Multivariate modeling via artificial neural network applied to enhance methylene blue sorption using graphene-like carbon material prepared from edible sugar

Biomass-Derived Carbonaceous Materials with Graphene/Graphene-Like Structures: Definition, Classification, and Environmental Applications

Biomass-derived carbonaceous materials with graphene/graphene-like structures (BGS) have attracted tremendous attention in the field of environmental remediation. The introduction of graphene/graphene-like structures into raw biochars can effectively improve their properties, such as electrical conductivity, surface functional groups, and catalytic activity. In 2021, the International Organization for Standardization defined graphene as a "single layer of carbon atoms with each atom bound to three neighbours in a honeycomb structure". Considering this definition, several studies have incorrectly referred to BGS (e.g., biomass-derived few-layer graphene or porous graphene-like nanosheets) as "graphene". The definitions and classifications of BGS and their applications in environmental remediation have not been assessed critically thus far. Comprehensive analysis and sufficient and robust evidence are highly desired to accurately determine the specific structures of BGS. In this perspective, we provide a systematic framework to define and classify the BGS. The state-of-the-art methods currently used to determine the structural properties of BGS are scrutinized. We then discuss the design and fabrication of BGS and how their distinctive features could improve the applicability of biomass-derived carbonaceous materials, particularly in environmental remediation. The environmental applications of these BGS are highlighted, and future research opportunities and needs are identified. The fundamental insights in this perspective provide critical guidance for the further development of BGS for a wide range of environmental applications.

Green composites based on volcanic red algae Cyanidiales, cellulose, and coffee waste biomass modified with magnetic nanoparticles for the removal of methylene blue

In this paper, green nanocomposites based on biomass and superparamagnetic nanoparticles were synthesized and used as adsorbents to remove methylene blue (MB) from water with magnetic separation. The adsorbents were synthesized through the wet co-precipitation technique, in which iron-oxide nanoparticles coated the cores based on coffee, cellulose, and red volcanic algae waste. The procedure resulted in materials that could be easily separated from aqueous solutions with magnets. The morphology and chemical composition of the nanocomposites were characterized by SEM, FT-IR, and XPS methods. The adsorption studies of MB removal with UV-vis spectrometry showed that the adsorption performance of the prepared materials strongly depended on their morphology and the type of the organic adsorbent. The adsorption studies presented the highest effectiveness in neutral pH with only a slight effect on ionic strength. The MB removal undergoes pseudo-second kinetics for all adsorbents. The maximal adsorption capacity for the coffee@Fe₃O₄-2, cellulose@Fe₃O₄-1, and algae@Fe₃O₄-1 is 38.23 mg g^-1, 41.61 mg g^-1, and 48.41 mg g^-1, respectively. The mechanism of MB adsorption follows the Langmuir model using coffee@Fe₃O₄ and cellulose@Fe₃O₄, while for algae@Fe₃O₄ the process fits to the Redlich-Peterson model. The removal efficiency analysis based on UV-vis adsorption spectra revealed that the adsorption effectiveness of the nanocomposites increased as follows: coffee@Fe₃O₄-2 > cellulose@Fe₃O₄-1 > algae@Fe₃O₄-1, demonstrating an MB removal efficiency of up to 90%.

Graphene oxide incorporated cellulose acetate beads for efficient removal of methylene blue dye; isotherms, kinetic, mechanism and co-existing ions studies

In this investigation, new porous adsorbent beads were formulated via the incorporation of graphene oxide (GO) into cellulose acetate beads (CA) for the adsorptive removal of methylene blue (MB) dye. The experimental results signified that the adsorption of MB dye increased with the increase in the GO ratio from 10 to 25%. In addition, the adsorption process obeyed PSO kinetic model and Langmuir isotherm model with a maximum adsorption capacity reaching 369.85 mg/g. More importantly, it was proposed that the adsorption mechanism of MB dye onto GO@CA proceeded via electrostatic interactions, H-bonding, van der Waals forces, n-π and π -π interactions. Besides, the fabricated beads exhibited an excellent ability to recycle and reuse after five successive cycles. In addition, there was a high selectivity of GO@CA beads towards MB molecules in the presence of co-existing cations such as Fe2+, Zn2+, Cu2+ and Ni2+.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.

Predictive capability evaluation and optimization of Pb(II) removal by reduced graphene oxide-based inverse spinel nickel ferrite nanocomposite

Pb(II) is a heavy metal that is a prominent contaminant in water contamination. Among the different pollution removal strategies, adsorption was determined to be the most effective. The adsorbent and its type determine the adsorption process's efficiency. As part of this effort, a magnetic reduced graphene oxide-based inverse spinel nickel ferrite (rGNF) nanocomposite for Pb(II) removal is synthesized, and the optimal values of the independent process variables (like initial concentration, pH, residence time, temperature, and adsorbent dosage) to achieve maximum removal efficiency are investigated using conventional response surface methodology (RSM) and artificial neural networks (ANN). The results indicate that the initial concentration, adsorbent dose, residence time, pH, and process temperature are set to 15 mg/L, 0.55 g/L, 100 min, 5, and 30 °C, respectively, the maximum removal efficiency (99.8%) can be obtained. Using the interactive effects of process variables findings, the adsorption surface mechanism was examined in relation to process factors. A data-driven quadratic equation is derived based on the ANOVA, and its predictions are compared with ANN predictions to evaluate the predictive capabilities of both approaches. The R² values of RSM and ANN predictions are 0.979 and 0.991 respectively and confirm the superiority of the ANN approach.

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.

Development of artificial neural networks software for arsenic adsorption from an aqueous environment

Arsenic contamination is a global problem, as it affects the health of millions of people. For this study, data-driven artificial neural network (ANN) software was developed to predict and validate the removal of As(V) from an aqueous solution using graphene oxide (GO) under various experimental conditions. A reliable model for wastewater treatment is essential in order to predict its overall performance and to provide an idea of how to control its operation. This model considered the adsorption process parameters (initial concentration, adsorbent dosage, pH, and residence time) as the input variables and arsenic removal as the only output. The ANN model predicted the adsorption efficiency with high accuracy for both training and testing datasets, when compared with the available response surface methodology (RSM) model. Based on the best model synaptic weights, user-friendly ANN software was created to predict and analyze arsenic removal as a function of adsorption process parameters. We developed various graphical user interfaces (GUI) for easy use of the developed model. Thus, a researcher can efficiently operate the software without an understanding of programming or artificial neural networks. Sensitivity analysis and quantitative estimation were carried out to study the function of adsorption process parameter variables on As(V) removal efficiency, using the GUI of the model. The model prediction shows that the adsorbent dosages, initial concentration, and pH are the most influential parameters. The efficiency was increased as the adsorbent dosages increased, decreasing with initial concentration and pH. The result show that the pH 2.0-5.0 is optimal for adsorbent efficiency (%).

Metal-Based Nanocomposite Materials for Efficient Photocatalytic Degradation of Phenanthrene from Aqueous Solutions

Polycyclic aromatic hydrocarbons (PAHs) are a class of naturally occurring chemicals resulting from the insufficient combustion of fossil fuels. Among the PAHs, phenanthrene is one of the most studied compounds in the marine ecosystems. The damaging effects of phenanthrene on the environment are increasing day by day globally. To lessen its effect on the environment, it is essential to remove phenanthrene from the water resources in particular and the environment in general through advanced treatment methods such as photocatalytic degradation with high-performance characteristics and low cost. Therefore, the combination of metals or amalgamation of bimetallic oxides as an efficient photocatalyst demonstrated its propitiousness for the degradation of phenanthrene from aqueous solutions. Here, we reviewed the different nanocomposite materials as a photocatalyst, the mechanism and reactions to the treatment of phenanthrene, as well as the influence of other variables on the rate of phenanthrene degradation.

Process modeling, characterization, optimization, and mechanisms of fluoride adsorption using magnetic agro-based adsorbent

In this study, fluoride removal from polluted potable water using magnetic carbon-based adsorbents derived from agricultural biomass was thoroughly investigated. An experimental matrix is designed considering the interactive effects of independent process variables (pH, adsorbent dose, contact time, and initial fluoride concentration) on the removal efficiency. Isotherms and kinetics studies, as well as anions interactions, were also investigated to understand the adsorption mechanisms further. The model parameters of isotherms and kinetics are estimated using nonlinear differential evolution optimization (DEO). Approaches like adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) are implemented to predict the fluoride removal and identify the optimal process values. The optimum removal efficiency of GAC-Fe₃O₄ (89.34%) was found to be higher than that of PAC-Fe₃O₄ (85.14%). Kinetics experiments indicated that they follow the intraparticle diffusion model, and adsorption isotherms indicated that they follow Langmuir and Freundlich models. Both PAC-Fe₃O₄ and GAC-Fe₃O₄ adsorbents have shown an adsorption capacity of 1.20 and 2.74 mg/g, respectively. The model predictions from ANFIS have a strong correlation with experimental results and superior to RSM predictions. The shape of the contours depicts the nonlinearity of the interactive effects and the mechanisms in the adsorption process.

Magnetic nanocomposites for sustainable water purification-a comprehensive review

Numerous contaminants in huge amounts are discharged to the environment from various anthropogenic activities. Waterbodies are one of the major receivers of these contaminants. The contaminated water can pose serious threats to humans and animals, by distrubing the ecosystem. In treating the contaminated water, adsorption processes have attained significant maturity due to lower cost, easy operation and environmental friendliness. The adsorption process uses various adsorbent materials and some of emerging adsorbent materials include carbon- and polymer-based magnetic nanocomposites. These hybrid magnetic nanocomposites have attained extensive applications in water treatment technologies due to their magnetic properties as well as combination of unique characteristics of organic and inorganic elements. Carbon- and polymer-related magnetic nanocomposites are more adapted materials for the removal of various kinds of contaminants from waterbodies. These nanocomposites can be produced via different approaches such as filling, pulse-laser irradiation, ball milling, and electro-spinning. This comprehensive review is compiled by reviewing published work of last the latest recent 3 years. The review article extensively focuses on different approaches for producing various carbon- and polymer-based magnetic nanocomposites, their merits and demerits and applications for sustainable water purification. More specifically, use of carbon- and polymer-based magnetic nanocomposites for removal of heavy metal ions and dyes is discussed in detail, critically analyzed and compared with other technologies. In addition, commercial viability in terms of regeneration of adsorbents is also reviewed. Furthermore, the future challenges and prospects in employing magnetic nanocomposites for contaminant removal from various water sources are presented.

Multi-objective optimization of permeable reactive barrier design for Cr(VI) removal from groundwater

The present study aims to develop a practical approach for the optimal permeable reactive barrier (PRB) design towards Cr(VI) removal from groundwater. Batch and column experiments were performed to investigate the characteristics of the four proposed reactive materials; nanoscale zero-valent iron (Fe⁰), bimetallic nanoscale zero-valent iron (Fe⁰/Cu), activated carbon (AC) and sand/zeolite mixture (S/Z). Kinetic analysis and dynamic modeling of the experimental data were implemented to determine the controlling conditions of the reactive performance of the PRB's materials. The sensitivity index of the design parameters was examined as an indicator of their effect on the reactive responses. Moreover, the Response Surface Methodology (RSM) was considered for optimizing the design variables of the PRB based on the practical factorial analysis. Results revealed that Fe⁰ and Fe⁰/Cu showed high performance in Cr(VI) removal, with a slight superiority to Fe⁰, with final removal efficiency values of 89.7 and 84.1%, respectively. Kinetic analysis depicted that pseudo second order was the best fitting model for Cr(VI) removal in the four materials' cases. ANOVA statistical analysis revealed that quadratic polynomial model was the best model, corresponding to the highest correlation efficiency and adequate precision, to describe the relationships in the four PRB's cases between the selected dependent variables; resident time (t_R), reactive material mass per sectional area of contaminant plume (M/A) and reactive material cost (Cost_PRB) towards the independent parameters; barrier thickness (b) and permeability (K_r). Additionally, sensitivity analysis has been conducted which depicted the high sensitivity, in the four PRB's cases, of average pore water velocity within the barrier (v_r) v_r and K_r with the highest and the second-highest sensitivity index (SI) values towards t_R, respectively. The RSM-optimization revealed that Fe⁰ is the most feasible reactive material, comparing to the other considered materials, with respect to the optimal conditions regarding the long residency (t_R = 22 days) and low cost (b = 0.521 m), with around 95.2% desirability of its optimal solution. Overall, the current study represents a significant contribution and a vital step towards an accurate PRB's design based on previously determined optimal conditions.

Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane

Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation speed, initial concentration of hydrogen peroxide (H₂O₂), and contact time on dye removal efficiency were investigated systematically. Both Response Surface Methodology (RSM) and Artificial Neural Network coupled with Particle Swarm Optimization (ANN-PSO) approaches were used for predicting the optimum process parameters to achieve maximum MB dye removal efficiency. The best optimal topology for PSO embedded ANN architecture was found to be 4-6-1. This optimized network provided higher R² values for randomized training, testing and validation data sets, which are 0.944, 0.931 and 0.946 respectively, thus confirming the efficacy of the ANN-PSO model. Compared to RSM, results confirmed that the hybrid ANN-PSO shows superior modeling capability for prediction of MB dye removal. The maximum MB dye removal efficiency of 99.5% was achieved at pH-5.77, 179 rpm, ratio of H₂O₂/MB dye of 73.2:1, within 229 min. Thus, this work demonstrated that JP-immobilized BP/PVA membrane is a promising and feasible alternative for treating industrial effluent.

Synthesis of sewage sludge-based carbon/TiO2 /ZnO nanocomposite adsorbent for the removal of Ni(II), Cu(II), and chemical oxygen demands from aqueous solutions and industrial wastewater

The removal of heavy metal ions and organic materials from wastewater due to their toxicity is necessary. In the present study, the titanium dioxide/zinc oxide (TiO₂ /ZnO) nanocomposite has been coated on the sewage sludge carbon (SSC) surface and its application was investigated for the adsorption of Ni(II), Cu(II), and chemical oxygen demands (COD) reduction from aqueous solutions and industrial wastewaters in Eshtehard, Iran. The effect of adsorption parameters in a single system such as TiO₂ /ZnO ratio, TiO₂ /ZnO concentration, pH, adsorbent dosage, contact time, ionic strength, temperature, and initial concentrations of Ni(II), Cu(II), and COD was investigated on the adsorption capacity of synthesized SSC/TiO₂ /ZnO adsorbent. The pseudo-second order and Redlich-Peterson isotherm models were best described the kinetic and equilibrium data of Ni(II), Cu(II), and COD sorption. The maximum monolayer sorption capacities of Ni(II), Cu(II), and COD were found to be 62.3, 75.1, and 1,120.3 mg/g, respectively. The central composite design was used to investigate the interaction effects of pH and initial concentrations of Ni(II), Cu(II), and COD on the simultaneous removal of Ni(II), Cu(II), and COD from aqueous solutions in a ternary system. The potential of synthesized SSC/TiO₂ /ZnO adsorbent was investigated for Ni(II), Cu(II), and COD adsorption from industrial wastewaters of Iran. PRACTITIONER POINTS: The novel sewage sludge carbon/TiO₂ /ZnO adsorbent was synthesized. Adsorption of Ni(II), Cu(II), and chemical oxygen demands (COD) from industrial wastewaters was investigated. Maximum Ni(II), Cu(II), and COD sorption capacities were 62.3, 75.1, and 1,120.3 mg/g. Simultaneous removal of Ni(II), Cu(II), and COD was investigated in a ternary system.

Modelling of methylene blue adsorption using peroxidase immobilized functionalized Buckypaper/polyvinyl alcohol membrane via ant colony optimization

Jicama peroxidase (JP) was covalently immobilized onto functionalized multi-walled carbon nanotube (MWCNT) Buckypaper/Polyvinyl alcohol (BP/PVA) membrane and employed for degradation of methylene blue dye. The parameters of the isotherm and kinetic models are estimating using ant colony optimization (ACO), which do not meddle the non-linearity form of the respective models. The proposed inverse modelling through ACO optimization was implemented, and the parameters were evaluated to minimize the non-linear error functions. The adsorption of MB dye onto JP-immobilized BP/PVA membrane follows Freundlich isotherm model (R² = 0.99) and the pseudo 1st order or 2nd kinetic model (R² = 0.980 & 0.968 respectively). The model predictions from the parameters estimated by ACO resulted values close the experimental values, thus inferring that this approach captured the inherent characteristics of MB adsorption. Moreover, the thermodynamic studies indicated that the adsorption was favourable, spontaneous, and exothermic in nature. The comprehensive structural analyses have confirmed the successful binding of peroxidase onto BP/PVA membrane, as well as the effective MB dye removal using immobilized JP membrane. Compared to BP/PVA membrane, the reusability test revealed that JP-immobilized BP/PVA membrane has better dye removal performances as it can retain 64% of its dye removal efficiency even after eight consecutive cycles. Therefore, the experimental results along with modelling results demonstrated that JP-immobilized BP/PVA membrane is expected to bring notable impacts for the development of effective green and sustainable wastewater treatment technologies.

Tackling environmental challenges in pollution controls using artificial intelligence: A review

This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.

Green Activated Magnetic Graphitic Carbon Oxide and Its Application for Hazardous Water Pollutants Removal

Graphitic carbon oxide (GCO) and magnetic graphitic carbon oxide (MGCO) were prepared from sugar via optimized green activation by employing ozone oxidation, and applied to wastewater treatment. The maximal oxidation and adsorption yield of pollutants were achieved at pH 2.0−4.0, which is the optimized pH for ozone oxidation of GC to generate GCO. As-prepared GCO and MGCO were characterized using X-ray, infrared, and microscopic techniques. The MGCO has enough saturation magnetization (MS) of 41.38 emu g−1 for separation of the sorbent from the reaction medium by applying an external magnetic field. Batch adsorption of radioactive and heavy metals (Th(IV), Pb(II)), and a dye (methylene blue (MB)) using GCO and MGCO was evaluated by varying the adsorbent dose, equilibrium pH, contact time, initial metal and dye concentrations, and kinetics and isotherms. Adsorption kinetics and isotherm studies indicated that Th(IV), Pb(II), and MB adsorption were best described by pseudo-second-order kinetics and Langmuir isotherm with R2 (correlation coefficient) > 0.99, respectively. The maximum adsorption capacities for Th(IV), Pb(II), and MB were 52.63, 47.39, and 111.12 mg g−1 on GCO and 76.02, 71.94, and 76.92 mg g−1 on MGCO. GCO and MGCO are prospectively effective and low-cost adsorbents for ion removal in wastewater treatment. As prepared MGCO can be reused up to three cycles for Th(IV), Pb(II), and MB. This work provides fundamental information about the equilibrium adsorption isotherms and mechanisms for Th(IV), Pb(II), and MB on GCO and MGCO.

Removal of U(VI) by sugar-based magnetic pseudo-graphene oxide and its application to authentic groundwater using electromagnetic system

Uranium U(VI) is toxic even at trace levels in aqueous solution and has adverse impacts on the health of human beings. In this study, a sugar-based magnetic pseudo-graphene oxide (SMGO) composite was prepared for the removal of U(VI) from groundwater by graphitization of sugar and ozonation, as well as synthesis with nano-size magnetite particles. To investigate the applicability of SMGO, U(VI)-spiked groundwater as well as U(VI)-contaminated groundwater samples were used in electromagnetic system. The pH-edge adsorption results suggest that adsorption occurs via an inner-sphere surface complex with an optimized pH of 4, where UO₂²⁺ is the dominant U(VI) species. The adsorption isotherm results confirmed that the adsorption of U(VI) onto SMGO occurred via a monolayer process on the homogeneous surface of SMGO and the maximum removal capacity of U(VI) was 28.2 mg/g. The high-gradient magnetic separation (HGMS) principle was applied to U(VI) removal using SMGO to facilitate recovery and the repeated use of the adsorbent during multiple batch cycles. The results indicated that the initial U(VI) concentration (439.1 μg/L) was reduced to a value less than the standard level of U(VI) for drinking water (30 μg/L) during six batch cycles and the separation efficiency was 95.2%. As such, SMGO and electromagnetic system using the HGMS principle are promising technologies for the removal of U(VI) in groundwater.

Green Synthesis of Iron Oxide Nanoparticles for Lead Removal from Aqueous Solutions

Pb(II) being carcinogenic and one of the heavy metals which always pose a severe threat to human health. Adsorption is a commonly used method for the removal of heavy metal ions as this process possess high efficiency, easy to handle and cost-effective. Iron oxide based nanomaterial were found to be more attractive for the removal of heavy metals from the aqueous solution because of their size, high surface area, and magnetic. Therefore, in this research study, iron oxide nanoparticles modified with tangerine peel extract (T-Fe3O4) and utilized to carry batch adsorption experiments for the removal of lead from aqueous solutions. It was observed that 99% of Pb(II) adsorption removal was achieved with 0.6 g/L of T-Fe3O4 at an initial concentration of metal at 10 ppm and room temperature of 25°C. The adsorption isotherm was found to be monolayer on the homogeneous surface of the adsorbent. Therefore, the green tangerine peel modified iron oxide nanoparticles can be applied for lead removal from water resources for providing clean and hygienic water for a sustainable and healthier life.