Search

ABSTRACTThis study simulated the effects of land-use changes on runoff pollution in a catchment of Tehran, Iran. The catchment has a semi-arid climate, a relatively long period from dry summer to rainy autumn and highly varying precipitation levels. Urban runoff samples were collected from six stations including five different land-based activities and a mixed land-use, which supported all land-use types. Event Mean Concentration (EMC) was applied to determine the Nonpoint Source (NPS) pollution of urban runoff such as Total Suspended Solid (TSS), Total Nitrogen (TN), Total Phosphorus (TP) and heavy metals, including zinc (Zn), copper (Cu) and lead (Pb), in five land-uses. Sampling was performed during five events with different Antecedent Dry Days (ADDs) in time period 2019-2020. The Storm Water Management Model (SWMM) was employed to simulate the quality of urban runoff base on measured EMCs in each event for the different parameters under investigation and the average of measured EMCs. The results illustrated that that the maximum and minimum amount of EMCs were pertained to the events with 115 and 1 dry days respectively. While, daily simulation of runoff pollution based on measured amount of EMCs in the catchment indicated that the calibration criteria such as Root Mean Square Error (RMSE), Normalized Objective Function (NOF), Nash-Sutcliffe Efficiency (NSE) and R2were very good for the minimum amount of EMCs and satisfactory for the average of EMC`s calculated, NSE values was not satisfactory for the maximum amount of EMCs. Uncertainty of ADDs can significantly affect the results of the maximum EMC simulation. This study also found higher TSS and TP concentrations in runoff in open space land-use and significantly higher heavy metals and TN concentrations in runoff in the industrial land-use compared with other land-use types in the catchment.

In this study, two different initial concentrations of Chlorella vulgariswere injected into two types of dairy wastewater (referred to as wastewater and effluent, respectively) at a percentage of 25, 50, and 75% (v/v), and nutrient removal, microalgae growth and lipid accumulation were examined throughout cultivation (wastewater = before purification and effluent = after Purification). Different concentrations of injected algae, the type of applied wastewater, and different dilution ratios had an important effect on nutrient removal and microalgae growth. The highest amount of nutrients and algal cell number in the initial dose of algae (13 million cells/ml) and mediums of effluent and in wastewater related to dilutions (25, 50, and 75%) respectively. Concerning lipid accumulation in Chlorella vulgaris, the highest concentration was observed in effluent and wastewater at a dilution of 50% and 75%, respectively. In general, it can be concluded that wastewater is more suitable than effluent for lipid production. The aim of this study is production biofuels by biomass and low cost.

Due to the lack of natural supply of clean water, the increasing population growth, the ever-increasing expansion of the discharge of industrial units and the pollution of surface and underground water sources, the use of salt water desalination systems and the sea water has increased, which despite the many benefits, and the environmental effects caused by the effluent of the desalination facilities create problems. Therefore, to overcome some of the mentioned problems, microalgae cultivation systems are able to increase the removal and removal of nutrients, especially nitrogen, phosphorus, heavy metals, and organic materials from wastewater. This study were studied for bioremediation and to evaluate the removal efficiency of nitrate, phosphate, and total iron at zero middle and final times by Scenedesmussp. and Arthrospira platensismicroalgae grown in the effluent of desalination plants of Bandar-e-Turkmen unit, Iran with the concentrations rate (50 and 100%). The amount of nitrate, phosphate, and total iron in the effluent has decreased during the experiment. The highest percentage of removal efficiency and the slope of reduction (nitrate and phosphate) in 100% concentration of wastewater, respectively, during the (zero-middle and middle-end) times, 72.85%, 82.95%, respectively, related to Scenedesmussp. It showed that Arthrospira platensisin 50% concentration of wastewater during the (middle-end) times has the highest phosphate removal efficiency of 78.59%. The highest percentage of total iron removal efficiency in Arthrospira platensiswas observed during the middle-final times in the effluent of 100% concentration (100%). Microalgae Scenedesmussp. showed a better performance than Arthrospira platensisin removing nitrate and phosphate in 100% wastewater concentration, while in 50% wastewater concentration, S. platensisshowed a high efficiency in phosphate removal and removal of iron with S. platensisin 100% wastewater concentration. It showed great efficiency.

One of the critical consequences of drought is the reduction of water resources and reduction of agricultural production. Therefore, it is essential to evaluate the relationship between meteorological drought and vegetation. To investigate this relationship, meteorological data from 28 rain gauge and remote sensing stations located in Lorestan Province and its neighboring regions were used in this study. First, the standardized precipitation index (SPI) was calculated between 1987 and 2017 using meteorological data, and then, the vegetation health index (VHI) was calculated using satellite images for the same years. The correlation between SPI and VHI was computed by Pearson’s correlation coefficient. The results showed that the highest Pearson’s correlation coefficient was 0.77, belonging to the SPIs for October and November with 9- and 12-month time periods. Multivariate linear regression was also performed between the SPI and vegetation health index (VHI), and the results showed that SPI was significantly correlated with VHI at a 5% level over 9- and 12-month periods. Finally, a confusion matrix was used to evaluate the compatibility of the SPI and VHI drought classes.

The main objective of this experimental work is to investigate the effect of partial replacement of cement with limestone powder on the permeability of concrete, especially in severe curing conditions. For this evaluation, 5%, 15% and 30% cement of concrete mix design were replaced by limestone powder and two different curing conditions applied to the concrete samples. Some of the concrete specimens were submerged in seawater till the age of the test, whereas others were cured in standard conditions. A series of tests, including the Rapid Chloride Penetration Test (RCPT), water penetration test under pressure, and water absorption test were carried out at ages 7, 28 and 90 days to evaluate the permeability of concrete containing limestone. The results show that the penetration of chloride ion increases with the increase in limestone content in concrete. For seawater conditions, samples with 5% and 15% limestone had less chloride permeability than the control sample. For concrete with 30% limestone powder, the permeability significantly increases for both curing conditions. In the normal conditions, water penetration depth and water absorption increased with the increase in limestone content. This trend was observed for the water penetration depth of samples in seawater, but like RCPT results, the water absorption of samples containing up to 15% had less permeability to the control sample. The compressive strength of all specimens cured in seawater was reduced compared to the standard curing condition.

Bedload transport due to approved complexity and challenges has been the subject of different modeling approaches. Due to imprecise of the empirical equations, the potencies of the intelligent techniques in developing more accurate bedload predictive models have been highlighted. In this paper, an optimum hybridized artificial neural network (ANN) with firefly metaheuristic algorithm (FA) through a dynamic setting parameter approach was developed and introduced. The model was applied on 879 datasets including 5 dominant parameters of bedload transport (discharge, flow velocity, slope, depth, mean grain size) from 19 gravel-bed streams of Idaho- USA. Detailed analyses using different analytical error metrics as well as comparison with several empirical equations showed an improved R2-value from 0.1 in empirical equation to 0.95 in hybrid model. The assessed performances of applied model demonstrated for 6.03% progress in ANNand at least 63.08% in empirical equations. According to observed results, the hybrid model with 84.65% accuracy was outperformed than others in providing closer and more compatible outputs to measurements. Referring to carried out sensitivity analysis, the discharge and velocity were identified as the most effective factors on predicted bedload.

Concrete is one of the essential construction materials that requires many natural resources for production and releases much pollution such as greenhouse gases. As a cement replacement material, fly ash is an essential tool to reduce the environmental impacts of cement production. The main objective of this study was to evaluate the bond strength between concrete and reinforcement for different fly ash levels. In the experiments, 5%, 15%, and 25% of Portland cement were replaced by fly ash, while the water-to-binder ratio (w/b) was kept constant at 0.42. Standard test specimens of 15 cm containing a 16-mm-diameter bar at the center were used in bond strength tests. Bond strength was measured at 7, 28, and 90 days of age through a direct pull-out test. The results show that adding fly ash with any percentage reduces the bond strength at an early age while replacing 15% cement with fly ash has more positive effects as it enhances bond strength up to 20% in the long term. Evaluation of existing models for predicting bond strength also showed that these models' average provided greater bond strength than experimental results in 28 days. Therefore, it is necessary to provide an appropriate model for estimating the bond strength of concrete containing fly ash.

ABSTRACTThe occurrence of local scour around bridge piers or abutments is one of the major reasons for the destruction of bridges. Therefore, this phenomenon should be prevented or at least minimized using some methods, namely using collars in recent years. In the present paper, by proposing a new approach and developing an experimental model of partial collars (collars not fully encircling the abutment), the effect of length and width of collars on reducing scouring and sedimentation pattern around the bridge abutments with vertical walls were investigated under different conditions. Moreover, the obtained results were compared with the results of models with full collars. The results revealed that collar dimensions could have a great impact on its efficiency. In partial collars, if only the upstream face of the abutment is protected and there is no protrusion toward the downstream, there will be no high and acceptable efficiency. However, increasing length and width, its positive efficiency will be enhanced. Although increasing the length of a collar of this type to more than 0.6 times the length of the abutment diminishes the scouring around the bridge abutment, increasing its width not only does reduce the scour depth but also produces a much greater effect on the location of the scouring hole to be farther away from the downstream of the abutment. Furthermore, the efficiency of full collars is also enhanced as their length and width increase. Comparison of the results shows that the efficiency of full collars is less than that of partial collars, despite their greater cross-sectional area. Therefore, partial collars are more cost-effective than full collars due to their lower cross-sectional areas. The average percentage of scour depth reduction for all four flows for different models in the erodible bed and at the base point ranged from 24 to 68% and 24 to 97%, respectively.

ABSTRACTThe aim of this research was to evaluate the antibacterial activity of the muscle wall extracts of sea cucumber, Stichopus horrens, against Lactococcus garvieae, Streptococcus iniae, Aeromonas hydrophila, and Yersinia ruckeribacteria in rainbow trout using the disk-diffusion and well-diffusion methods. In this study, nine sea cucumbers (with the mean weight of 1690 ± 12.18 g) were randomly obtained from two locations in Chabahar Bay, Iran in November 2018. The muscle wall of the body was extracted with each of the ethyl acetate, methanol, and acetone organic solvents. The antibacterial activities of extracts were determined. Only the ethyl acetate extracts of S. horrensin the concentrations at 8 and 12 mg/mL had an inhibitory effect on all the examined bacteria. Y. ruckeriand A .hydrophilabacteria were sensitive to the acetone extracts. The best property was recorded with the S.horrens ethyl acetate extracts, with the MIC value of 0.625 mg/mL against S. iniaeand L. garvieae. The MIC values ranging from 0.626 to 1.25 mg/mL were also displayed with the acetone extracts against Y. ruckeriand A. hydrophila, respectively. In conclusion, the ethyl acetate extracts of S. horrensdisplayed the best spectrum of bactericidal effect obtained from four bacteria strains examined.

Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.

Damage detection in concrete gravity dams using Hilbert-Huang Method, as one of the most common signal processing techniques, is studied in this research. After considering a typical geometry for dams, damage is modelled by a reduction in the modulus of elasticity in the dam’s body (in three levels of damage) and in different areas of the structure. The dam is excited by a horizontal earthquake and the primary natural frequencies of the dam are calculated by applying Hilbert-Huang Method to the measured response, which is the acceleration of five points in the system. Based on the changes in the frequencies of the damaged and undamaged structure, a parameter, called relative frequency error, has been introduced. The results show that the proposed criterion used in this study can not only properly identify the location of damage but also predict the severity of the structural damage in concrete gravity dams accurately.

In the present research, external validation criteria and uncertainty analysis are conducted to predict maximum scour depth at downstream of the stilling basins using evolutionary polynomial regression (EPR) and model tree (MT) and nonlinear regression (NLR). Data sets used to develop models were collected from experimental models of stilling basins. Effective parameters on the scour depth were included those of discharge, approaching flow depth, mean flow velocity, geometric characteristics of stilling basins and physical properties of bed sediments at downstream of stilling basins. At first, MT and EPR models are implemented based on input–output data sets and result of performances for training and testing stages are evaluated. In addition, external validations and uncertainty analyses on the formulations given by EPR and MT are carried out using statistical errors functions and have absolute mean prediction errors less than that NLR technique. Finally, results of uncertainty and the prediction errors are compared with the GEP, MT, EPR and NLR models to select GEP model as a reliable model with the highest accuracy and smaller errors than other models.

This paper presents a new meshless numerical scheme to overcome the problem of shock waves and to apply boundary conditions in cases of dam-break flows in channels with constant and variable widths. The numerical program solves shallow water equations based on the discrete mixed subdomain least squares (DMSLS) meshless method with collocation points. The DMSLS meshless method is based on the minimization of a least squares functional defined as the weighted summation of the squared residuals of the governing equations over the entire domain and requiring the summation of residual function to be zero at collocation points in boundary subdomains. The collocated discrete subdomain meshless method is applied on the boundary, whereas the collocated discrete least squares meshless technique is applied to the interior domain. The meshless scheme extends for dam-break formulation of shallow water equations. The model is verified by comparing computed results with analytical and experimental data for constant and varying width channels. The developed model is also used to study one-dimensional dam-break problems involving different flow situations by considering changes to the channel width, a bumpy channel with various downstream boundary conditions, and the effects of bed friction and bed slope as source terms on wave propagation. The accuracy of the results is acceptable. [ABSTRACT FROM AUTHOR]

Recent investigations have demonstrated global sea level rise as being due to climate change impact. Probable changes in sea level rise need to be evaluated so that appropriate adaptive strategies can be implemented. This study evaluates the impact of climate change on sea level rise along the Iranian south coast. Climatic data simulated by a GCM (General Circulation Model) named CGCM3 under two-climate change scenarios A1b and A2 are used to investigate the impact of climate change. Among the different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves are selected for predicting sea level rise by using stepwise regression. Two Discrete Wavelet artificial Neural Network (DWNN) models and a Discrete Wavelet Adaptive Neuro-Fuzzy Inference System (DWANFIS) are developed to explore the relationship between selected climatic variables and sea level changes. In these models, wavelets are used to disaggregate the time series of input and output data into different components. ANFIS/ANN are then used to relate the disaggregated components of predictors and predictand (sea level) to each other. The results show a significant rise in sea level in the study region under climate change impact, which should be incorporated into coastal area management.