مجله محیط زیست و مهندسی آب
سال نهم شماره 2 (تابستان 1402)

Organophosphorus compounds are extensively used worldwide as pesticides which cause great hazards to human health and the environment. The aim of this study was to investigate the removal of diazinon insecticide from aqueous solution by using polypyrrole (PPy) onto polyvinyl alcohol (PVA) adsorbent in a batch system. Investigation of the adsorbent structure was performed by SEM and FTIR analyses. The effect of experimental parameters on adsorption such as pH, contact time, initial diazinon concentration, and initial adsorbent mass was studied. According to the experiments, the optimal pH of diazinon adsorption by PPy/PVA adsorbent at 20 °C was 6. As the contact time increased, the adsorption efficiency increased and reached equilibrium (98%) after 10 min. The adsorption capacity of diazinon increased with increasing adsorbent mass and reached its highest value in the adsorbent mass of 0.025 g in 10 ml of aqueous diazinon solution. The kinetics study showed that the data followed by Morris–Weber and pseudo-second-order equations. The equilibrium data also show good agreement with the Freundlich isotherm. Based on the results of the present study, PPy/PVA adsorbent has a high potential for diazinon pesticide removal and it can be suggested as a cheap, efficient and available adsorbent for diazinon pesticide removal from aqueous solutions. The maximum adsorption capacity of mercury by polymer composite was 23.37 mg/g.

By using photocatalytic processes, it is possible to reduce volatile organic compounds in the air under special conditions. The aim of this study was to evaluate the efficiency of the photocatalytic process in the presence of organic-metallic nanostructures model Bi2S3@NH2-MIL125(Ti) in reducing ethylbenzene concentration. The concentration of 150-300 ppm and the amount of deposited catalyst (1-3 g/m2) were studied. By increasing the airflow rate contaminated with ethylbenzene, the efficiency of the process decreased from 79% at 0.5 l/min to 41% at the flow rate of 3 l/min. By increasing the amount of catalyst coated on the reactor wall, the efficiency of the process increased from 65% at a dosage of 0.5 g/m2 to 86% at a dosage of 3 g/m2. By increasing the amount of input concentration. Of ethylbenzene, the efficiency of the process decreased from 94% at a concentration of 150 ppm to 55% at a concentration of 300 ppm. The optimal process conditions were determined at a flow rate of 0.5 l/m, the concentration of ethylbenzene, 150 ppm, and the amount of catalyst, 3.0 g/m2. The presence of different light sources including UV A, C, and visible light could reduce the initial concentration of ethylbenzene by.94, 95, and 67% respectively.

The presence of pharmaceutical wastewater can pose significant challenges to the environment. Since conventional wastewater treatment processes are not efficient for the complete separation of drug materials, solvent extraction through a hollow fiber membrane contactor could be a promising alternative. In this study, a developed polysulfone (PSF) hollow fiber membrane was fabricated using a non-solvent phase separation (NIPS) method to extract penicillin G from aqueous solutions in the membrane contactor system. From the characterization experiments, the prepared polysulfone membrane demonstrates an outer surface contact angle of 69.6°, critical water entry pressure (CEPw) of 250 kPa, total porosity of 72.2%, and collapsing pressure of 500 kPa. The extraction of penicillin G from the aqueous phase was performed with a 5% w/w solution of Aliquat 336. The effect of the operating parameters on the extraction flux of penicillin G was investigated using the response surface method (RSM). The optimum penicillin G flux of 1.46×10-3 kg/m2s was found at an operating pressure of 100 kPa, an aqueous phase flowrate of 70.5 ml/min, and an organic phase flowrate of 200 ml/min. Therefore, the developed PSF hollow fiber membrane contactor can be considered a proper choice to remove antibiotics (penicillin G) from aqueous solutions.

In this study, hydroxyapatite-supported zero-valent iron nanoparticle was synthesized by the sodium borohydride reduction method. To evaluate the performance of adsorbent for the removal of Cu(II) and Ni(II) ions, the influence of different sorption parameters, such as contact time, temperature, initial concentration of metal ions, the dosage of adsorbent, and pH value of the solutions were investigated. The highest removal efficiency of both metals occurred under the optimal conditions of 7, 45 min, 0.1, 50 ºC, and 5 mg/l for pH, contact time, adsorbent mass, temperature, and initial concentration, respectively. The kinetic and equilibrium data were well fitted by the pseudo-second-order model and Langmuir- Freundlich model, respectively. The maximum adsorption capacities of adsorbent towards Cu(II) and Ni(II) were 138 and 108 mg/g, respectively. The results indicated that the adsorbent could remove the majority of metals (95%) within 40 min without pH control. Thermodynamic parameters, i.e., ΔG°, ΔH°, and ΔS°, indicated that the sorption process was spontaneous and thermodynamically favorable.

Among the important parameters in water quality, are the amount of turbidity and the depth of light penetration in water. One common way to determine water turbidity is to use a Secchi disk, but this method is time-consuming and expensive, so an alternative method should be considered. Deep learning methods can play an important role in this field. The purpose of this study was to classify water quality based on turbidity and Secchi disk depth using a convolutional neural network method implemented in a Python programming environment. For this purpose, a simulated reservoir was used in the laboratory and the turbidity was increased step by step by increasing the clay in the reservoir water. Simultaneously with measuring the depth of the Secchi disk and water turbidity, the samples were imaged. These images were given to the convolutional neural network together with the obtained data. The results showed that the convolutional neural network with 300 epochs, can estimate the water quality class with 95% accuracy and 93% kappa statistic, and it has only a 5% error rate.

Controlling the excess flow energy when using the inclined chute to transfer water is always one of the important hydraulic issues. In the present study, the effect of determining how the shape of the blocks and the longitudinal distance between them affects the energy dissipation under various angles of chutes was evaluated experimentally. The experiments were carried out on three chutes with angles of 20, 23, and 26.6 degrees, five different geometric shapes of blocks, and three different longitudinal distances of 1.5H, 2H, and 2.5H (H is block height). The results indicated that the energy dissipation rate on smooth chute models was between 21.38 to 66%. The baffles on the chute create the flow resistance, and the difference between the energy dissipation rate in baffled chute models compared to smooth chutes is between 21 and 61%. By increasing the angle of the chute from 20 to 26.6 degrees, the energy dissipation on smooth and baffled chutes decreases by 22 and 6%, respectively. Increasing the longitudinal distance between the blocks reduces energy dissipation in all models. For the superior model, a 66% increase in the longitudinal distance between the blocks decreases the energy dissipation rate by 10%.

Estimation of flow energy loss in gabion spillways can be effective in managing erosion downstream of structures, flood control, and riverbed stabilization. Therefore, in this research, using two soft computing models evolutionary polynomial regression (EPR) and multivariate adaptive regression spline (MARS), the amount of energy loss in these spillways was estimated. About 75% of the 74 laboratory data samples were used for training and the remaining 25% were used for testing the models. The dimensionless parameters of Froude number (Fr), spillway slope (S), gabion number (GN), and porosity (n) were used as input parameters. The results showed that the MARS model predicted the energy loss values by root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) of 0.05, 0.017, and 0.99, respectively, which has better performance than the EPR model has. The results of the Taylor diagram also showed that the performance of MARS and EPR are satisfying, and their accuracy is very close to each other. The regression equation by the EPR model was more complex than the regression equation by the MARS model. According to the obtained results, the use of the two soft computing models in estimating energy loss in spillways is recommended.

In the present research, three data-driven models including M5P, REP tree, and random forest were used to estimate daily reference evapotranspiration. The abilities of these three models to estimate reference evapotranspiration were studied in single and combined modes. To this end, the daily meteorological data of five synoptic stations in Kerman province in the period from 2000 to 2020 were used. A combination of meteorological variables, using sensitivity analysis versus the reference evapotranspiration values ​​obtained from FAO-Penman-Monteith, was considered as input for each of the mentioned models. Finally, the accuracy of the mentioned models and empirical methods in estimating the evapotranspiration of the reference plant were compared using statistical indicators, and the superior model was selected. The results of validation data showed that the M5P model in the form of individually (RMSE = 0.083 and NS = 0.998 in Bam station) and the weighted averaging in the form of the ensemble (RMSE = 0.155 and NS = 0.994 in Bam and Sirjan stations) in all stations had better results for estimating evapotranspiration rates than other methods. In general, tree models, especially M5P, had better results in estimating daily evapotranspiration than empirical models.

Flow resistance in rivers with vegetation patches is one of the challenging parameters in river engineering. This study aimed to investigate the effect of vegetation patches on flow resistance in Shapur, Fahlian, and Dalaki rivers. The data measured in this study include velocity measurement, surveying, and bed sampling. In this study, considering the simplifying assumptions, the equation for the flow resistance due to vegetation patches was derivated. Accordingly, the flow resistance due to vegetation patches was obtained by the difference in the flow resistance due to bed from the value of the total flow resistance for each section. The results show that the average percentage of vegetation patches’ contribution from the total flow resistance is 44%, which shows the significant effect of vegetation patches on flow resistance. In addition, an investigation of 71 measured velocity profiles showed the phenomenon of Dip in the velocity profiles near the vegetation patches, and by moving away from the vegetation patches, the effect of this phenomenon is reduced and the profiles become S-shaped.

The saline water intrusion to aquifers in arid regions is one of the major challenges in the development of water resources. In this research, the risk of saline water intrusion near a salt playa in Sirjan plain was evaluated by modifying and optimizing the GALDIT model. Parameters such as aquifer type, aquifer hydraulic conductivity, groundwater level, distance to salt playa, current status of saline water intrusion and aquifer thickness were used to zoning the risk of saline water intrusion. To modify and optimize the GALDIT model, single parameter sensitivity analysis method, analytical hierarchy process and fuzzy logic were used. According to the different models results, the Sirjan plain has been classified into three saline water intrusion risk zones namely: low, moderate and high. Groundwater electrical conductivity and chloride ion concentration were employed for the validation of the results of the models. Achieving results show that the fuzzy logic is the best method for optimizing the GALDIT model. So that the correlation coefficients between the results of Fuzzy GALDIT model results and parameters of electrical conductivity and chloride ion concentration are equal to 0.88 and 0.90 respectively