INVESTIGATION OF INDUSTRIAL WASTEWATER MANAGEMENT CONDITION BASED ON WASTEWATER ORGANIC POLLUTION LOADS AND TREATMENT FACILITIES IN THE INDUSTRIAL DISTRICT OF WEST TEHRAN

Industrial wastewater characteristics were investigated using questionnaire of Environmental Protection Organization of Iran, visiting industrial factories and interviewing. Industries and mining organization data base of all industries were classified in ten categories. Number of industries with greater than 50 staff in industry in this region was 283; fifty industries were selected with classified weighted sampling method for this study. The average wastewater flow rate generated by these 50 industries was 9422 m3/day, with an average volume of wastewater generated per capita found to be about e 437 liters per day. Minimum organic loading was related to non-metallic mineral industries with COD of 205 mg/L and BOD of 85 mg/L. maximum COD was related to paper industries with a rate of 8800 mg/L and maximum BOD was recorded at food and drug industries with rate of 1536 mg/L. All of these 10 categories of industries were scored based on generated COD, BOD loading and wastewater load per capita from 1 (minimum) to10 (maximum) and then were ranked from most polluting to least polluting industry. In this study, paper industries with overall score of 28 in three aforementioned parameters were major source of pollution in the industrial district of west Tehran. On the other hand non metallic industry with the overall score of 5 was found to be the least polluting industry. &nbsp

Efficiency of Brij 35 and Tween 80 surfactants for treatment of gasoline contaminated soil

Abstract Background and Objectives: Soil pollution by oil compounds is a serious environmental and ground water problem throughout the world. Total petroleum hydrocarbons (TPH) are a combination of many distinctive compounds. Some of these compounds in exposure with human and animal can cause cancer, disorder central nervous system, liver and lungs affects. The objective of this research was to investigate gasoline removal (C10 – C 28) using Brij 35 and Tween 80 from polluted soil. Materials and Methods: In this experimental study, the efficiency of soil washing with nonionic surfactants (Brij 35, Tween 80) for remediation of gasoline polluted soils was studied. The effects of soil washing time, agitation, surfactant concentration, and pH on the removal efficiency were studied. Results: The results showed that gasoline removal efficiency increased with increasing agitation speed and washing time. In addition, it was found that removal efficiency of gasoline was decreased by increasing surfactant concentration. But, increasing pH did not have remarkable effect on removal efficiency of gasoline. The efficiencies of Tween 80 and Brij 35 for removal of gasoline under optimal condition were 70 -80 and 60- 65% respectively. Conclusion: The results showed that soil washing with non ionic surfactants was effective in removal of gasoline from polluted soil and it can be recommended for treating surface soil polluted

Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process

Background & Aims of the Study: A feed forward artificial neural network (FFANN) was developed to predict the efficiency of total petroleum hydrocarbon (TPH) removal from a contaminated soil, using soil washing process with Tween 80. The main objective of this study was to assess the performance of developed FFANN model for the estimation of   TPH removal. Materials and Methods: Several independent repressors including pH, shaking speed, surfactant concentration and contact time were used to describe the removal of TPH as a dependent variable in a FFANN model. 85% of data set observations were used for training the model and remaining 15% were used for model testing, approximately. The performance of the model was compared with linear regression and assessed, using Root of Mean Square Error (RMSE) as goodness-of-fit measure Results: For the prediction of TPH removal efficiency, a FANN model with a three-hidden-layer structure of 4-3-1 and a learning rate of 0.01 showed the best predictive results. The RMSE and R2 for the training and testing steps of the model were obtained to be 2.596, 0.966, 10.70 and 0.78, respectively. Conclusion: For about 80% of the TPH removal efficiency can be described by the assessed regressors the developed model. Thus, focusing on the optimization of soil washing process regarding to shaking speed, contact time, surfactant concentration and pH can improve the TPH removal performance from polluted soils. The results of this study could be the basis for the application of FANN for the assessment of soil washing process and the control of petroleum hydrocarbon emission into the environments

Data on using macro invertebrates to investigate the biological integrity of permanent streams located in a semi-arid region

The aquatic ecosystems are continuously endangered due to variety of hazardous chemicals containing different toxic agents which can be emitted from anthropogenic sources. Besides the increasing of human population, various kinds of contaminants enter into the surface water resources. The aim of the present study was to investigate the abundance and diversity of macro invertebrates in two permanent streams located in the northern part of Tehran. The biological integrity of the streams was determined by manual sampling approach at five points. The distances between the sampling points were at least 2 km. The bio indicator organisms, organic pollution, and dissolved oxygen were measured. The different types of benthic invertebrates such as riffle beetle, midge and caddish fly larvae, dragon fly, may fly and stone fly nymph, riffle beetle adult, pyralid caterpillar, leech, and pouch snail were identified. It can be concluded that, the identified benthic macro invertebrates can be served as appropriate biological indicator in the studied area. Keywords: Biological integrity, Tehran, Macro invertebrate

MWCNT-Fe3O4 as a superior adsorbent for microcystins LR removal: Investigation on the magnetic adsorption separation, artificial neural network modeling, and genetic algorithm optimization

Magnetic multi-wall carbon nanotube (MMWCNT) was prepared by simple protocol and its structural features were characterized using SEM, TEM, and XRD analysis. The association between removal (%) and variables such as pH (3 − 11), adsorbent amounts (0.005, 0.1, 0.25, 0.5, 0.75, and 1 g/L), reaction time (5–180 min), and concentration of microcystins-LR (10, 25, 50, 75, and 125 μg/L) was investigated and optimized. The results of the isotherm study indicated that Langmuir offered high determination coefficients (R2 = 0.993, 0.996, and 0.998, for the three different working temperatures of 20 °C, 35 °C, and 50 °C respectively) and was the optimum isotherm to anticipate adsorption of MC-LR (microcystins-LR) by magnetic MWCNT adsorbent. The kinetic study revealed that the adsorption kinetics of MC-LR could be better defined using the pseudo-second-order model. A three-layer model of an artificial neural network was applied to forecast the MC-LR removal efficiency by magnetic MWCNTs over 66 runs. To forecast the MC-LR removal efficiency, the minimum mean squared error of 0.0011 and determination coefficient (R2) of 0.9813 were obtained. The use of the artificial neural network model achieved a good level of compatibility between the acquired and anticipated data