Development of a novel method for the removal of diazinon pesticide from aqueous solution and modeling by artificial neural networks (ANN)

Adsorption of diazinon from aqueous solution using metal organic framework and functionalized graphene: Comparison of BBD, ANN models

In the present study, metal organic frameworks (MOFs) and aminated graphitic carbonaceous structure (ACS-RGO) through chemical synthesis prepared by a simple precipitation method and used for diazinon removal. Several techniques such as XRD , FESEM and FTIR were applied for identification of MOF-5 and ACS-RGO. Also, response surface methodology (RSM) was employed in this work to look at the effectiveness of diazinon adsorption. To forecast pesticide removal, we applied artificial neural network (ANN) and Box-Behnken Design (BBD) models. For the ANN model, a sensitivity analysis was also performed. The effect of independent variables like solution pH, various concentrations of diazinon, MOFs and ACS-RGO adsorbent dose and contact time were assessed to find out the optimum conditions. Based on the model prediction, the optimal condition for adsorption ACS-RGO and MOF-5 were determined to be pH 6.6 and 6.6, adsorbent dose of 0.59 and 0.906 g/L, and mixing time of 52.15 and 36.96 min respectively. These conditions resulted in 96.69% and 80.62% diazinon removal using ACS-RGO and MOF-5, respectively. Isotherm studies proved the adsorption of ACS-RGO and MOF-5 following the Langmuir isotherm model for diazinon removal. Diazinon removal followed by the pseudo-second and Pseudo-first order kinetics model provides a better fit for analyzing the kinetic data associated with pesticide adsorption for ACS-RGO and MOF-5, respectively. Based on the obtained results, the predicted values for the efficiency of diazinon removal with the ANN and BBD were similar (R2=0.98). Therefore, two models were able to predict diazinon removal by ACS-RGO and MOF-5.

Synthesis of CoWO4/g-C3N4 Z-scheme heterojunction for the efficient photodegradation of diazinon with the addition of H2O2

Pesticides are on the list of substances that are routinely monitored by agencies and organizations in various natural environments and habitats. Diazinon (DZN) is the active ingredient in more than 20 agricultural pesticides, it causes the most damage and has been prohibited in many countries around the world. The final product CoWO4/g-C3N4 Z-scheme heterojunction was successfully synthesized in this work, where CoWO4 nanoparticles were deposited on the surface of g-C3N4. CoWO4/g-C3N4 structure allowed for the efficient separation of photo-generated electron-hole pairs, with electrons at the CoWO4 CB migrating to the g-C3N4 VB and preserving the electrons at the g-C3N4 CB and holes in the CoWO4 VB. The photodegradation efficiency of DZN using CoWO4/g-C3N4 Z-scheme heterojunction was investigated, as compared with its precursors, such as CoWO4, and g-C3N4. CoWO4/g-C3N4 Z-scheme heterojunction demonstrated the highest degradation capacity for DZN removal. Based on the results, the photocatalysis of the CoWO4/g-C3N4 Z-scheme heterojunction can be recycled for the effective removal of DZN by simple washing after three runs, proving the heterojunction's stability and suggesting CoWO4 as a promising material for the removal of DZN from contaminated water sources.

A review on the applicability of adsorption techniques for remediation of recalcitrant pesticides

Pesticide has revolutionised the agricultural industry by reducing yield losses and by enhancing productivity. But indiscriminate usage of such chemicals can negatively impact human health and ecosystem balance as certain pesticides can be recalcitrant in nature. Out of some of the suggested sustainable techniques to remove the pesticide load from the environment, adsorption is found to be highly efficient and can also be implemented on a large scale. It has been observed that natural adsorption that takes place after the application of the pesticide is not enough to reduce the pesticide load, hence, adsorbents like activated carbon, plant-based adsorbents, agricultural by-products, silica materials, polymeric adsorbents, metal organic framework etc are being experimented upon. It is becoming increasingly important to choose adsorbents which will not leave any secondary pollutant after treatment and the cost of production of such adsorbent should be feasible. In this review paper, it has been established that certain adsorbent like biochar, hydrochar, resin, metal organic framework etc can efficiently remove pesticides namely chlorpyrifos, diazinon, 2,4-Dichlorophenoxyacetic Acid, atrazine, fipronil, imidacloprid etc. The mechanism of adsorption, thermodynamics and kinetic part have been discussed in detail with respect to the pesticide and adsorbent under discussion. The reason behind choosing an adsorbent for the removal of a particular pesticide have also been explained. It is further highly recommended to carry out a cost analysis before implementing an absorbent because inspite of its efficacy, it might not be cost effective to use it for a particular type of pesticide or contaminant.

Process optimization of cypermethrin biodegradation by regression analysis and parametric modeling along with biochemical degradation pathway

Pyrethroid pesticides are of great environmental and health concern with regard to neurotoxicity and ubiquitous occurrence. Here, we reported a new bacterial strain identified as Bacillus cereus AKAD 3-1 that degraded 88.1% of 50 mg/l of cypermethrin in an aqueous medium. The biodegradation of cypermethrin was optimized by CCD (central composite design) and validated by ANN-GA (artificial neural network-genetic algorithm). Both the approaches proved to possess good performance in modeling and optimizing the growth conditions. Results indicated that the process variables have a significant (< 0.0001) impact on cypermethrin biodegradation. Moreover, the predicted CCD model had a "lack of fit p-value" of "0.9975." The optimum CCD and ANN model had an R² value of 0.9703 and 0.9907, indicating that the two models' experimental and predicted values are closely fitted. The isolate successfully converted cypermethrin to CO₂ and phenol without producing any toxic metabolite. Finally, a degradation pathway was proposed with the intermediate compounds identified by GC-MS. The present study highlights an important potential application of strain AKAD 3-1 for the in situ bioremediation of cypermethrin-contaminated environments.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Optimization of extraction conditions and determination of the Chlorpyrifos, Diazinon, and malathion residues in environment samples: Fruit (Apple, Orange, and Tomato)

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CPF, diazinon, and malathion residues in fruit was studed.

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Concentration of studied pesticides is positively correlated to the texture of studied fruits.

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Pesticide residues in studying fruits decreased by an increase in public surveillance.

Organophosphate (OP) insecticide, such as diazinon is found in the environments like water which is still approved for agricultural uses. When diazinon residues enter the human body, it functions as an acetylcholinesterase (AChE) inhibitor. This research aims to measure of chlorpyrifos (CPF), diazinon, and malathion residues in fruit such as apple, orange, and tomato after optimizing extraction conditions. Pesticide residues are measured by gas chromatography (GC) technique. Effective variables on pesticide residues are studied including pesticides kind, sampling station, and fruit kind. Results show that average concentration CPF, diazinon, and malathion residues in orange water are 7.05 ± 0.01 mg L⁻¹, 6.66 ± 0.03 mg L⁻¹, and 12.38 ± 0.02 µgL⁻¹, respectively. The average concentration CPF, diazinon, and malathion residues in apple water are 0.74 ± 0.02 mgL⁻¹, 0.70 ± 0.01 mgL⁻¹, and 1.10 ± 0.01 µgL⁻¹, respectively. The average concentration CPF, diazinon, and malathion residues in tomato water are 0.60 ± 0.02 mgL⁻¹, 0.57 ± 0.02 mgL⁻¹, and 0.89 ± 0.01 µgL⁻¹, respectively. The highest CPF concentration is observed in the orange fruit from station 20. Due to an appropriate storage condition and presenting organic fruits in the supermarket, the least mean concentration of pesticides is obtained in studied fruits from station 2. The manner of washing, peeling, and storage period before consuming fruits lead to decreasing studied pesticides concentration about 15–35, 40–50, and 50–60%, respectively. Increasing the fruit shelf-life led to decreasing studied pesticides concentration. Between pesticide concentration and variables: pesticides kind, sampling station, fruit kind, are not seen meaningful statistic relationship (P > 0.05). This study showed that pesticide residues in fruits can be decreased by washing, refrigerating, peeling procedures and increase in public surveillance.

Bio-Zirconium Metal-Organic Framework Regenerable Bio-Beads for the Effective Removal of Organophosphates from Polluted Water

Organophosphate-based pesticides, such as diazinon, are among the most toxic organic contaminants to human and environment. Effective removal of diazinon from contaminated water sources is critical. Zirconium Metal−organic frameworks (Zr-MOFs) are promising candidates for the removal of organic contaminants from wastewater. Herein, we report the adequacy of a bio based Zr-MOF named MIP-202 for the removal of diazinon from water. On the other hand, the use of these materials in powder form is not workable, the development of scalable and economical processes and integrative of these materials onto beads is paramount for industrial processes. Hence, it was reported a scalable, bio aqueous solution-based preparation strategy for Bio Zr-MOF beads production. The composite material exposed identical reactivity under the same ambient parameters compared to powdered material in an aqueous solution. These results signify a critical procedure to an integrated strategy for organophosphates removal using bio-based MOFs, which demonstrates high potential for manufacturing applications such as continued removal of organophosphates from wastewater supplies.

The effect of organic farming on water reusability, sustainable ecosystem, and food toxicity

Water is a fundamental necessity for people's well-being and the ecosystem's sustainability; however, its toxicity due to agrochemicals usage for food production leads to the deterioration of water quality. The poor water quality diminishes its reusability, thus limiting efficient water usage. Organic farming is one of the best ways that does not only reduce the deterioration of water quality but also decrease food toxicity. In organic farming, the crop is grown with no/less chemical usage. Besides, organic farming maintains biodiversity and reduces the anthropogenic footprint on soil, air, water, wildlife, and especially on the farming communities. Fields that are organically managed continuously for years have fewer pest populations and were attributed to increased biodiversity and abundance of multi-trophic interactions as well as to changes in plant metabolites. Fewer insect pests (pathogen vectors), in turn, would result in fewer crop diseases and increase crop production. This review highlights that organic farming may play a critical role in the reduction of pests and pathogens, which eventually would reduce the need for chemical reagents to protect crops, improving yield quality and water reusability.

High effective removal of diazinon from aqueous solutions using the magnetic tragacanth-montmorillonite nanocomposite: isotherm, kinetic, and mechanism study

Health and environmental impact of pesticide contamination of groundwater has been reported repeatedly in many studies. The removal of diazinon from agricultural wastewater is still of great interest due to using widely in many developing countries. In the presented study, the magnetic tragacanth-montmorillonite nanocomposite was utilized as an adsorbent to remove diazinon from an aqueous solution. The adsorbent properties were characterized using FE-SEM, EDX, FTIR, XRD, BET, and VSM techniques. The influence of adsorbent dosage, pH, contact time, and initial concentration of diazinon was studied in a batch system. Different adsorption kinetics and isotherm models were used to describe the kinetic and equilibrium data. The results indicated that the adsorption kinetic was fitted better with a Elovich kinetic model, and the adsorption isotherm was well described by the Langmuir-Freundlich model, and the maximum adsorption capacity was 416 mg g^-1. According to Weber and Morris's model and Boyd plot, the results demonstrated that the adsorption kinetic was controlled simultaneously by film diffusion and intraparticle diffusion. Besides, a thermodynamic study showed that the removal of diazinon is an endothermic process. Considering the results, magnetic tragacanth-montmorillonite nanoadsorbent has a high capability to remove diazinon from aqueous solution.

Use of neural networks to estimate the sorption and desorption coefficients of herbicides: A case study of diuron, hexazinone, and sulfometuron-methyl in Brazil

The use of herbicides in Brazil has been carried out based on the manufacturer's recommendation, often disregarding the high variability of soil attributes. The use of statistical methods to predict the herbicide retention processes in the soil can contribute to the improvement of weed control efficiency associated with the lower risk of environmental contamination. This research evaluated the use of Artificial Neural Networks (ANNs) to predict soil sorption and desorption, as well as the environmental contamination potential of diuron, hexazinone and sulfometuron-methyl herbicides in Brazilian soils. The sorption and desorption coefficients of the three herbicides were determined in laboratory tests for 15 soils from different Brazilian states. To predict the sorption and desorption of diuron, hexazinone and sulfometuron-methyl were used a multilayer perceptron ANNs (MLP). The inputs were the characteristics of the herbicides and the physical and chemical attributes of the soils, and the outputs of were the sorption and desorption coefficients (Kfs and Kfd). The risk of leaching of diuron, hexazinone, and sulfometuron-methyl herbicides were evaluated considering the sorption values observed and those estimated by the models. The Artificial Neural Network (ANN) models were efficient for the prediction of sorption and desorption of diuron, hexazinone, and sulfometuron-methyl herbicides. The physicochemical properties of the herbicides were more important for the modeling of multilayer perceptron ANNs than the soil attributes. The herbicides diuron, hexazinone, and sulfometuron-methyl have a high potential risk for contamination of groundwater in different Brazilian states.

Efficient clean-up of waters contaminated with diazinon pesticide using photo-decomposition of peroxymonosulfate by ZnO decorated on a magnetic core/shell structure

In the present study, ZnO nanoparticles were anchored on a magnetic core/shell structure (SiO₂@Fe₃O₄) to perpetrate ZnO@SiO₂@Fe₃O₄ and then coupled with UV light as a heterogeneous nanocatalyst for activating peroxymonosulfate (PMS) into diazinon (DZ) degradation. Several techniques like XRD (X-ray diffraction), BET (Brunaeur, Emmett and Teller), TEM (Transmission electron microscope), FESEM (Field emission-scanning electron microscope) coupled with EDS (Energy Dispersive X-ray Spectrometer), PL (photoluminescence), VSM (Vibrating Sample Magnetometer) and UV-vis diffuse reflectance spectroscopy (DRS) were applied for identification of catalyst features. A possible mechanism for PMS activation and DZ degradation was proposed in details. The effect of solution pH, various concentrations of catalyst, PMS and DZ, quenching agents, different chemical oxidants and co-existing anions was assessed as operating factors to determine the optimum conditions. PMS decomposed effectively in coupling with ZnO@SiO₂@Fe₃O₄ and UV. At optimal conditions, over 95 and 56% of DZ and TOC were removed during 60 min reaction, respectively. The complete degradation of DZ was confirmed using its absorption peak in UV-vis spectra analysis over 60 min treatment. A wide variety of free radicals was identified during quenching tests. HO^• and h⁺ played a pivotal role in the degradation process of DZ. Decreasing the degradation efficiency in the presence of anions was as Cl^- > CO₃^2- > NO₃^- > PO₄^3- > SO₄^2- > HCO₃^-. A negligible amount of leaching Fe (<0.2 mg/L) was found for ZnO@SiO₂@Fe₃O₄, indicating that the catalyst possesses a high stability in oxidation systems. In addition, a significant potential was achieved in reusing of catalyst within five consecutive runs. In conclusion, ZnO@SiO₂@Fe₃O₄/PMS/UV hybrid system can be utilized as a promising advanced oxidation process into efficient degradation of pesticides, thanks to easy recovery, high catalytic activity, co-production of different reactive species and high durability and recyclability potential.

An Easy Synthesis for Preparing Bio-Based Hybrid Adsorbent Useful for Fast Adsorption of Polar Pollutants

For the first time, γ-Al₂O₃ and Bio-Based Substances (BBS) hybrids (A-BBS) were prepared through a simple electrostatic interaction occurring between alumina, used as a support, and BBS (Bio-Based Substance from composted biowastes) carrying positive and negative charges, respectively. We evaluated the optimal amount of BBS to be immobilized on the support and the stability of the resulting A-BBS in order to use this novel hybrid material as an adsorbent for the removal of polar pollutants. Characterization was carried out by X-Ray Diffraction (XRD) for evaluating the crystal structure of the support, Fourier transform infrared spectroscopy (FT-IR) to evidence the presence of BBS on the hybrid material, thermogravimetric analysis (TGA) to measure the thermal stability of the hybrid materials and quantify the BBS amount immobilized on the support, N₂ adsorption at 77 K for the evaluation of the surface area and porosity of the systems, Zeta potential measurements to evaluate the effect of BBS immobilization on the surface charge of the particles and choose the substrates possibly interacting with them. Firstly, we tested the adsorption capability of three samples differently coated with BBS toward cationic species considering various adsorbate/adsorbent ratio. Crystal Violet (CV) was chosen as model pollutant to compare the performance of the hybrid materials with those of other materials described in the literature. The adsorption data were modeled by Langmuir and Freundlich adsorption isotherms. Then, we studied the adsorption capability of the developed material towards molecules with different structures; for this purpose, two contaminants of emerging concerns (carbamazepine and atenolol) were tested. The results indicate that A-BBS could be applied in wastewater treatment for the removal of a significant amount of polar species. In addition, a comparison with literature data concerning CV adsorption was carried out in order to evaluate the environmental impact of synthetic routes used to prepare different adsorbents.

Pilot-scale treatment of atrazine production wastewater by UV/O3/ultrasound: Factor effects and system optimization

This study shed light on removing atrazine from pesticide production wastewater using a pilot-scale UV/O₃/ultrasound flow-through system. A significant quadratic polynomial prediction model with an adjusted R² of 0.90 was obtained from central composite design with response surface methodology. The optimal atrazine removal rate (97.68%) was obtained at the conditions of 75 W UV power, 10.75 g h^-1 O₃ flow rate and 142.5 W ultrasound power. A Monte Carlo simulation aided artificial neural networks model was further developed to quantify the importance of O₃ flow rate (40%), UV power (30%) and ultrasound power (30%). Their individual and interaction effects were also discussed in terms of reaction kinetics. UV and ultrasound could both enhance the decomposition of O₃ and promote hydroxyl radical (OH·) formation. Nonetheless, the dose of O₃ was the dominant factor and must be optimized because excess O₃ can react with OH·, thereby reducing the rate of atrazine degradation. The presence of other organic compounds in the background matrix appreciably inhibited the degradation of atrazine, while the effects of Cl^-, CO₃^2- and HCO₃^- were comparatively negligible. It was concluded that the optimization of system performance using response surface methodology and neural networks would be beneficial for scaling up the treatment by UV/O₃/ultrasound at industrial level.