Showing 4 results for Alinezhad
H. Alinezhad Jahromi, A. Mohammadkhani, M. H. Salehi,
Volume 16, Issue 60 (Summer 2012)
Abstract
Nowadays, due to drought and water shortage, use of unconventional waters, particularly sewage, has become usual in agriculture whereas they often contain heavy metals. The present study was employed to evaluate the effect of urban wastewater of Shahrekord on growth, yield and accumulation of heavy metals (lead and cadmium) in balm (Melissa officinalis) as a medicinal plant with five treatments (0, 25, 50, 75 and 100 percent wastewater) and three replications in a completely randomized experimental design. The results showed that the highest shoot length, stem diameter and stem number, number of leaves and tillers are achieved in the treatment of 100 percent. The wet and dry weight of shoots and roots was highest in 100 % of wastewater. Oil percentage of the leaves was also the highest amount (1.23 %) in 100 % of wastewater. Accumulation of lead in roots and aerial parts and its transmission factor was not significant for the treatments. However, the highest concentration of lead in root (0.057 mg/kg) and shoots (0.013 mg/kg) was observed in 100 % of wastewater and the lowest one was related to zero percent of wastewater treatment. The lead concentration was less than the critical limit for all the treatments. The amount of cadmium was undetectable in all the plant samples. The results of this study demonstrated that urban wastewater of Shahrekord, in addition to providing water, increases plant growth and essential oil.
A. Alinezhad, A. Gohari, S. Eslamian, Z. Saberi,
Volume 23, Issue 4 (Special Issue of Flood and Soil Erosion, Winter 2019)
Abstract
The evaluation of climate change impact on hydrological cycle includes uncertainty. This study aimed to evaluate the uncertainty of climate change impact on the Zayandeh-Rud Reservoir inflow during the future period of 2020-2049. The outputs of 22 GCM models were used under the three emission scenarios including RCP2.6, RCP4.5 and RCP8.5. The Bayesian Model Averaging (BMA) was used as the uncertainty analysis for weighting the 22 GCM models based on their ability to simulate the baseline 1990-2005 period. Results showed that different GCM models had different abilities in estimating climatic and hydrological variables and the application of uncertainty analysis in climate change studies could be necessary. The monthly temperature in the upstream of Zayandeh-Rud reservoir could be raised by 0.85 to 1 ◦C; also, the precipitation might be increased by 2 to 3 percent. The high flow during winter season will increase under climate change, while the spring and autumn seasons’ low flows are expected to reduce. Additionally, the annual reservoir inflow may decrease by 1 to 8 percent, showing the necessity for change in Zayandeh-Rud reservoir’s rule curve and allocation of water resources.
F. Mehri Yari, H. Pirkharrati, Kh. Farhadi, N. Soltanalinezhad, F. Naghshafkan,
Volume 24, Issue 1 (Spring 2020)
Abstract
Soil pollution by heavy metals is a serious environmental problem that threatens the human health. The present study was carried out to investigate and detect the contamination of heavy metals of arsenic, copper, lead, zinc and iron due to human and natural activities in the sediment of lake bed and the surface soils of the eastern part of Urmia Province, West Azarbaijan Province. A total of 20 soil samples and surface deposition from the depths of 0 to 30 cm were collected randomly from the studied areas. After preparing the samples, extraction was carried out to determine the concentration of the heavy metals in the soil by using hydrochloric acid and nitric acid, and the total concentration of metals was measured using ICP-OES. The results of the calculation of the contamination factor showed that copper, iron, zinc and lead in the class of low and medium pollution and arsenic in 65.5% of the samples were very high in the class. The high concentrations of copper, lead and zinc contamination in the margin of the city and the contamination of arsenic in the lake bed were observed. The analysis of the contamination factor maps and contamination index with land use and geological map showed that copper, lead and zinc were mostly affected by human activities and arsenic influenced by the maternal materials in the region.
M. Alinezhadi, S. F. Mousavi, Kh. Hosseini,
Volume 25, Issue 1 (Spring 2021)
Abstract
Nowadays, the prediction of river discharge is one of the important issues in hydrology and water resources; the results of daily river discharge pattern could be used in the management of water resources and hydraulic structures and flood prediction. In this research, Gene Expression Programming (GEP), parametric Linear Regression (LR), parametric Nonlinear Regression (NLR) and non-parametric K- Nearest Neighbor (K-NN) were used to predict the average daily discharge of Karun River in Mollasani hydrometric station for the statistical period of 1967-2017. Different combinations of the recorded data were used as the input pattern to predict the mean daily river discharge. The obtained esults indicated that GEP, with R2= 0.827, RMSE= 59.45 and MAE= 26.64, had a better performance, as compared to LR, NLR and K-NN methods, at the validation stage for daily Karun River discharge prediction with 5-day lag, at the Mollasani station. Also, the performance of the models in the maximum discharge prediction showed that all models underestimated the flow discharge in most cases.
