Environmental Health Engineering and Management Journal

195–201

Authors: Mahmoud Zarei*, Sakha Pezhhanfar, Abolfazl Ahmadi Someh

Background: Colors are very useful in different industries such as textile and leather but when they enter water, can cause many biological and environmental problems. In the present research, a waste agricultural material which is freely available is employed to analyze its efficiency for removing acid dye from contaminated wastewaters. Methods: In this study, batch adsorption experiments were performed in the treatment process of acid red 88 (AR88) by Iranian golden Sesamum indicum (IGSI) seeds hull which is produced abundantly in some countries and especially in Iran up to 1100 kg/ha. Also, the effect of operational parameters like adsorption time, pH, dye concentration and adsorbent dosage was studied on pollutant removing efficiency. The experimental data of AR88 adsorption was fitted to Langmuir, Freundlich and Temkin isotherm models. The scanning electron microscopy (SEM) images for the IGSI were taken before and after adsorption process. Results: The efficiency of dye adsorption on adsorbent was found to be 98.2%. The optimum pH for treatment was 4.5 which is in the acidic range. Enhancing the adsorbent dosage from 0.5 to 2.5 g caused increasing in removal efficiency from 73.85% to 95.85%. Decreasing in dye concentration from 70 to 30 mg/L caused increasing in removal efficiency from 79.73% to 95.83%. The process of adsorption was best fitted to Langmuir model and the amount of dye adsorbed on adsorbent, qe, was found to be 25 mg/g. Comparison between SEM images before and after dye adsorption, showed the significant difference that was due to the dye loading on adsorbent. Conclusion: The results of present study demonstrated higher dye removal efficiency for AR88 in acidic pHs. Employing the IGSI material in this study proves to be a potential alternative to expensive adsorbents, utilized for the treatment of contaminated industrial waste waters.

209–215

Authors: Amin Mohebbi Tafreshi, Ghazaleh Mohebbi Tafreshi*

Background: A new method has been presented specifically for zoning the quality of groundwater for drinking purposes; this method is the groundwater quality index (GQI) method. The present research used the GQI method to qualitatively zoning of the Lenjan groundwater for drinking purposes. Methods: Three phases were applied in this research. In the first phase, working on the quality data of 38 wells within the studied plain, the raster map of quality concentration parameters, including pH, TDS, Cl, SO4, Ca, Mg, and Na parameters, was provided by interpolation using the kriging method in the ArcGIS software. In the second phase, the mentioned maps were standardized so that various bits of data can follow a common standard and scale. In the third phase, weight was applied to each standardized map, and ultimately the classification map for each parameter was drawn. The final GQI map was created by combining the mentioned classification maps. Results: The GQI values for Lenjan plain were rated from the minimum (67.48) to the maximum (90.05). The results showed an average to acceptable level of quality for drinking water. Conclusion: According to the final map, the central and southern parts of Lenjan plain, which have acceptable GQI rankings, are the best zones from which to use groundwater for drinking purposes.

225–231

Authors: Meysam Alizamir*, Soheil Sobhanardakani

Background: The effects of trace elements on human health and the environment gives importance to the analysis of heavy metals contamination in environmental samples and, more particularly, human food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn) contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA). Methods: This study presents a new method for predicting heavy metal concentrations in the groundwater resources of Ghahavand plain based on ANN and ICA. The developed approaches were trained using 75% of the data to obtain the optimum coefficients and then tested using 25% of the data. Two statistical indicators, the coefficient of determination (R2) and the root-mean-square error (RMSE), were employed to evaluate model performance. A comparison of the performances of the ICA-ANN and ANN models revealed the superiority of the new model. Results of this study demonstrate that heavy metal concentrations can be reliably predicted by applying the new approach. Results: Results from different statistical indicators during the training and validation periods indicate that the best performance can be obtained with the ANN-ICA model. Conclusion: This method can be employed effectively to predict heavy metal concentrations in the groundwater resources of Ghahavand plain.

239–244

Authors: Hesam Aryanpour, Seyed Alireza Movahedi Naeini*, Ahmad Ahmadian

Background: Cattle manure (CM) is the most common organic fertilizer used by farmers. However, its usually slow decomposition leads to the use of chemical fertilizers. Therefore, experiments on nano- and micro-sized particles of CM were conducted to evaluate the possibility of accelerating its decomposition in soil. Methods: The effects of a sole application of CM in different sizes (nano-, micro-, and natural-sized particles) in two ranges (5 and 20 Mg ha-1) and the combined application of CM and chemical fertilizers on the plant growth characteristics of soybean (cv. JS 335) were studied at Gorgan University. Nano- and micro-sized particles of CM were produced using a ball mill, and their half-life in soil was measured. Soil properties were measured before planting. Grain yield, 1000 grain weight, number of pods per plant, biological yield, plant height, and nutrient contents in plant shoot material were measured. Results: The results showed that the use of nano-sized particles of CM (nCM) caused a significant increase in yield and yield components. Increasing the amount of crushing resulted in an increased rate of CM mineralization and in proper nitration before the formation of nodes in the roots. A significantly higher yield was obtained with nCM than with chemical fertilizer, and due to the nCM particles’ half-life in soil, the plants were allowed to absorb nutrients for a longer time period. Conclusion: The nCM has two major advantages over chemical fertilizers in that it does not release nutrients as quickly as chemical fertilizers and the loss of nutrients from soil is low.