Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach

In this study, nano zero-valent iron-reduced graphene oxide (NZVI-rGO) composites were synthesized to remove 2,4-dichlorophenol (2,4-DCP) as an efficient adsorbent. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) indicated that NZVI particles were successfully loaded and dispersed uniformly on rGO nanosheets. Fourier transform infrared spectroscopy (FTIR) analysis showed that the interaction between NZVI-rGO and 2,4-DCP promoted the adsorption process. A three-level, four-factor Box-Behnken design (BBD) of the response surface methodology (RSM) was used to optimize the influencing factors including NZVI-rGO dosage, 2,4-DCP initial concentration, reaction time and initial pH. A statistically significant, well-fitting quadratic regression model was successfully constructed to predict 2,4-DCP removal rate. The high F value (15.95), very low P value (<0.0001), nonsignificant lack of fit, and appropriate coefficient of determination ( R 2 = 0.941 ) demonstrate a good correlation between the experimental and predicted values of the proposed model. The analyses of variance reveal that NZVI-rGO dosage and reaction time have a positive effect on 2,4-DCP removal, whereas the increase of contaminant concentration and initial pH inhibit the removal, whereas the effect of contaminant concentration and initial pH is in reverse, where the change of NZVI-rGO dosage has the greatest effect. The optimum condition is1.215 g/L of NZVI-rGO dosage, 20.856 mg/L of 2,4-DCP concentration, 4.115 of pH, and 8.157 min of reaction time. It is verified by parallel experiments under the optimum condition, achieving the removal efficiency of100%.

An experimental study on the determination of dispersion coefficient in layered soils

Dispersivity is a measurable parameter in soil porous media that is used for studying the transport of contaminants to groundwater. The value of this parameter depends on various factors, including the kind of porous media (homogeneous or heterogeneous), flow velocity, initial contaminant concentration, travel distance, and sampling method. A physical model with dimensions of 0.10 m in width, 0.80 m in height, and 1.10 m in length was constructed to investigate the effects of these parameters on the dispersivity value. The stratified soil consisted of three 20-cm-thick layers containing fine-grained, medium-grained, and coarse-grained soil. Sodium chloride solutions with electrical conductivity values of 10, 14, and 19 dS/m were used as the contaminants. Flow was forced through the layered heterogeneous soils at three discharge velocities of 17.58, 22.02, and 26.18 × 10−5 m/s. The point and mixed sampling methods were used. The results indicated that the soil dispersivity values in the layered heterogeneous soils and homogeneous soil were influenced by contaminant concentration, flow velocity, and travel distance. Moreover, the dispersivity values obtained by point sampling were lower than those obtained using the mixed sampling method, and the mean dispersivity value in the layered heterogeneous soils was lower than that of the homogeneous soil.

Case study for estimation of the amount of contaminants stored in soil in an industrial area

One of the main sources of contaminants in the soil is industrial activity which has become one of the major environmental problems of the last few decades. The development of geoinformatics as well as the introduction of standards and regulations has led to a decreased risk of soil contamination and the cost-effective optimization of remediation activities. Based on the above, the aim of our study is to demonstrate the geoinformation processing of the remediation performed in an industrial area located in the Great Hungarian Plain, with special regard to the estimation of the amount and spread of the contaminants accumulated in the soil. In order to reveal the lithological and hydrogeological properties of the investigated area and the environmental status of the underground areas, we performed a large number of shallow land drillings (115). During the field sampling, 1000–1500 grams of samples were collected from the drill bit and were processed in an accredited laboratory. Based on the concentration and volume models created it can be concluded that with the estimations performed via modeling, we were able to locate the most critical areas from the standpoint of contamination. It was revealed that the focal point of the contaminants accumulated in the soil was in the central part of the investigated area. Furthermore, the model demonstrated the effect of lithological factors, since contaminants tend to accumulate more heavily in cohesive soils compared to porous rocks. The extent of contaminant concentration in the aquifer increased with decreasing depth; however, after reaching the floor clay the extent of contaminant concentration began to decrease. The lithological layer closest to the surface contained the most contaminants.

Enhanced UV Direct Photolysis And UV/H2O2 For Oxidation of Triclosan And Ibuprofen In Synthetic Effluent: An Experimental Study

This study aimed to evaluate the implementation of an advanced oxidation system based on UV radiation and UV/H2O2 for degradation of TCS and IBU in synthetic effluent. The assays occurred in a 2L reactor, protected from external light and equipped with a UV lamp (λ = 254nm). The effect of contaminant concentration, fractions of chemical species present, and mineralization were evaluated. In the UV/ H2O2 system, different concentrations of H2O2 were studied for oxidation of the contaminants. The kinetic experiments took place between 75 - 270 min of UV irradiation. The results showed > 99% oxidation of TCS in the direct photolysis system at pH 9.4 after 12 min. The degradation of IBU in the UV/H2O2 system, when 10mg L-1 of H2O2 was used, obtained 97.39% oxidation. We obtained k' values of 0.189 min-1 for TCS when its highest oxidation occurred, and k' values of 0.0219 min-1 for IBU. The system was not able to completely mineralize the contaminants, presenting high values of TOC and COD after treatment, thus suggesting the occurrence of phototransformation.

Results of studies of processes of clearing of gaseous contaminants and ventilating the atmosphere of habitable pressurized modules in a space station

The paper presents partial analytical solutions for equations describing variation in trace amounts of carbon dioxide in the atmosphere of habitable spaces within pressurized modules (PM) of a space station. The solutions may find practical application in calculations of concentrations for any contaminants, or for air flow through the purification system. It is shown that in a case where low-toxic contaminants are released into the PM atmosphere, it would be enough to cycle 3 volumes of the PM air through the purification system when the system operates without the breakthrough concentration (without the residual concentration of the contaminant at the outlet from the purification system), in order to achieve the 95% purification of the atmosphere. For highly toxic contaminants this value should be significantly increased depending on the maximum allowable concentration of the substance (47 volumes and more — up to 99.9% purification). The paper also considers variation in the concentration of the contaminant in the atmosphere during intermixing of atmospheres between PMs using intermodular ventilation. As a result, new analytical solutions were obtained for practical calculations which make it possible to determine gaseous contaminant concentration at any point in time and the time of the final equalization of the contaminant concentration in the space station atmosphere. It was determined that the time needed for complete mixing of gaseous contaminants through intermodular ventilation between two PMs does not depend on the initial concentrations of the contaminants (and only depends on the PM volumes and the intermodular ventilation flow rate). Key words: space station, pressurized module atmosphere, carbon dioxide, atmosphere purification, variation in concentration, air flow, atmosphere mixing.

RESULTS OF STUDIES OF THE PROCESSES OF CLEARING OF GASEOUS CONTAMINANTS AND VENTILATING THE ATMOSPHERE OF HABITABLE PRESSURIZED MODULES IN A SPACE STATION

The paper presents partial analytical solutions for equations describing variation in trace amounts of carbon dioxide in the atmosphere of habitable spaces within pressurized modules (PM) of a space station. The solutions may find practical application in calculations of concentrations for any contaminants, or for air flow through the purification system. It is shown that in a case where low-toxic contaminants are released into the PM atmosphere, it would be enough to cycle 3 volumes of the PM air through the purification system when the system operates without the breakthrough concentration (without the residual concentration of the contaminant at the outlet from the purification system), in order to achieve the 95% purification of the atmosphere. For highly toxic contaminants this value should be significantly increased depending on the maximum allowable concentration of the substance (47 volumes and more — up to 99.9% purification). The paper also considers variation in the concentration of the contaminant in the atmosphere during intermixing of atmospheres between PMs using intermodular ventilation. As a result, new analytical solutions were obtained for practical calculations which make it possible to determine gaseous contaminant concentration at any point in time and the time of the final equalization of the contaminant concentration in the space station atmosphere. It was determined that the time needed for complete mixing of gaseous contaminants through intermodular ventilation between two PMs does not depend on the initial concentrations of the contaminants (and only depends on the PM volumes and the intermodular ventilation flow rate). Key words: space station, pressurized module atmosphere, carbon dioxide, atmosphere purification, variation in concentration, air flow, atmosphere mixing.

Simultaneous identification of contaminant source location, initial contaminant concentration, horizontal and vertical hydraulic conductivity in a three-dimensional coastal aquifer via ensemble Kalman filter

&lt;p&gt;In coastal aquifers, we face the problem of salt water intrusion, which creates a complex flow field. Many of these coastal aquifers are also exposed to contaminants from various sources. In addition, in many cases there is no information about the characteristics of the aquifer. Simultaneous identification of the contaminant source and coastal aquifer characteristics can be a challenging issue. Much work has been done to identify the contaminant source, but in the complex velocity field of coastal aquifer, no one has resolved this issue yet. We want to address that in a three-dimensional artificial coastal aquifer.&lt;/p&gt;&lt;p&gt;To achieve this goal, we have developed a method in which the contaminant source can be identified and the characteristics of the aquifer can be estimated by using information obtained from observation wells. First, by assuming the input parameters required to simulate the contaminant transfer to the aquifer, this three-dimensional coastal aquifer that is affected by various phenomena such as seawater intrusion, tides, shore slope, rain, discharge and injection wells, is simulated and the time series of the output parameters including head, salinity and contaminant concentration are estimated. In the next step, with the aim of performing inverse modeling, random values &amp;#8203;&amp;#8203;are added to the time series of outputs obtained at specific points (points belonging to observation wells) in order to rebuilt the initial conditions of the problem to achieve the desired unknowns (contaminant source and aquifer characteristics). The unknowns estimated in this study are the contaminant source location (x, y, z), the initial contaminant concentration, the horizontal and vertical hydraulic conductivity of the aquifer. SEAWAT model in GMS software environment has been used to solve the equations of flow and contaminant transfer and simulate a three-dimensional coastal aquifer. Next, for reverse modeling, one of the Bayesian Filters subset (ensemble Kalman filter) has been used in the Python programming language environment. Also, to reduce the code run time, the neural network model is designed and trained for the SEAWAT model.&lt;/p&gt;&lt;p&gt;This method is able to meet the main purpose of the study, namely estimating the value &amp;#8203;&amp;#8203;of unknown input parameters, including the contaminant source location, the initial contaminant concentration, the horizontal and vertical hydraulic conductivity of the aquifer. In addition, that makes it possible to achieve a three-dimensional numerical model of the coastal aquifer that can be used as a benchmark to examine more accurately the impact of different phenomena simultaneously. In conclusion, we have developed an algorithm which can be used in the world's coastal aquifers to identify the contaminant source and estimate its characteristics.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Key words: coastal aquifer, seawater intrusion, contaminants, groundwater, flow field, parameter estimation, ensemble kalman filter&lt;/p&gt;

Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow

&lt;p&gt;Artificial Neural Networks (ANNs) have been widely applied to model hydrological problems with the increasing availability of data and computing power. ANNs are particularly useful to predict dynamic variables and to learn / discover constitutive relationships between variables. In the hydrology field, a specific example of the relationship takes the form of the governing equations of contaminant transport in porous media flow. Fluid flow in porous media is a spatio-temporal problem and it requires a certain numerical structure to solve. The ANNs, on the other hand, are black-box models that lack interpretability especially in their structure and prediction. Therefore, the discovery of the relationships using ANNs is not apparent. Recently, a distributed spatio-temporal ANN architecture (DISTANA) was proposed. The structure consists of transition kernels that learn the connectivity between one spatial cell and its neighboring cells, and prediction kernels that transform the transition kernels output to predict the quantities of interest at the subsequent time step. Another method, namely the Universal Differential Equation (UDE) for scientific machine learning was also introduced. UDE solves spatio-temporal problems by using a Convolutional Neural Network (CNN) structure to handle the spatial dependency and then approximating the differential operator with an ANN. This differential operator will be solved with Ordinary Differential Equation (ODE) solvers to administer the time dependency. In our work, we combine both methods to design an improved network structure to solve a contaminant transport problem in porous media, governed with the non-linear diffusion-sorption equation. The designed architecture consists of flux kernels and state kernels. Flux kernels are necessary to calculate the connectivity between neighboring cells, and are especially useful for handling different types of boundary conditions (Dirichlet, Neumann, and Cauchy). Furthermore, the state kernels are able to predict both observable states and mass-conserved states (total and dissolved contaminant concentration) separately. Additionally, to discover the constitutive relationship of sorption (i.e. the non-linear retardation factor R), we regularize its training to reflect the known monotonicity of R. As a result, our network is able to approximate R generated with the linear, Freundlich, and Langmuir sorption model, as well as the contaminant concentration with high accuracy.&lt;/p&gt;

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node’s start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.