Advances in Environmental Technology
Volume:6 Issue: 1, Winter 2020

In the present study, the amount of nitrate and phosphate leaching from agricultural lands into the Zanjanrood River in Iran was simulated using the Soil & Water Assessment Tool (SWAT) model. The measured average monthly discharges at the Sarcham station were used to calibrate and validate the SWAT model, and the SWAT Calibration and Uncertainty Program (SWAT-CUP) model was applied to perform the uncertainty and sensitivity analyses. Three scenarios for the irrigation methods and five for the fertilizer rates were defined. The p-factor and r-factor were used for the uncertainty analysis, and two statistical indices of the coefficient of determination (R2) and Nash-Sutcliffe efficiency (NS) were utilized in the validation model. For the calibration of the monthly runoff at the basin’s outlet, the coefficients of r-factor, p-factor, R2, and NS were obtained as 0.27, 0.11, 0.83, and 0.53, respectively. The results showed that by increasing the pressurized irrigation areas, the nitrate and phosphate pollutions in the river basin were not significantly affected. With regard to fertilizer rates, by reducing the consumption of urea and phosphate fertilizers up to 50%, the amount of nitrate and phosphate leaching into the Zanjanrood River was reduced up to 16.7% and 19.2%, respectively. On the other hand, an increase of 50% in fertilizer application increased nitrate and phosphate leaching into the river by 17.2% and 17.7%, respectively. In addition, by reducing the fertilization rate and preventing unnecessary fertilization by farmers, the pollution of water resources can be largely prevented.

Mining activities are among the main sources of trace elements in the environment, which constitute a real concern worldwide, especially in developing countries. This study aimed to investigate the multivariate approaches such as Correlation Matrix and Hierarchical Cluster Analysis (HCA) for the identification of probable sources of trace elements in the deposited dust near the Mehdi Abad Pb/Zn mine located in Mehriz, Yazd province, as well as the evaluation of dust contamination based on the Geo-accumulation Index (I geo), Nemerow Pollution Index (PI Nemerow), Improved Nemerow Index (IN), and Combined Pollution Index (CPI). In addition, an anthropogenic index was used to determine the sources of the elements.  For this purpose, deposited dust was collected in nine sites using a marble dust collector (MDCO). Next, the chemical analysis of dust was determined using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Statistics analysis was carried out using SPSS 22.0. The results showed that concentrations of zinc, thallium, silver, aluminium, arsenic, bismuth, calcium, cadmium, cerium, caesium, erbium, europium, gadolinium, hafnium, potassium, lutetium, manganese, sodium, phosphorus, antimony, scandium, and samarium in the deposited dust was higher than the background value. HCA identified two origins for the elements. The anthropogenic index confirmed the geogenic origin of elements in the deposited dust. Furthermore, CPI and PI Nemerow indices values showed that all sampling sites were in the heavily contaminated class. The results of IN Index showed that 56% of sampling sites were in the heavily contaminated class. The analysis of I geo, PI Nemerow, and IN indices showed that arsenic caused extreme contamination of the deposited dust at sampling sites.

The optimum conditions for the removal of cadmium as heavy and toxic metals via a biomass were investigated in this program. The biomass was prepared from an eco-friendly, native, and low-cost algae microorganism, e.g., Colpomenia sinuosa. The cadmium uptake involved the biosorption process onto the cell wall of the Colpomenia sinuosa. The experiments were carried out on the five different parameters of temperature, algae biomass dosage, the initial cadmium concentration, pH of the cadmium solution, and contact time for interval times of cadmium of the biomass surface. The design of the experiment (DOE) was done for different conditions. The optimum conditions were compared via two optimization methods. Both the response surface methodology (RSM) and fuzzy modeling were treated with experimental data. The contour maps were planned for understanding the effects of two interactive factors. The combined effects of pH-temperature, pH-contact time, and algae biomass dosage-temperature were plotted for cadmium uptake.

The comparative sorption studies were carried out to investigate the performance of keratin nanoparticles (KNPs) and magnetic KNPs (MKNPs) for Zn(II) uptake. MKNPs showed remarkably higher Zn(II) removal due to the lower keratin weight percent in its structure (8.4%). MKNPs revealed relatively uniform Zn(II) removal within pH range between 4.0 to 6.0 at the temperature of 25°C rather than KNPs. Both KNP and MKNP exhibited two-stage kinetic behavior and reached to their equilibrium adsorption capacity within 30 min. The adsorption of Zn(II) on KNPs and MKNPs followed pseudo second order kinetic model. It was found that the experimental data were best fitted to Sips or Redlich-Peterson isotherm when KNP was used as biosorbent. Unlike KNP, MKNP conformed better to Langmuir model. The maximum adsorption capacity of MKNP at two doses of 3.0 and 5.0 g/L was calculated to be 30 and 18 mg/g, respectively. As the dosage of MKNP raised from 3.0 to 5.0 g/L, the value of KL increased from 0.045 L/mg to 0.154 L/mg, confirming more biosorbent tendency to adsorb metal ions.

In this study a network of high frequency ultrasonic’s transducers without additives was introduced for removing the Reactive Red 120 dye from aqueous solution. pH, irradiation time, initial concentration and number of piezoelectric were input variables at constant temperature of 25 °C. The results revealed that the ultrasonic waves played an important role in cracking the hydrocarbon bonds due to the cavitation phenomenon and OH° attacks. The effects of the variables and their interactions were investigated by the central composite design (CCD) method as one of the response surface methodologies (RSM). Maximum dye removal’s efficiency (76.05%) was attained at initial concentration of 5 mg/l, irradiation time of 50 min; pH 10 and 5 ultrasonic’s transducers. It was in a good agreement with the experimental, 78%. Finally, to more evaluates, the RSM model was compared to the artificial neural network (ANN) model. Performance’s functions reported that the RSM was better than the ANN in predicting the dye removal’s efficiency (R%).

The vast majority of microorganisms in composting sites produce aerosols, which can cause respiratory difficulties. The objective of this study was to evaluate the emission of bioaerosols from compost applications in urban green space and assess their potential health hazards. The biological samples of bacteria and fungi in the air were collected in 20 points of the urban green space. Fungal medium and bacterial medium has been used as two plates containing the same medium to identify the bacteria and fungi in the air. The ambient temperature and humidity were measured at each of the 20 points of the sampling sites. The obtained results showed that the average concentration of bacteria and fungi in the background samples (before compost application) were 1108 and 122 CFU/m3, respectively. The bacterial and fungal concentration increased three times in the main samples (bacteria: 8393 CFU/m3 and fungi: 1659 CFU/m3) and increased relatively two times in the downwind samples at a distance of 10 m. Although the airborne fungal concentration in the main samples increased three times more than the background samples, a significant statistical difference was not verified between these values. As a result, the increasing of airborne fungi from compost application cannot be proven with certainty. Compost application in the urban green space is considered as the potential source for pathogenic bacteria emission.