Health Organization NO3- concentration standard value. The excess multiple was 1.08-6.83. The spatial variation of groundwater NO3- concentration, soil total nitrogen and surface soil dissolved nitrate nitrogen of vadose zone was affected by the combination of natural structural factors and anthropogenic factors. The source of groundwater nitrate was mainly from livestock manure and domestic sewage, followed by chemical fertilizer leaching. The nitrogen cycle in the aeration zone-groundwater-dominated nitrogen circulation in the groundwater runoff area of the piedmont basin was nitrification. These findings are highly significant for the prevention and remediation of groundwater pollution when viewing the basin system as an independent unit, and for studying the sources and fate of nitrate pollution in the water environment.

Migration and evolution of dissolved organic matter in landfill leachate-contaminated groundwater plume

Groundwater pollution around landfill sites is becoming a global environmental problem. The hydrochemistry of dissolved organic matter (DOM) in landfill leachate-contaminated groundwater plume has been investigated extensively. The spatial migration and evolution of DOM, the relationship between hydrochemistry and DOM dynamics, and the role of microorganisms in DOM transformation are still largely unclear. In this study, UV–vis, 3D-EEM PARAFAC and principal components analyses were combined to investigate the migration and evolution of DOM. The results show that when the contamination plume gradually diffuses, the molecular weight of DOM in downstream groundwater tends to be high, while the humification degree is low and DOM is mostly derived from microbial metabolism. Compared with humic-like and fulvic-like substances, protein-like substances have a higher migration capability, making them suitable indicators of groundwater pollution. Under low-permeability aquifer medium (K < 10−7 cm/s), the DOM pollution was easy to rebound. An evident positive correlation exists between electrical conductivity and the maximum fluorescence intensity of humic-like and fulvic-like substances. Venn diagram shows that 15.31% (117 of 764) of the species were unique in the upstream and could not be detected in downstream groundwater. In comparison, 43.05% (489 of 1136) of the species were unique in the downstream and 382 were not detected in upstream groundwater. The main microorganisms (such as Polynucleobacter, Comamonadaceae, Desulfobulbus, and Magnetovibrio) play a pivotal role in DOM transformation in the aquifer. This study provides a novel insight into the monitoring, early warning and remediation of groundwater pollution in landfills.

Application of Computational Fluid Dynamics in Groundwater Pollution Remediation

Computational fluid dynamics is widely used in scientific research in various fields. This paper introduces the basic principles of computational fluid dynamics numerical simulation calculation, and summarizes the simulation analysis of pollutant migration and diffusion in the surface underwater-groundwater recharge process, the mainstream fluid numerical model characteristics and the application of groundwater pollution control and restoration. It provides a reference for the selection of numerical models in groundwater pollution survey simulation and control restoration research.

Influence of pH on Cr(VI) reduction by organic reducing substances from sugarcane molasses

The remediation of Cr(VI) is one of the hot issues in groundwater pollution remediation. The influences and effects of pH on Cr(VI) reduction by organic reducing substances in sugarcane molasses were studied under the sterilization and unsterilization conditions by means of batch laboratory-scale experiments. The results showed that pH of reaction solution has a great influence on the reduction of Cr(VI) by sugarcane molasses. When pH ≥ 3.0, the final removal efficiency of Cr(VI) on the condition of sterilization and unsterilization at pH values of 8.0, 5.8, 4.0 and 3.0 was 15.441%, 17.945%, 29.205%, 36.258% and 99.236%, and 98.577%, 99.235% and 99.368%, respectively. When pH < 3.0, the final removal efficiency of Cr(VI) on the condition of sterilization and unsterilization at pH values of 2.5 and 2.0 was 99.631% and 99.895%, and 99.631% and 99.895%, respectively. The main mechanism involves chemical reduction and biological reduction. And biological reduction in the process of Cr(VI) reduction by sugarcane molasses is of the first priority when pH ≥ 3.0; when pH < 3.0, the rate of chemical reduction was higher and it was not the basic existing biological reduction. The relationship between pH and pseudo-first-order rate constant (kobs) on both the sterilization and unsterilization conditions was satisfied with Boltzmann model. There is an inflection point at the pH of about 2.6, which also indicated the influence mechanism of pH on the reduction of Cr(VI) by sugarcane molasses.