Assessing the carcinogenic and non-carcinogenic health risks of metals in the drinking water of Isfahan, Iran.

Metals are significant contributors to water pollution, posing serious threats to human health. This study aims to assess the carcinogenic and non-carcinogenic health risks associated with metals in Isfahan drinking water. Eighty water samples were randomly collected from the city's distribution network between January and March 2020-2021. Inductively coupled plasma Optical Emission Spectrometry was used to measure toxic metals, namely Pb, Cr, Cd, Ni, and As concentrations. Results revealed that the mean concentration of Ni (70.03 µg/L) exceeded the WHO reference value (70 µg/L), while the other metals were below the standard values. The average chronic daily intake order of toxic metals was Ni > Cr > Pb > As > Cd. Non-carcinogenic risk assessment through hazard quotient (HQ) and hazard index (HI) demonstrated that both THI for adults (HQingestion + HQdermal = 4.02E-03) and THI for children (HIingestion + HIdermal = 3.83E-03) were below the acceptable limit (less than 1). This indicated no non-carcinogenic risk to residents through water ingestion or dermal exposure. However, findings indicated that the ingestion route was the primary exposure pathway, with HQ values for ingestion exceeding HQ values for dermal adsorption. Carcinogenic risk assessment showed that the risk associated with As metal exceeded the acceptable limit (1 × 10-6). Therefore, implementing treatment improvement programs and appropriate control measures is essential to safeguard the health of Isfahan City residents.

A comparative study of response surface methodology and artificial neural network based algorithm genetic for modeling and optimization of EP/US/GAC oxidation process in dexamethasone degradation: Application for real wastewater, electrical energy consumption.

Dexamethasone (DXM) is a broadly used drug, which is frequently identified in the water environments due to its improper disposal and incomplete removal in wastewater treatment plant. The inability of conventional treatment processes of wastewater causes that researchers pay a great attention to study and develop effective wastewater treatment systems. This work deals with the study of integrated electro-peroxone/granular activated carbon (EP/US/GAC) process in the degradation of dexamethasone (DXM) from a water environment and the remediation of real pharmaceutical wastewater. Two approaches of response surface methodology based on central composite design (RSM-CCD) and artificial neural network based on algorithm genetic (ANN-GA) were employed for modeling and optimization of the process. Both the models presented significant adequacy for modeling and prediction of the process according to statistical linear and nonlinear metrics (R2 = 0.9998 and 0.9996 and RMSE = 0.2128 and 0.1784 for ANN-GA and RSM-CCD, respectively). The optimization study provided the same outcomes for both ANN-GA and RSM-CCD approaches, where approximately complete DEX oxidation was achieved at pH = 9.3, operating time = 10 min, US power = 300 W/L, applied current = 470 mA, and electrolyte concentration = 0.05 M. A synergistic study signified that the EP/US/GAC process made an 82% synergy index as compared to the individual US and EP processes. The calculated energy consumption for the integrated process was achieved to be 2.79 kW h/gCOD. Quenching test by tert-butanol and p-benzoquinone revealed that HO• radical possessed the largest contribution in DEX degradation. The efficiency of EP/US/GAC process in the remediation of real pharmaceutical wastewater showed a significant decline in COD content (92% removal after 180 min), and the ratio of initial BOD/COD ratio of 0.27 was elevated up to 0.7 after 100 min treatment time. The performance stability of EP/US/GAC system showed no remarkable drop in removal efficiency, and leakage of lead ions from the anode surface was negligible and below WHO guideline for drinking water. Generally, this research work manifested that the integrated EP/US/GAC system elevated the degradation efficiency and can be proposed as a pretreatment step before biological treatment processes for the remediation of recalcitrant wastewaters.

4-Chlorophenol adsorption from water solutions by activated carbon functionalized with amine groups: response surface method and artificial neural networks.

4-Chlorophenol pollution is a significant environmental concern. In this study, powdered activated carbon modified with amine groups is synthesized and investigated its efficiency in removing 4-chlorophenols from aqueous environments. Response surface methodology (RSM) and central composite design (CCD) were used to investigate the effect of different parameters, including pH, contact time, adsorbent dosage, and initial 4-chlorophenol concentration, on 4-chlorophenol removal efficiency. The RSM-CCD approach was implemented in R software to design and analyze the experiments. The statistical analysis of variance (ANOVA) was used to describe the roles of effecting parameters on response. Isotherm and kinetic studies were done with three Langmuir, Freundlich, and Temkin isotherm models and four pseudo-first-order, pseudo-second-order, Elovich, and intraparticle kinetic models in both linear and non-linear forms. The synthesized adsorbent was characterized using X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) analyses. The results showed that the synthesized modified activated carbon had a maximum adsorption capacity of 316.1 mg/g and exhibited high efficiency in removing 4-chlorophenols. The optimal conditions for the highest removal efficiency were an adsorbent dosage of 0.55 g/L, contact time of 35 min, initial concentration of 4-chlorophenol of 110 mg/L, and pH of 3. The thermodynamic study indicated that the adsorption process was exothermic and spontaneous. The synthesized adsorbent also showed excellent reusability even after five successive cycles. These findings demonstrate the potential of modified activated carbon as an effective method for removing 4-chlorophenols from aqueous environments and contributing to developing sustainable and efficient water treatment technologies.

Modeling, optimization and efficient use of MMT K10 nanoclay for Pb (II) removal using RSM, ANN and GA.

Regarding the long-term toxic effects of Pb (II) ions on human health and its bioaccumulation property, taking measures for its reduction in the environment is necessary. The MMT-K10 (montmorillonite-k10) nanoclay was characterized by XRD, XRF, BET, FESEM, and FTIR. The effects of pH, initial concentrations, reaction time, and adsorbent dosage were studied. The experimental design study was carried out with RSM-BBD method. Results prediction and optimization were investigated with RSM and artificial neural network (ANN)-genetic algorithm (GA) respectively. The RSM results showed that the experimental data followed the quadratic model with the highest regression coefficient value (R2 = 0.9903) and insignificant lack of fit (0.2426) showing the validity of the Quadratic model. The optimal adsorption conditions were obtained at pH 5.44, adsorbent = 0.98 g/l, concentration of Pb (II) ions = 25 mg/L, and reaction time = 68 min. Similar optimization results were observed by RSM and artificial neural network-genetic algorithm methods. The experimental data revealed that the process followed the Langmuir isotherm and the maximum adsorption capacity was 40.86 mg/g. Besides, the kinetic data indicated that the results fitted with the pseudo-second-order model. Hence, the MMT-K10 nanoclay can be a suitable adsorbent due to having a natural source, simple and inexpensive preparation, and high adsorption capacity.

Kinetic studies of dexamethasone degradation in aqueous solution via a photocatalytic UV/H2O2/MgO process.

Wastewaters discharged from different industries and hospitals may contain pharmaceuticals, especially dexamethasone (DEX). Thus, we applied the UV/H2O2 photocatalytic method in the presence of the MgO nanoparticles to remove dexamethasone from synthetic wastewater. Moreover, the effects of parameters such as pH (3-11), hydrogen peroxide concentration (1-8 mM), initial DEX concentration (5-30 mg/L), and catalyst dosage (0.01-0.2 g/L) during the reaction times (0-30 min) were investigated. Furthermore, the efficiency of UV/H2O2 in the presence and absence of catalysts was investigated. The photocatalyst is characterized by X-ray diffraction (XRD), field emission scanning electron microscope (FE-SEM), and Fourier-transform infrared spectroscopy (FTIR) techniques. It was found that the removal rate was enhanced by decreasing pH and the initial dexamethasone concentration. The removal rate was enhanced somewhat with concentrations of hydrogen peroxide and MgO. In the case of UV/H2O2/MgO, 87% removal efficiency was achieved, under the optimal conditions: pH 3, contact time of 30 min, dexamethasone concentration of 20 mg/L, H2O2 of 0.5 mM, and UV radiation of 55 watts. The kinetic data indicated that the reaction followed the second-order kinetic model. The results showed that the UV/H2O2 photochemical process can efficiently remove dexamethasone from aqueous in the presence of a MgO catalyst, and the mineralization efficiency was reached at about 98%.

Adsorptive removal of humic substances using cationic surfactant-modified nano pumice from water environment: Optimization, isotherm, kinetic and thermodynamic studies.

In this study, nano pumice (NP) and a cationic surfactant (hexadecyltrimethylammonium-chloride (HDTMA.Cl)) treated nano pumice (HMNP) were used for humic acid (HA) adsorption from an aqueous solution. The adsorption process was modeled and optimized using Response surface methodology-central composite design (RSM-CCD) and Artificial neural networks- Genetic algorithm (ANN-GA). The results show that the ANN model outperforms the RSM-CCD model in terms of response prediction. Optimization results based on the RSM-CCD approach proposed pH 3, adsorbent dose 3 g L-1, reaction time 60 min, and initial HA concentration 5 mg L-1 as optimal points of the variables, to reach the maximum adsorption efficiency of 100% and 65.4% by HMNP and NP adsorbents. The maximal adsorption capacity of NP was 1.21 mg g-1, while that of HMNP was 27.34 mg g-1. The optimal points of process parameters by the ANN-GA method are in accordance with the values suggested by the RSM-CCD method. In isotherm studies, Langmuir model was found to be the best-fitted model for both adsorbent with R2 = 0.97 for NP and 0.992 for HMNP, and also among three different kinetic models which were assessed, Pseudo-second-order model with R2 = 0.9989 for HMNP and R2 = 0.9957 for NP were the best-fitted models for HA removal. Thermodynamic studies indicated that the HA adsorption process by both of the adsorbents is endothermic and the nature of HMNP was found spontaneous while for NP was non-spontaneous. The value of ΔH for both adsorbents was in the range of 34-36.8 kJ mol-1 so the process is clarified as chemical-physical adsorption. The reusability test revealed that the adsorption effectiveness of HMNP drops from 100% to 82.4% after 10 consecutive recycles. The influence of interfacing anions indicated that the adsorption efficiency drops from 100% to 95.4% when the anions were added to the reaction solution.

Facile fabrication of amino-functionalized MIL-68(Al) metal-organic framework for effective adsorption of arsenate (As(V)).

An amino-functionalized MIL-68(Al) metal-organic framework (amino-MIL-68(Al) MOF) was synthesized by solvothermal method and then characterized by FESEM, XRD, FTIR, EDX-mapping, and BET-BJH techniques. In order to predict arsenate (As(V)) removal, a robust quadratic model (R2 > 0.99, F-value = 2389.17 and p value < 0.0001) was developed by the central composite design (CCD) method and then the genetic algorithm (GA) was utilized to optimize the system response and four independent variables. The results showed that As(V) adsorption on MOF was affected by solution pH, adsorbent dose, As(V) concentration and reaction time, respectively. Predicted and experimental As(V) removal efficiencies under optimal conditions were 99.45 and 99.87%, respectively. The fitting of experimental data showed that As(V) adsorption on MOF is well described by the nonlinear form of the Langmuir isotherm and pseudo-second-order kinetic. At optimum pH 3, the maximum As(V) adsorption capacity was 74.29 mg/g. Thermodynamic studies in the temperature range of 25 to 50 °C showed that As(V) adsorption is a spontaneous endothermic process. The reusability of MOF in ten adsorption/regeneration cycles was studied and the results showed high reusability of this adsorbent. The highest interventional effect in inhibiting As(V) adsorption was related to phosphate anion. The results of this study showed that amino-MIL-68(Al) can be used as an effective MOF with a high surface area (> 1000 m2/g) and high reusability for As(V)-contaminated water.

Acid red 18 removal from aqueous solution by nanocrystalline granular ferric hydroxide (GFH); optimization by response surface methodology & genetic-algorithm.

The need for fresh water is more than before by population growth, and industrial development have affected the quality of water supplies, one of the important reason for water contamination is synthetic dyes and their extensive use in industries. Adsorption has been considered as a common methods for dye removal from waters. In this study, Acid Red18 removal in batch mode by using Granular Ferric Hydroxide (GFH) was investigated. The GFH characterized by XRD, FESEM and FTIR analysis. Experiments were designed using RSM-CCD method. The maximum removal efficiency was obtained 78.59% at pH = 5, GFH dosage = 2 g/l, AR18 concentration = 77.5 mg/l and 85 min of contact time. Optimization with RSM and Genetic Algorithm carried out and is similar together. The non-linear adsorption Isotherm and kinetic fitted with Freundlich (R2 = 0.978) and pseudo-second-order (R2 = 0.989) models, respectively. Thermodynamic studies showed that the AR18 adsorption is endothermic process and GFH nature was found spontaneous.

The superior adsorption capacity of 2,4-Dinitrophenol under ultrasound-assisted magnetic adsorption system: Modeling and process optimization by central composite design.

2,4-Dinitrophenol (DNP) was listed as a priority pollutant; accordingly, DNP-contaminated effluent must be treated before discharging to the receiving resources. In the present study, the hybrid ultrasound-assisted GO-Fe3O4 system was employed to decontaminate DNP solution. Ultrasound irradiation makes the mass transfer of adsorbate improved and Fe3O4 enables GO separation from liquid phase under external magnetic field. The as-synthesized GO-Fe3O4 composite was characterized by SEM, TEM, XRD, FTIR, BET and VSM. A response surface methodology based central composite design (RSM-CCD) was used to estimate and optimize of various variables on DNP removal percentage. Under optimal conditions (pH: 4.45, adsorbent dose: 0.178 g/L, ultrasound frequency: 40.02 kHz and DNP concentration: 50.10 mg/L, maximum adsorption capacity was calculated to be 425.58 mg/g for the ultrasound system, higher than the simple system 309.40 mg/g, indicating the importance of synergistic effect between the ultrasound waves and the adsorption process. The ultrasound-assisted adsorption system showed the better agreement with the Langmuir isotherm (R2 > 0.997), while the results of the stirring system were more consistent with the Freundlich model (R2 > 0.991). The experimental results indicated that the pseudo-second-order kinetic model well fitted by experiment data and rate constant was calculated to be 0.000148 min-1 and 0.000002 min-1 under ultrasound and silent systems, respectively. The rate of desorption under ultrasound was more favorable and reuse of the adsorbent in both systems after 10th consecutive cycles reduced by about 22%. Thermodynamic calculations also confirmed the endothermicity and spontaneity of both systems. Electrostatic attraction, hydrogen bonding, and π -π interactions played key roles during the adsorption of DNP onto the MGO. In conclusion, the outcomes of this study provide valuable information of the ultrasound-assisted GO-Fe3O4 system for practical applications.

Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches.

In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed a low percentage of carbon and organic matter to be incorporated in MSW compost ash. The design of adsorption experiments was performed by Box-Behnken design (BBD), and process variables were modeled and optimized using Box-Behnken design-response surface methodology (BBD-RSM) and genetic algorithm-artificial neural network (GA-ANN). BBD-RSM approach disclosed that a quadratic polynomial model fitted well to the experimental data (F-value = 94.596 and R2 = 0.9436), and ANN suggested a three-layer model with test-R2 = 0.9832, the structure of 4-8-1, and learning algorithm type of Levenberg-Marquardt backpropagation. The same optimization results were suggested by BBD-RSM and GA-ANN approaches so that the optimum conditions for RR198 absorption was observed at pH = 3, operating time = 80 min, RR198 = 20 mg L-1 and MSW compost ash dosage = 2 g L-1. The adsorption behavior was appropriately described by Freundlich isotherm, pseudo-second-order kinetic model. Further, the data were found to be better described with the nonlinear when compared to the linear form of these equations. Also, the thermodynamic study revealed the spontaneous and exothermic nature of the adsorption process. In relation to the reuse, a 12.1% reduction in the adsorption efficiency was seen after five successive cycles. The present study showed that MSW compost ash as an economical, reusable, and efficient adsorbent would be desirable for application in the adsorption process to dye wastewater treatment, and both BBD-RSM and GA-ANN approaches are highly potential methods in adsorption modeling and optimization study of the adsorption process. The present work also provides preliminary information, which is helpful for developing the adsorption process on an industrial scale.

Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling.

In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted to isotherm, kinetic and thermodynamic models. The quantification of adsorbent elements was determined using EDX. XRD analysis was utilized to study the crystalline properties of adsorbent. The FT-IR spectra were taken from adsorbent before and after adsorption to study the presence and changes in functional groups. The constituted composition of the adsorbent was determined by XRF. Also, the ionic strength and adsorbent reusability were explored. The influences of operational parameters like pH, initial pesticide concentration, adsorbent dosage and contact time were investigated systematically. ANN-GA and RSM techniques were used to identify the optimal process variables that result in the highest removal. Based on the RSM approach, the optimization conditions for maximum removal efficiency is obtained at pH = 3, adsorbent dosage = 4 g/L, contact time = 30 min, and initial pesticide concentration = 6.2 mg/L. To accurately identify the parameters of nonlinear isotherm and kinetic models, a hybrid evolutionary differential evolution optimization (DEO) is applied. Results indicated that the equilibrium adsorption data were best fitted with Langmuir and Temkin isotherms and kinetic data were well described by pseudo-first and second-order kinetic models. The thermodynamic parameters such as entropy, enthalpy and Gibbs energy were evaluated to study the effect of temperature on pesticide adsorption.

Diuron degradation using three-dimensional electro-peroxone (3D/E-peroxone) process in the presence of TiO2/GAC: Application for real wastewater and optimization using RSM-CCD and ANN-GA approaches.

The present study investigates the efficiency of a three-dimensional electro-peroxone (3D/E-peroxone) reactor filled with TiO2-GAC in removing diuron from aqueous solution and in the remediation of real pesticide wastewater. The behavior of the system in terms of the effect of independent variables on diuron was investigated and optimized by RSM-CCD and ANN-GA methods. Both approaches proved to have a very good performance in the modeling of the process and determined the optimum condition of the independent variables as follows: initial pH = 10, applied current = 500 mA, supporting electrolyte = 0.07 M, ozone concentration = 10 mg L-1, and reaction time = 10 min. The 3D/E-peroxone process achieved a synergistic effect in diuron abatement and reduced significantly energy consumption, as compared to its individual components. H2O2 concentration generated in the electrolysis system was notably increased in the presence of TiO2-GAC microparticles. The BOD5/COD ratio of the real pesticide wastewater increased from 0.049 to 0.571 within 90 min treatment. Giving to the considerable enhancement of the biodegradability of the wastewater, this study strongly suggests that the 3D/E-peroxone process can be considered as a promising pretreatment step before a biological treatment process to produce intermediates which are more easily degradable by microorganisms.

Bacterial co-infections with SARS-CoV-2.

The pandemic coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has affected millions of people worldwide. To date, there are no proven effective therapies for this virus. Efforts made to develop antiviral strategies for the treatment of COVID-19 are underway. Respiratory viral infections, such as influenza, predispose patients to co-infections and these lead to increased disease severity and mortality. Numerous types of antibiotics such as azithromycin have been employed for the prevention and treatment of bacterial co-infection and secondary bacterial infections in patients with a viral respiratory infection (e.g., SARS-CoV-2). Although antibiotics do not directly affect SARS-CoV-2, viral respiratory infections often result in bacterial pneumonia. It is possible that some patients die from bacterial co-infection rather than virus itself. To date, a considerable number of bacterial strains have been resistant to various antibiotics such as azithromycin, and the overuse could render those or other antibiotics even less effective. Therefore, bacterial co-infection and secondary bacterial infection are considered critical risk factors for the severity and mortality rates of COVID-19. Also, the antibiotic-resistant as a result of overusing must be considered. In this review, we will summarize the bacterial co-infection and secondary bacterial infection in some featured respiratory viral infections, especially COVID-19.

Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network.

The present study investigates the synergistic performance of the sonophotolytic-activated ZnO/persulfate (US/UV/ZnO/PS) process in the decolorization of acid blue 113 (AB113) dye from aqueous solution and its feasibility for the treatment of real textile wastewater. Decolorization of AB113 solution was modeled by central composite design-response surface methodology (CCD-RSM) and artificial neural network (ANN) approaches and optimized by CCD-RSM and genetic algorithm (GA) approaches. Statistical metrics indicated that both CCD-RSM and ANN approaches seemed satisfactory. However, the results of statistical fit measures indicated a relative superiority of CCD-RSM as compared to the ANN approach. The results of optimization of the process parameters by CCD-RSM and GA approaches appeared to be similar as follows: pH = 6.1, reaction time = 25 min, US power density = 300 W/L, ZnO = 0.88 g/L and PS = 2.43 mmol/L. The synergistic effect of the hybrid US/UV/ZnO/PS process in comparison with its individual processes (US, UV, ZnO, and PS) was found to be 54.3%. Quenching experiments discovered that and HO are the main oxidizing radicals in a mildly acidic condition of the reaction solution. The removal efficiency of AB113 in the presence of some anions decreased in the order of bicarbonate > sulfate > phosphate > nitrate > chloride. Further, the reusability feasibility of ZnO showed that the ZnO material retained its photocatalytic property after five successive cycles of reusability test, while Zn2+ ion concentration in the reaction solution was measured to be 2.81 mg/L. The findings also indicated that the integrated process application suppresses extremely chemical and electrical costs. The study of the feasibility of the US/UV/ZnO/PS process in the treatment of real textile wastewater was done by determining COD, TOC and BOD5/COD ratio. Results demonstrated that the 96.6 and 97.1% reduction of COD and TOC was achieved after 5 and 7 h reaction time, respectively. The obtained BOD5/COD ratio changed from about 0.15 (for non-treated wastewater) to about 0.61 with increasing reaction time from zero to 90 min. In conclusion, the hybrid US/UV/ZnO/PS system can be proposed as a novel and promising approach to be utilized as a pretreatment technique before a biological treatment process to facilitate the biological treatment of recalcitrant textile wastewater.

Evaluation of carcinogenic risks related to nitrate exposure in drinking water in Iran.

Nitrate is one of the most important contaminants that can release into the environment predominantly as a result of anthropogenic processes. Excessive intake of nitrates may increase the risk of certain types of cancer. The aim of this study was to investigate the concentration of nitrate in drinking water and its health to people in Iran. This cross-sectional study has performed in 2019. Nitrate concentrations in drinking water supplies were obtained from peer-reviewed publications. Monte Carlo stimulations and mathematical models were used to determine the excess cancer risk. Risk level for assessing the carcinogen risk was 10-5 (1 per 100,000 persons). Nitrate concentrations and cancer risk related to nitrate were classified by GIS software. According to the obtained results, the drinking water supplies of Tehran, Mashhad (Khorasan Razavi), Zahedan (Sistan and Baluchestan), Shiraz (Fars), Qom, Ardabil and Ahwaz (Khuzestan) have higher nitrate concentrations than the limit recommended by WHO and Institute of Standards and industrial Research of Iran (ISIRI). The estimated cancer risks for the provinces of Tehran, Mashhad (Khorasan Razavi), Zahedan (Sistan and Baluchestan), Shiraz (Fars), Qom, Ardabil and Ahwaz (Khuzestan) were in the no negligible range set by the Health Canada and WHO. The majority of Iran provinces that have impermissible level of nitrate in drinking water supplies had a significant association between cancer prevalence and nitrate exposure. •The findings demonstrated that carcinogen risk values of nitrate exposure through drinking water was 0.001%.•Results showed that Tehran, Mashhad (Khorasan Razavi), Zahedan (Sistan and Baluchestan), Shiraz (Fars), Qom, Ardabil and Ahwaz (Khuzestan) are more exposed to additional cancer risk related to nitrosamine.•The results of this study is considered as the comprehensive report that indicate the association between gastrointestinal cancer and nitrate exposure through drinking water.

The efficiency of UV/S2O8 2- photo-oxidation process in the presence of Al2O3 for the removal of dexamethasone from aqueous solution: kinetic studies.

This study aimed to investigate the efficiency of the UV/S2O8 2- photocatalytic process in the presence of Al2O3 nanoparticles for the removal of dexamethasone from aqueous solution. In this experimental study, the variables pH, persulfate concentration, initial concentration of dexamethasone, the catalyst dose were studied in order to investigate the process efficiency. Furthermore, the efficiency of UV/S2O8 2- in the presence and absence of catalyst was investigated. The Al2O3 nanoparticle catalyst was characterized using X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) analyses and scanning electron microscopy (SEM) image. The results showed that a decrease in pH and the initial concentration of dexamethasone increased the process efficiency. Given the increased concentrations of the persulfate and Al2O3, the removal efficiency was partially increased. In UV/S2O8 2-/Al2O3 under optimum conditions (pH = 3, t = 30 minutes, dexamethasone concentration = 20 mg/L, 0.5 mM of persulfate, and UV radiation = 55 watts), 94% of the dexamethasone was removed. The kinetic response showed that the reaction data corresponded to the pseudo-first-order kinetic model. The results showed that the UV/S2O8 2- photochemical process can efficiently remove dexamethasone from aqueous solution in the presence of Al2O3 catalyst and the mineralization efficiency reached about 98%. Therefore, this process is recommended due to its high efficiency and availability for the removal of pharmaceutical compounds.

Optimized synthesis of carbon-doped nano-MgO and its performance study in catalyzed ozonation of humic acid in aqueous solutions: Modeling based on response surface methodology.

This research study focused on the optimization of the synthesis of carbon-doped nano-MgO (C-MgO) and the investigation of its catalytic capacity in a catalytic ozonation process (COP) for the removal of humic acid (HA). Characterization analyses, including SEM, EDX, XRD, BET, and photoluminescence test showed that the C-MgO was successfully synthesized. L8 orthogonal arrays according to the Taguchi methodology optimized the synthesis of the C-MgO as follows: sucrose to MgO ratio = 0.5, sonication time = 15 min, calcination temperature = 400 °C and pH = 10.5. A central composite design based on response surface methodology was employed to optimize and model the COP in the removal of HA. A quadratic polynomial model with p-value < 0.0001 and R2 = 0.9988 showed a better fit to experimental responses. The optimum levels of the studied parameters in the COP based on the predictive model were obtained as follows: pH = 9.5, reaction time = 12 min, catalyst dose = 1 g/L, and HA concentration = 5 mg/L. The HA mineralization was determined to be 86.8% at the 100 min reaction time. Additionally, the COP exhibited 34% synergistic effect and the kinetic rate constant of 0.1898 min-1 in the HA removal. The presence of tert-butanol, methanol, salicylic acid, and some anions did not significantly affect the removal of the HA in the COP. From a practical view, this report indicated that the C-MgO catalyst could be potentially applied in the COP for the treatment of the water having high concentrations of HA substances.

Thermochemical degradation of furfural by sulfate radicals in aqueous solution: optimization and synergistic effect studies.

In this study, thermochemical degradation of furfural by sulfate radical has been investigated to find the best-operating conditions. For this purpose, the response surface methodology (RSM) based on central composite design (CCD) was applied to optimize the five independent variables of thermally activated persulfate (TAP)/nZVI oxidation process including pH, PS concentration, furfural concentration, nZVI dosage, and heat. The ANOVA results ("P > F value" < 0.0001 and [Formula: see text] = 0.9701) showed the obtained quadratic model is acceptable to predict furfural removal. Based on the reduced quadratic model PS concentration, nZVI dosage, and heat revealed the positive effects on removal efficiency, while pH and furfural concentration had a negative effect. Accordingly, 98.4% of furfural could be removed within 60 min of reaction under the optimum conditions: pH 5.26, PS concentration of 20.52 mM, furfural concentration of 84.32 mg/L, nZVI dosage of 1.15 mg/L, and a temperature of 79 °C. In such circumstances, the furfural removal efficiency for TAP, PS/nZVI, PS, and nZVI was 94.5, 9, 3, and 2%, respectively. Therefore, based on the synergy index (SI) values, the combination of PS, nZVI, and heat can lead to a synergistic effect in the performance of the thermochemical process.

The stinging Apidae and Vespidae (Hymenoptera: Apocrita) in Iranian islands, Qeshm, Abu-Musa, Great Tunb and Lesser Tunb on the Persian Gulf.

OBJECTIVE: To study the stinging flying Hymenoptera (Apidae and Vespidae) fauna in four Iranian Islands, Qeshm, Greater Tunb, Lesser Tunb and Abu-Musa on the Persian Gulf. METHODS: The flies were captured by used of Malaise trap, fly trap, bottle trap and insect net-hashing from March 2011 to July 2012. RESULTS: In this study, 11 species of stinging Hymenoptera were reported for the first time in Persian Gulf region. CONCLUSIONS: Some of this species such as Vespa orientalis and Polistes olivaceus are more common in the Persian Gulf islands and can cause clinical problem to islands resident and travelers.