Swift preferential water flow through macropores can rapidly pollute groundwater, spreading agricultural and industrial contaminants and threatening water security and ecosystems. To improve the simulation of pollutant transport in soil, a software package based on the kinematic-dispersive wave van Genuchten (KDW-VG) model combined with the particle swarm optimization (PSO) method was used to simulate preferential water flow through an unsaturated soil matrix. The KDW-VG model evolved from the KDW model, replacing the KDW model's power-law function with the more physically robust Mualem-van Genuchten framework. However, existing models often require detailed measurements of water flux versus mobile water content, which limits their applicability under field conditions. In this research, observed data from four rainfall intensities from 55.58 to 160.49 (mm h-1), were used to calibrate both the KDW and KDW-VG models. The hydrographs from a soil column with artificial macropores were recorded to calibrate both models. Using the PSO inverse method, unknown parameters were determined by minimizing the error between observed and simulated hydrographs. The finite-difference technique was used to solve both models. The results showed that the KDW-VG model fit the observations more closely, because of the replacement of the power-law function with the Mualem-van Genuchten framework. The dispersive effect was higher at lower rainfall intensities. Overall, the KDW-VG model's parameters exhibited less sensitivity to rainfall variations, which is a key advantage. This research advances computational techniques for modelling mass transfer in environmental systems, specifically addressing preferential water flow and pollutant transport. By improving the accuracy of pollutant transport models while requiring less detailed input data, the method can be applied under field conditions to provide more reliable predictions. Future work will test the model under field conditions, extend it to varied soils, and integrate realistic macropores using advanced imaging and computation.

Air pollution is one of the most pressing issues in populated Middle Eastern cities, in particular for the city of Ahvaz, Iran, imposing deleterious effects on the environment, public health, economy, culture, and other sectors. In this study, we investigate the relationship between meteorological parameters, PM10, AOD, air mass source origin, and visibility during severe desert dust storms (Average3h PM10 > 3200 µg m−3) between 2009 and 2012. Six of seven such events occurred between February and March. Interestingly, for the seven cases there was always an alarming PM10 mass concentration peak (137–553 µg m−3) between 12:00–18:00 (local time) that was 18–24 h before the dominant peak of the storm (3279–4899 µg m−3). The maximum wind speed over the multi-day periods examined for the dust storms is usually observed 6 h before the alarming PM10 peak. The minimum relative humidity, dew point temperature and air pressure occurred ± 3 h around the time of the alarming PM10 peak. Wind speed was the meteorological parameter that was consistently higher around the time of the first peak as compared to the second peak, with the reverse being true for sea level pressure. Based on four years of daily data in Ahvaz, PM10 was positively correlated with wind speed and air temperature and inversely correlated with sea level pressure and RH. An empirically-derived equation with R2 = 0.95 is reported to estimate the maximum PM10 concentration for severe desert dust events in the study region based on meteorological parameters. Finally, AOD is shown to correlate strongly (R2 = 0.86) with PM10 during periods with severe desert dust storms in the region.

The biochemical oxygen demand (BOD5) could be used as an indication of wastewater treatment quality, but measuring BOD5 is very time-consuming and costly. Ahvaz wastewater treatment plant (A-WWTP) plays a pivotal role in reducing the input load to the Karun River and it is very important to check its efficiency. Thus, the most critical parameters affecting the BOD5 were determined using the linear regression and stepwise method. The capability of the multivariate linear regression model (MLR), feed-forward artificial neural network (FF-ANN), and adaptive neuro-fuzzy inference system (ANFIS) were investigated with different architectures and inputs to predict the effluent BOD5 of A-WWTP (for daily and monthly modes). These architectures had two, three, four, or five inputs. The results of the MLR revealed that the maximum correlation coefficients (R) for training and testing were 0.916 and 0.864 (daily), and 0.809 and 0.793 on a monthly basis, respectively. The maximum R in FF-ANN for training and testing was 0.960 and 0.906 (daily basis), and 0.921 and 0.849 (monthly basis), respectively. Meanwhile, the maximum R in ANFIS for training and testing was 0.980 and 0.933 daily, and 0.968 and 0.927 monthly, respectively. The results indicated that the three models are appropriate, but the ANFIS is a more accurate model. In addition, based on conditions and available wastewater qualitative parameters, all of the architectures can be used to estimate the output BOD5.

Microwave torrefaction of oat hull was conducted to enhance its physicochemical properties. A bench-top reactor with an internal stirrer was used for oat hull pretreatment at temperatures of 225 °C, 255 °C, and 285 °C, and residence times of 3, 6, and 9 min, respectively. Results showed that a high temperature level at 3 min residence time or severe torrefaction increased calorific values by up to 35% of its original value, while decreasing mass yield down to 60.77%. Severe torrefaction further decreased moisture absorption, moisture content, and grinding energy consumption but decreased energy yield and bulk density. Residence time had no significant effect on biomass physicochemical changes; however, production cost may be significantly increased by longer residence times. It was also concluded that increased microwave power levels from 400 to 650 W decreased energy consumption by shortening processing times, resulting in a positive economic impact of the process. Moderate and severe torrefaction significantly enhanced biomass fuel properties, and short residence times are recommended in order to decrease electricity consumption. In addition, microwave pretreatment enhances biomass in a similar way to conventional torrefaction, but at a faster processing time. Moreover, the liquid fraction as a by-product may represent a valuable product for the food industry.

One of the most essential methods for preserving water resources is water reuse. The present study has been aimed at adjusting a gray water treatment system with high purification efficiency, low cost, easy maintenance, and high availability in every environment. To this goal, a new simple hybrid model for gray water treatment was made. In this model after conducting the settlement and aeration processes, seven different combinations of polypropylene activated carbon and anthracite filters beside resins were utilized for removing the contaminants in single and combined forms with three repetitions, and the results of each step were compared with the available standards. The different flow rates of 5, 2, 1, 0.5, 0.25, and 0.1 l/min were passed through the selected system to assess their effects on the reduction efficiencies of the pollutants in the system. The instantaneous ozonation method was used at the ozone concentrations of 1, 2.5, and 5 g/h. The results suggested the weakness of each filter alone for reducing the pollutants. After investigating the seven combinations, the combined filter of “polypropylene + resin + activated carbon” was recognized as the best filter for nondrinking purposes. The pH accounted for the lowest change of 10%, and chlorine and BOD5 accounted for the highest omissions of 90% and 80%, respectively. The system displayed the best performance at the flow intensity of 0.25 l/min or 15 l/h. The minimum rate of ozone production 1 g/h was able to remove all the coliform within any periods. This treated gray water is suitable for nondrinking uses such as irrigation and washing places.

Optimal water allocation may be considered a valuable solution to increase the productivity of water resources in arid and semi-arid areas. To achieve this purpose, the main resources of water consumption in the Baghmalek Plain (Khuzestan Province) from October 2014 to September 2015 and the groundwater table for 12 years (2002-2014) were simulated. Multi-objective optimization of the cropping pattern and groundwater simulation model have been programmed to generate a decision system in agricultural water management. Groundwater flow was simulated to address optimal discharge scenarios based on a finite difference numerical approach using MODFLOW software. The study years were divided into 48 seasonal stress periods and coefficients of hydraulic conductivity, specific yield and recharge were calibrated (36 periods) and verified (12 periods). The results showed that the flow model had an acceptable simulation accuracy by variance of 2.9 and 3.84 in the calibration and verification processes, respectively. Furthermore, precipitation is the main source of water supplying the cropping pattern, especially in the water-deficit scenario when total water demand is not fully satisfied. (c) 2020 International Commission for Irrigation and Drainage

aDepartment of Environmental and Biological Sciences, University of Eastern Finland, FI-70211 Kuopio, Finland, Tel. +358 414708731; emails: makafil@uef.fi, m-kafil@phdstu.scu.ac.ir (M. Kafil), Tel. +358 40 5050668; email: jorma.jokiniemi@uef.fi (J. Jokiniemi), Tel. +358 40 3553805; email: anna.lahde@uef.fi (A. Lähde), Tel. +358 50 3696419; email: amit.bhatnagar@uef.fi (A. Bhatnagar) bDepartment of Irrigation and Drainage Engineering, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Khuzestan, Iran, Tel. +98 9161183014; emails: boroomand@scu.ac.ir, boroomandsaeed@yahoo.com (S.B. Nasab), hmoazed955@yahoo.com (H. Moazed)

This study aimed to investigate the effect of modified Ceratophyllum demersum on the removal of heavy metal cadmium.The effect of pH (3-8), contact time (5-240 min), biomass concentration (0.02-4 g/L) and initial concentration of metal (10-200 mg/L) on the removal of cadmium, as well as kinetic and isotherm adsorption models were studied using the batch adsorption experiments.The results showed that with an increase in pH from 3 to 8, the removal efficiency increased from 93% to 97%, then decreased to 85%.By increasing the contact time from 5 to 180 min, the removal efficiency ranged from 67% to 98% and then slightly decreased until to 240 min.Also, increased adsorbent dosage from 0.02 to 4 g/L, the removal efficiency increased from 37% to 99%.The removal efficiency decreased from 96% to 31% with an increase in the initial concentration of cadmium from 10 to 200 mg/L.Optimum condition was found to be at pH, contact time, biomass concentration and cadmium concentration of 7, 60 min, 1 g/L and 10 mg/L, respectively.Pseudo-second-order kinetic and Langmuir model (R 2 > 0.99) were well fitted to the data.The results of study confirmed the high ability of biosorption process through Ceratophyllum demersum for wastewater treatment polluted with cadmium.

This study assessed the available status of waste management system in Ahvaz and its impact on the environment, as well as seven other scenarios in order to quantitatively calculate potential environmental impacts by utilizing the life cycle assessment (LCA) method. These scenarios were as follows: scenario 1: landfilling without biogas collection; scenario 2: landfilling with biogas collection; scenario 3: composting and landfilling without biogas collection; scenario 4: recycling and composting; scenario 5: composting and incineration; scenario 6: anaerobic digestion, recycling, and landfilling; scenario 7: anaerobic digestion and incineration. Emissions were calculated by the integrated waste management (IWM) model and classified into five impact categories: resource consumption, global warming, acidification potential, photochemical oxidation, and eco-toxicity. In terms of resource consumption and the depletion of non-renewable resources, the third scenario showed the worst performance due to its lack of any recycling, energy recovery, and conversion to energy. In terms of greenhouse gas emissions and the effect on global warming, scenario 1 and scenario 2 showed that disposing the whole amount of waste resulted in the most amount of greenhouse gases produced. Moreover, 50% gas and energy recovery from landfills, in comparison with the non-recovery method, reduced the index of global warming by 12%. Finally, scenarios which were based on producing energy from waste showed a reasonably positive performance in terms of greenhouse gases emissions and the influence on global warming.

A field experiment was conducted to understand the potential of vetiver grass (Vetiveria zizanioides) in heavy metal uptake from the soil and wastewater. Four main irrigation treatments including T1 (treated industrial wastewater), T2 (1:1 ratio of municipal:industrial wastewater), T3 (treated municipal wastewater) and T4 (fresh water) were applied. Moreover, the effect of arbuscular mycorrhizal fungus (AMF), Glomus mosseae, on plant growth and heavy metal concentration was evaluated. Three main criteria including bioconcentration factor (BCF), translocation factor (TF) and heavy metal uptake were applied to assess the potential of vetiver grass in accumulation and translocation of heavy metals to aerial parts. The highest concentration of heavy metals was found in plant and soil irrigated with T1 treatment followed by T2, T3 and the lowest concentrations were found in T4 treatment. Irrigation with treated municipal wastewater led to a significant increase in plant biomass and heavy metal uptake compared to other treatments. In T1 treatment (industrial wastewater), vetiver grass caused a significant decrease in Zn, Fe, Cu, Cd and Pb concentrations in soil as compared to no-plant treatment (without planting vetiver grass). Therefore, vetiver grass, irrigated with treated industrial wastewater, is a promising method for the development of urban and industrial green space.

Preferential water flow in soil macropores such as underground channels formed by worm activity and plant root growth, can move a large volume of water and contaminants to groundwater resources in a short time. To describe these types of water flow in soil, Di Pietro et al. (2003) developed and proposed kinematic-dispersive wave (KDW) model. They suggested this model by adding a dispersive term to the kinematic wave (KW) model that was severely convective and was presented by Germann in 1985. The fundamental assumption of this model is that the water flux (u) is exclusively a function of the mobile water content, but in the KDW model, considering its additional dispersive term, it is assumed that the water flux is a non-linear function of the mobile water content and its first-time derivative. The first term of this assumption is a power function where the water flux depends on the mobile water content. This equation is just a mathematical equation and has no significant physical meaning. In this research, this power function is substituted by the shape of van Genuchten model that has an acceptable physical meaning, and thus the kinematic-dispersive wave van Genuchten (KDW-VG) model is introduced for the first time as the innovation of this research. The models were calibrated and validated with observations of four different rainfall intensities that were applied on the surface of a soil column with artificial preferential pathways. The output water fluxes from the bottom of the soil column versus the soil mobile volumetric water content in the column were recorded at set times. First, both the KDW and KDW-VG models were calibrated and their indefinite coefficients were determined by minimizing the error function between the observed and modelled water fluxes versus mobile volumetric water content using particle swarm optimization (PSO) algorithm. Next, both models, which are second-degree non-linear partial differential equations, were solved using numerical finite difference method with the MAMAS programming language, and were validated by experimental observations of rainfall hydrograph that was passed through the preferential routes of a physical model and was recorded from the bottom of the soil column. Root-mean-square error (RMSE) comparison of the models predictions and observations indicated that the proposed model (KDW-VG) could predict the observations more accurately compared with the KDW model, and also had better performance in the calibration stage.

Heavy metals pollution is a topic with worldwide concern because of its high toxicity to human beings, animals, and plants. The clay minerals, being important constituents of soil, have been playing this role always by taking up various contaminants as water flows over soil or penetrate underground. The high specific surface area, chemical and mechanical stability, layered structure, high cation exchange capacity (CEC), Bronsted and Lewis acidity, etc., have made the clays excellent materials for adsorption. In this study, retention process of Cd(II) contaminant using Bentonite and Nanoclay Cloisite Na+investigated. Based on data obtained in this study it can be concluded thatthe retention capacity of Nanoclay Cloisite Na+ is much more than Bentonite for Cd ions, because of high specific surface area and high cation exchange capacity of Nanoclay Cloisite Na+.

Hydraulic modeling of denitrification beds is very useful for improving the performance of substrates. In this work, a column study was conducted for 98 days on two types of substrates using two different column lengths and at varying loading rates to determine the best hydraulic dimensions of the substrates. One substrate consisted of soil alone and the other substrate consisted of the same soil along with sugarcane bagasse (acting as an additional carbon source). The substrate with 0.7 h actual hydraulic retention time and 5.84 cm h(-1) hydraulic loading rate was chosen as the best model for simulation of denitrification substrate with an optimum nitrate removal efficiency (85%) and a maximum removal rate (50 mg L-1 h(-1)). The Fourier Transform Infrared analysis confirmed the degradation of sugarcane bagasse during the denitrification process. (C) 2016 Elsevier Ltd. All rights reserved.

Nitaret is one of the stable components of nitrogen in the nature. Nitrate compounds are highly soluble and can easily imported to the surface and groundwaters and finally lead to be polluted of them. Denitrification is one of permanent removal methods of nitrate from terrestrial and aquatic ecosystems. In this study, denitrification process and nitrate removal rate changes with time in a column experimental study was evaluated on two types of denitrification beds, first type was a mixture of bagasse and soil (30 % of the volume of bagasse and 70 % of the volume of soil) and second type was only soil without bagasse. All experiments were performed under saturation conditions (anaerobic conditions). The influent nitrate concentraion to the all beds was considered an average of 45 mg/l. With sampling of inflow and outflow of the columns, nitrate removal changes were assessed over a period of three months. The maximum percentage of nitrate removal in columns with mixture of soil and bagasse were occurred in the end of the experiment (94%) and in columns without the bagasse were ocured in the first of the experiment (89%).The results showed that sugar cane bagasse as a carbonic source can be very useful in the design of carbonic filters and denitrification walls for nitrate favorable removal of inlet concentrated solutions. Overall, the results confirm that the denitrification process is one of the main mechanisms of biological nitrate removal from anaerobic enviroments.

Due to the growing population and scarcity of fresh water it is increasingly important to produce potable water by desalination of saline water. However, desalination requires energy and in a susta ...

Modeling of water flow through vadose zone under unsaturated conditions necessitates the knowledge of soil hydraulic properties, which are water retention curve and water field capacity of soil. Indirect prediction of these characteristics based on readily available soil properties in the form of pedotransfer functions (PTFs) as a fast and low-cost solution has been widely practiced and very useful in irrigation and drainage. This study aimed to present the proper PTFs using mathematical modelling for estimating soil moisture at point of field capacity for Khuzestan province soils under laboratory and field conditions. The buried probes of the time domain reflectometry device (TDR) were inserted at various depths in order to monitor soil moisture conditions in both the physical model and experimental field under a surface-point source drip irrigation with discharge rates of 4 lph. Then, physical soil properties and soil water contents at their specific matric potentials were measured to calculate the hydraulic parameters of Van Genuchten-Mualem model with the RETC program. The results of this research to evaluate the performance of several well-known Point-PTFs showed that quasi-empirical models based on physical principles that have been tested in the field can be a good alternative to traditional methods for estimating water field capacity of soil. So that, the PTF of Twarakavi et al. could carefully predict that water field capacity of soil with indices of normalized root mean square error (3.1 percent) and standard error (0.5 percent) and more accurate than Rosetta artificial neural network approach or Dexter equation. Accordingly, another two PTFs were proposed to improve the accuracy of the water field capacity prediction in the form of regression equations on the basis of the parameters of Van Genuchten model and readily available soil properties for the semi-arid region of Khuzestan province. Results of obtained PTFs showed clearly negative effects of soil compaction and the amount of sand on the water field capacity of soil. On the contrary, the amount of clay and silt had positive and increasing effects on the water field capacity of soil, significantly.

Iron doped aluminium oxide nanoparticles are of interest for number of applications (e.g. water treatment, catalytic conversion of exhaust gases) due to their high surface area, hardness, catalytic and magnetic properties. In the present study, flame spray pyrolysis (FSP) was employed for the synthesis of Fe/Al2O3 nanoparticles. Precursor solutions of aluminium acetylacetonate (0.2 mol·L− 1) and ferrocene (0 to 0.2 mol·L− 1) in toluene were used to synthesise pure and iron (Fe) doped Al2O3. The particle composition and morphology were studied and effect of iron concentration was analysed. It was found that in the absence of the iron precursor, FSP produced a mixture of two Al2O3 polymorphs: θ-Al2O3 and η-Al2O3. The addition of ferrocene as an iron precursor was found to suppress formation of θ-Al2O3. At an iron molar concentration of 0.2 mol·L– 1 mainly hercynite, FeAl2O4, was observed. Furthermore, increasing the iron concentration caused a linear shift of the X-ray diffraction peaks from positions corresponding to η-Al2O3 to those of FeAl2O4. This indicates the formation of a solid solution (FexAl2O3 + x) at intermediated concentrations. It was also found that the primary particle size, which was below 10 nm, did not significantly change with the increased iron concentration and was comparable to the mean crystallite size indicating that size of these single crystalline primaries is determined by the synthesis process rather than the chemistry of the product. However, the hydrodynamic size was around 180 nm indicating that the particles are agglomerates in the water suspension. Additionally, zeta potential of the nanoparticles was found to decrease slightly with increasing iron content, though in all cases it was above 50 mV. Finally, the potential of synthesized nanoparticles was examined for the removal of fluoride because fluoride causes harmful health effects to human health at elevated concentrations. The results of fluoride removal using synthesized nanoparticles produced in this study showed that the highest fluoride removal efficiency was observed for the sample having no iron content.