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.

Tocilizumab degradation via photo-catalytic ozonation process from aqueous.

Following the advent of the coronavirus pandemic, tocilizumab has emerged as a potentially efficacious therapeutic intervention. The utilization of O3-Heterogeneous photocatalytic process (O3-HPCP) as a hybrid advanced oxidation technique has been employed for the degradation of pollutants. The present study employed a solvothermal technique for the synthesis of the BiOI-MOF composite. The utilization of FTIR, FESEM, EDAX, XRD, UV-vis, BET, TEM, and XPS analysis was employed to confirm the exceptional quality of the catalyst. the study employed an experimental design, subsequently followed by the analysis of collected data in order to forecast the most favorable conditions. The purpose of this study was to investigate the impact of several factors, including reaction time (30-60 min), catalyst dose (0.25-0.5 mg/L), pH levels (4-8), ozone concentration (20-40 mMol/L), and tocilizumab concentration (10-20 mg/L), on the performance of O3-HPCP. The best model was discovered by evaluating the F-value and P-value coefficients, which were found to be 0.0001 and 347.93, respectively. In the given experimental conditions, which include a catalyst dose of 0.46 mg/L, a reaction time of 59 min, a pH of 7.0, and an ozone concentration of 32 mMol/L, the removal efficiencies were found to be 92% for tocilizumab, 79.8% for COD, and 59% for TOC. The obtained R2 value of 0.98 suggests a strong correlation between the observed data and the predicted values, indicating that the reaction rate followed first-order kinetics. The coefficient of synergy for the degradation of tocilizumab was shown to be 1.22. The catalyst exhibited satisfactory outcomes, but with a marginal reduction in efficacy of approximately 3%. The sulfate ion (SO42-) exhibited no influence on process efficiency, whereas the nitrate ion (NO3-) exerted the most significant impact among the anions. The progress of the process was impeded by organic scavengers, with methanol exhibiting the most pronounced influence and sodium azide exerting the least significant impact. The efficacy of pure BiOI and NH2-MIL125 (Ti) was diminished when employed in their pure form state. The energy consumption per unit of degradation, denoted as EEO, was determined to be 161.8 KWh/m3-order.

Nonionizing Electromagnetic Irradiations; Biological Interactions, Human Safety.

Human is usually exposed to environmental radiation from natural and man-made sources. Therefore, it is important to investigate the effects of exposure to environmental radiation, partly related to understanding and protecting against the risk of exposure to environmental radiation with beneficial and adverse impacts on human life. The rapid development of technologies causes a dramatic enhancement of radiation in the human environment. In this study, we address the biological effects caused by different fractions of non-ionizing electromagnetic irradiation to humans and describe possible approaches for minimizing adverse health effects initiated by radiation. The main focus was on biological mechanisms initiated by irradiation and represented protection, and safety approaches to prevent health disorders.

Visible-light-driven TiO2@Fe2O3/Chitosan nanocomposite with promoted photodegradation of meropenem and imipenem antibiotics by peroxymonosulfate.

This study assessed wastewater treatment by visible-light/Peroxymonosulfate process using its linking with TiO2@Fe3O4 nanoparticles coated on chitosan. Meropenem and Imipenem photodegradation was evaluated as a model-resistant contaminant by TiO2@Fe2O3/chitosan nanocomposite. The synthesised TiO2@Fe2O3/chitosan was characterised using various techniques. Fe2O3 and TiO2 nanoparticles on the chitosan surface were affirmed via XRD, EDX, and FTIR findings. The FESEM and TEM results verified the deposition of TiO2@Fe2O3 on the chitosan surface. Under optimum circumstances (pH = 4, catalyst dosage = 0.5 g/L, antibiotics concentration = 25 mg/L reaction time = 30 min, and PMS = 2 mM), maximum degradation efficiency was obtained at about 95.64 and 93.9% for Meropenem and Imipenem, respectively. Also, the experiments demonstrated that TiO2@Fe2O3/chitosan had a better performance than photolysis and adsorption by catalyst without visible light irradiation in degrading antibiotics. The scavenger tests confirmed that O2⋅-, SO4⋅-, HO⋅, and h+ are present simultaneously during the pollutant photodegradation process. After five recovery cycles, the system eliminated over 80 percent of antibiotics. It suggested that the catalyst's capacity to be reused may be cost-effective.

Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain.

Electroencephalography (EEG) signals are considered one of the oldest techniques for detecting disorders in medical signal processing. However, brain complexity and the non-stationary nature of EEG signals represent a challenge when applying this technique. The current paper proposes new geometrical features for classification of seizure (S) and seizure-free (SF) EEG signals with respect to the Poincaré pattern of discrete wavelet transform (DWT) coefficients. DWT decomposes EEG signal to four levels, and thus Poincaré plot is shown for coefficients. Due to patterns of the Poincaré plot, novel geometrical features are computed from EEG signals. The computed features are involved in standard descriptors of 2­D projection (STD), summation of triangle area using consecutive points (STA), as well as summation of shortest distance from each point relative to the 45-degree line (SSHD), and summation of distance from each point relative to the coordinate center (SDTC). The proposed procedure leads to discriminate features between S and SF EEG signals. Thereafter, a binary particle swarm optimization (BPSO) is developed as an appropriate technique for feature selection. Finally, k-nearest neighbor (KNN) and support vector machine (SVM) classifiers are used for classifying features in S and SF groups. By developing the proposed method, we have archived classification accuracy of 99.3 % with respect to the proposed geometrical features. Accordingly, S and SF EEG signals have been classified. Also, Poincaré plot of SF EEG signals has more regular geometrical shapes as compared to S group. As a final remark, we notice that the Poincaré plot of coefficients in S EEG signals has occupied more space as compared to SF EEG signals (Tab. 3, Fig. 11, Ref. 57). Text in PDF www.elis.sk Keywords: EEG signal, DWT, Poincaré plot, geometrical feature, BPSO, SVM, KNN.

Effects of workload and job stress on the shift work disorders among nurses: PLS SEM modeling.

Nurses, as the largest forces in the health system, are always challenged with various work responsibilities such as long working hours, lack of manpower and death of patients. This study aimed at providing a model of the relationship between workload and physical and mental health, sleep disorders, and individual and family problems by the mediation role of job stress. The present study is a cross-sectional study that was conducted on 300 nurses in a specialty and sub-specialty hospital in Tehran. For this reason, various questionnaires including demographic, survey of shift workers (SOS) and job stress questionnaires were used to collect the desired data. The proposed model was presented using structural equation modeling method based on Smart-PLS and SPSS-20 software. The results show that workload has an effect on job stress (ß=0.747), mental health (ß=-0.291), Physical health (ß=-0.253), sleep quality (ß=-0.234) and personal and family problems (ß=-0.206). Also the results of this study show that job stress has an effect on mental health (ß=-0.295), Physical health (ß=-0.349), sleep quality (ß=-0.295) and Personal and family problems (ß=-0.441). In conclusion, results showed that the data fitted well with the model and that workload is associated with physical and mental problems, sleep disorders and individual and family problems both directly and indirectly through job stress mediation.

Assessment of rotavirus and norovirus emitted from water spray park: QMRA, diseases burden and sensitivity analysis.

A quantitative model on exposure to pathogenic viruses in air of recreational area and their corresponding health effects is necessary to provide mitigation actions in content of emergency response plans (ERP). Here, the health risk associated with exposure to two pathogenic viruses of concern: Rotavirus (RoV) and Norovirus (NoV) in air of water spray park were estimated using a quantitative microbial risk assessment (QMRA) model. To this end, real-time Reverse Transcriptase polymerase chain reaction (real-time RT-PCR) was employed to measure the concentration levels of RoV and NoV over a twelve-month period. The probability of infection, illness and diseases burden of gastrointestinal illness (GI) caused by RoV and NoV for both workers and visitors were estimated using QMRA and Monto-Carlo simulation technique. The annual mean concentration for RoV and NoV in sampling air of water spray park were 20and 1754, respectively. The %95 confidence interval (CI) calculated annual DALY indicator for RoV (Workers: 2.62 × 10-4-2.62 × 10-1, Visitors: 1.50 × 10-5-2.42 × 10-1) and NoV (Workers: 5.54 × 10-3-2.53 × 10-1; Visitors: 5.18 × 10-4-2.54 × 10-1) were significantly higher the recommended values by WHO and US EPA (10-6-10-4 DALY pppy). According to sensitivity analysis, exposure dose and disease burden per case (DBPC) were found as the most influencing factors on disease burden as a consequences of exposure to RoV and NoV, respectively. The comprehensive information on DALY and QMRA can aid authorities involved in risk assessment and recreational actions to adopt proper approach and mitigation actions to minimize the health risk.

Effect of creatine supplementation on kidney stones recurrence in an athlete: a case report.

INTRODUCTION: Several studies examined the effects of creatine monohydrate supplementation on renal function, but no previous study has investigated its effects on kidney stones in humans. OBSERVATION: A renal ultrasound in a healthy young athlete (without a known renal morphological anomaly, normal kidney function, normal phospholipid and uric acid data) revealed a kidney stone of 11 mm in the lower right calyx. Extracorporeal shock wave lithotripsy was applied in order to break the down stone. Twentyone days later, a follow-up renal ultrasound showed the absence of stones in the kidney. One week later, the athlete started creatine monohydrate supplementation for two months. Fourteen months after stopping creatine monohydrate supplementation, a third renal ultrasound confirmed the absence of stones in the kidney. CONCLUSION: Two months of creatine monohydrate supplementation in an athlete with a history of kidney stones could not be associated with kidney stone recurrence in the long run.

Identification of normal and depression EEG signals in variational mode decomposition domain.

Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system.

Corrigendum to data on nitrate-nitrite pollution in the groundwater resources a Sonqor plain in Iran [Data in Brief Volume 20 (2018) Pages 394-401].

[This corrects the article DOI: 10.1016/j.dib.2018.08.023.].

Association of Ramadan Participation with Psychological Parameters: A Cross-Sectional Study during the COVID-19 Pandemic in Iran.

COVID-19 restrictions are associated with poor physical-activity (PA). Less is known about the relationship between the combination of these restrictions with Ramadan intermittent fasting (RIF), PA, mental health, and sleep-quality. The present study aimed to evaluate whether COVID-19 restrictions and RIF during the fourth wave of the COVID-19 pandemic in Iran are associated with poor PA, anxiety, well-being, and sleep-quality outcomes. A total of 510 individuals participated in an online questionnaire that was disseminated to adults (≥18 years) residing in Iran from 13 May 2021 to 16 May 2021 (~3 days), just after the end of Ramadan 2021. PA behavior (Godin-Shephard Leisure-Time Exercise Questionnaire), anxiety (General Anxiety Disorder-7), well-being (Mental Health Continuum-Short Form), and sleep-quality (Pittsburgh Sleep Quality Index). Of 510 individuals included in the study (331 female (64.9%); mean ± SD, 31 ± 12 years), 172 (33.7%) reported less PA during the Ramadan 2021. PA was associated with better well-being and sleep-quality outcomes. Regardless of PA, participants who fasted for all of Ramadan had less anxiety and better well-being outcomes than those who fasted part of Ramadan or did not fast at all. However, the fasting part of Ramadan decreased the sleep-quality of active participants. The Ramadan 2021 was associated with poor PA, well-being, and sleep-quality of Iranians. However, PA was associated with better well-being and sleep-quality outcomes, and those who fasted all Ramadan had better anxiety and well-being outcomes. Therefore, PA during Ramadan might be an essential and scalable mental health resilience builder during COVID-19 restrictions which should be encouraged.

The COVID-19 pandemic and physical activity during intermittent fasting, is it safe? A call for action.

Intermittent fasting (IF) has recently gained popularity, and has been used for centuries in many religious practices. The Ramadan fasting is a mandatory form of IF practiced by millions of healthy adult Muslims globally for a whole lunar month every year. In Islam, the "Sunna" also encourages Muslims to practice IF all along the year (e.g.; two days a week). The 2019-Coronavirus disease (COVID-19) pandemic in the context of Ramadan has raised the question whether fasting is safe practice during the COVID-19 pandemic health crisis, and what would be the healthy lifestyle behaviors while fasting that would minimize the risk of infection. As COVID-19 lacks a specific therapy, IF and physical activity could help promote human immunity and be part of holistic preventive strategy against COVID-19. In this commentary, the authors focus on this dilemma and provide recommendations to the fasting communities for safely practicing physical activity in time of COVID-19 pandemic.

Effect of Base Transceiver Station (BTS) waves on some blood factors in domestic pigeons: an experimental study.

PURPOSE: BTS waves are one of the most important environmental pollutants, but there is inadequate data of its effects on living creatures. Birds have major role in environmental balance and hematologic factors are good describers of animal health. Therefore, we studied hematological factors in pigeons to assess the health effects of BTS waves in urban birds. METHODS: This experiment has been run on 120 six month-old pigeons. After adaptation to laboratory settings, they divided to six random groups of distance from BTS and daily exposure time. G1: 50 cm/30 min, G2: 100 cm/30 min daily, G3: 150 cm/30 min, G4: 50 cm/60 min, G5: 100 cm/60 min and G6: 150 cm/60 min. Daily exposure done for 30 consecutive days. Hematologic studies done before and after exposure for analysis of WBC, Neut, Mono, Lymph, RBC, Hb, HCT, MCV, MCHC and platelets. Results processed statistically by SPSS software. RESULTS: The results of this study showed a significant difference between the six experimental groups. The results showed distance from the BTS source had the largest effect on PLT followed by HCT, MCV, MCHC, Neut, Hb, RBC, Lymph, WBC, and Mono, respectively. Moreover, the duration of exposure to BTS wave had the largest effect on Mono followed by PLT, Neut, MCV, MCHC, WBC, HCT, Lymph, RBC and Hb, respectively. CONCLUSIONS: Study showed that increasing exposure time and decreasing distance from the wave source have significant effect on hematologic factors. The distance has more effect than exposure time. Further investigation on protection and reducing the side effects are recommended.

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain.

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

A novel computer-aided diagnosis framework for EEG-based identification of neural diseases.

Recent advances in electroencephalogram (EEG) signal classification have primarily focused on domain-specific approaches, which impede algorithm cross-discipline capability. This study introduces a new computer-aided diagnosis (CAD) system for the classification of two distinct EEG domains under a unified sequential framework. The key motivation to consider two neural diseases by one framework is to develop a unified algorithm for EEG classification. The main contributions of this study are five-fold. First, EEG signals are decomposed into 10 intrinsic mode functions (IMFs) with the help of empirical wavelet transform. Second, a novel two-dimensional (2D) modeling of IMFs is plotted to visualize the complexity of EEG signals. Third, several new geometrical features are extracted to analyze the dynamic and chaotic essence. Fourth, significant features are selected by binary particle swarm optimization algorithm (B-PSO). Fifth, selected features are fed to the k-nearest neighbor classifier for EEG signal classification purposes. All the experiments are executed on one depression and two epileptic EEG datasets in a leave one out cross-validation strategy. The proposed CAD system provides an average classification accuracy of 93.35% in depression detection, 99.33% for regular against ictal, and 97.33% for interictal versus ictal respectively. The overall empirical analysis authenticates that the proposed CAD outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus, can be endorsed as an effective automated neural rehabilitation system.

Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain.

A widespread brain disorder of present days is depression which influences 264 million of the world's population. Depression may cause diverse undesirable consequences, including poor physical health, suicide, and self-harm if left untreated. Depression may have adverse effects on the personal, social, and professional lives of individuals. Both neurologists and researchers are trying to detect depression by challenging brain signals of Electroencephalogram (EEG) with chaotic and non-stationary characteristics. It is essential to detect early-stage depression to help patients obtain the best treatment promptly to prevent harmful consequences. In this paper, we proposed a new method based on centered correntropy (CC) and empirical wavelet transform (EWT) for the classification of normal and depressed EEG signals. The EEG signals are decomposed to rhythms by EWT and then CC of rhythms is computed as the discrimination feature and fed to K-nearest neighbor and support vector machine (SVM) classifiers. The proposed method was evaluated using EEG signals recorded from 22 depression and 22 normal subjects. We achieved 98.76%, 98.47%, and 99.05% average classification accuracy (ACC), sensitivity, and specificity in a 10-fold cross-validation strategy by using an SVM classifier. Such efficient results conclude that the method proposed can be used as a fast and accurate computer-aided detection system for the diagnosis of patients with depression in clinics and hospitals.

Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms.

Surgery is recommended for epilepsy diagnosis in cases where patients do not respond well to anti-epilepsy medications. Successful surgery is essentially dependent on the area suffered from epilepsy, i.e., focal area. Electroencephalogram (EEG) signals are considered a powerful tool to identify focal or non-focal (normal) areas. In this work, we propose an automated method for focal and non-focal EEG signal identification, taking into account non-linear features derived from rhythms in the empirical wavelet transform (EWT) domain. The research paradigm is related to the decomposition of EEG signals into the delta, theta, alpha, beta, and gamma rhythms through the development of the EWT. Specifically, various non-linear features are extracted from rhythms composed of Stein's unbiased risk estimation entropy, threshold entropy, centered correntropy, and information potential. From a statistical point of view, Kruskal-Wallis (KW) statistical test is then used to identify the significant features. The significant features obtained from the KW test are fed to support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. The SURE entropy provides an average classification accuracy of 93% and 82.6% for small and entire datasets by utilizing SVM and KNN classifiers with a tenfold cross-validation method, respectively. It is observed that the proposed method is better and competitive in comparison with other studies for small and large data, respectively. The obtained outcome concludes that the proposed framework could be used for people with epilepsy and can help the physicians to validate the assessment.

Human, Forest and vegetation health metrics of ground-level ozone (SOMO35, AOT40f and AOT40v) in Tehran.

PURPOSE: We aimed to investigate the spatial O3 indices (SOMO35: annual sum of maximum daily 8-h ozone means over 35 ppb, AOT40: the accumulated exposure over an hourly threshold of 40 ppb during daylight hours between 8:00 and 20:00 in the growing seasons of plants) in Tehran (2019-2020). METHODS: The data of ambient O3 concentrations, measured at twenty-three regulatory ambient air quality monitoring stations (AQMSs) in Tehran, were obtained. RESULTS: The annual mean O3 concentrations were found to be 15.8-25.7 ppb; the highest and lowest annual mean concentration of ambient O3 were observed in Shahrdari 22 and Shahr-e-Rey stations, respectively. Spatial distribution of exposure to O3 across Tehran was in the range of 1.36-1.64; the highest O3 concentrations were observed in the northern, west and south-western parts of Tehran, while the central and south areas of Tehran city experienced low to moderate concentrations. The indices of SOMO35, AOT40f and AOT40v across AQMSs in Tehran was in the range of 1830-6437 ppb. Days, 10,613-39,505 ppb.h and 4979-16,804 ppb.h, respectively. For Tehran city, the indices of SOMO35 and AOT40f were 4138 ppb. days and 27,556 ppb.h respectively. Our results revealed that the value of SOMO35 across AQMSs of Tehran was higher than the recommended target value of 3000 ppb. days. CONCLUSIONS: To reduce O3 pollution and its effects on both human and plants health, the governmental organizations should take appropriate sustainable control policies.

Poly (amidoamine) generation 6 functionalized Fe3O4@SiO2/GPTMS core-shell magnetic NPs as a new adsorbent for Arsenite adsorption: kinetic, isotherm and thermodynamic studies.

In this survey a new route has been developed the preparation of poly (amidoamine) generation 6 (PAMAM-G6) dendrimer functionalized Fe3O4/SiO2 nanoparticle and was used for arsenite (As (III)) adsorption. SiO2 was first grafted onto the surface of Fe3O4 to formation a core-shell structure. Then the introduction of epoxy rings were done by hydrolysis of methylsilane groups of 3-Glycidoxypropyltrimethoxysilane (GPTMS) on OH groups of SiO2 and afterwards, PAMAM-G6 reacted with epoxy rings of GPTMS to obtain a multiamino magnetic adsorbent. The as-prepared nanocomposite was characterized by TEM, Zeta potential, FESEM, VSM, FTIR, Raman and XPS techniques. The effects of reaction time from 5 to 50 min, initial As (III) concentration in the range of 1-10 mgL-1, initial adsorbent concentration in the range of 10-50 mgL-1 and initial pH in the range 3-8 were studied. The resulting of kinetic and isotherm models displays high adsorption affinity (233 mg/g) for As (III) and the adsorbent can reach the adsorbent can reach the adsorption equilibrium at a neutral pH (7). The As (III) loaded nanocomposite could be separated readily from aqueous solution by magnetic and regenerated simply via NaOH. The study of the adsorption procedure showed that the pseudo-second order kinetics and Langmuir isotherm well-fitted with the experimental data of As (III) adsorption onto nanocomposite.