A greenhouse experiment has been planned for this study to delineate the benefits of two types of rice husk biochars (namely B300 and B600 which are prepared at 300°C and 600°C, respectvely) and coal fly ash (CFA), as soil amendments, for decreasing the amount of some heavy metals (like Pb, Cd, Ni, Cr, and Cu) as well as mobility and phytoavailability in an artificially-calcareous multi-element-contaminated soil. The effect of soil amendment on heavy metals’ availability has been evaluated via sequential extraction experiment and phytoavailability of the plant. According to the results, among the studied amendments, B600 has had the highest positive effect on both dry matter yield in corn and heavy metals’ availability reduction in post-harvest soil samples (with the exception of Cr), compared to CFA and B300, due to the increasing specific surface area, CEC, and pH that promote heavy metals’ sorption in the soil through surface complexation and ion exchange mechanisms. Evaluation of heavy metals’ chemical forms in post-harvest soil samples indicates that addition of amendments has significantly decreased mobility factor of heavy metals (with the exception of Cr in CFA-amended soils). In general, application of three soil amendments to this polluted soil has considerable effect on the reduction of heavy metals’ availability and phytoavailability. However, among the studied amendments, B600 and CFA have had the maximum and minimum effect on heavy metals’ availability reduction, respectively.

Application of aromatic compounds has dramatically increased as raw materials in various industries and different factories have been established to produce aromatic compounds. The current research aims at characterizing industrial waste generation in aromatics production process in petrochemical industries and determining the best feasible alternative for waste disposal. For this purpose, the world’s biggest aromatic producer, i.e. Nouri Petrochemical Complex (NPC), located in Asaluyeh, Iran, has been selected as case study. Firstly, different waste streams, generated during aromatics production, have been determined through a specific checklist. Spent industrial soil, catalyst, spent sieve, and Normal­-Formyl­-Morpholine (NFM) solvent are the most important identified wastes in NPC, with the former being the most generated waste in NPC with a rate of 600 tons per year. Afterwards, the mentioned waste has been sampled and important physicochemical specification such as heavy metals and organic compounds has been measured. Ni, Cu, and As are remarkable trace heavy metals, observed in all kinds of generated waste. In the next step, industrial waste classification and coding has been done, based on different guidelines. Finally different feasible alternatives like material recovery, sanitary landfill, and incineration have been compared, based on conventional economic, technical, and environmental indices. The best feasible waste disposal methods are the extraction of heavy metals from spent catalysts, recycling of spent molecular sieves and spent industrial soil as additives to building materials, and recovery of thermal energy by incineration of spent NFM solvent.

The present study synthesizes Zn-TiO2 photocatalyst via a simple and economic green rout, in which Green Tea is applied as a green reducing agent due to the presence of polyphenols Molecules. Polyphenol molecules in green tea act as a reductant, thus changing Zn2+ to metallic Zn. The by-produced nanocmposites are characterized by using XRD, FESEM, EDS, and DRS. Zn-TiO2 photocatalyst possesses great efficient charge separation properties. In order to investigate the presence of Zn, different weight ratio of Zn to TiO2 (viz. 5 wt%, 10 wt%, 15 wt%, 20 wt%, 25 wt%, 30 wt%, 35 wt%, 40 wt%, 45 wt%, and 50 wt%) have been synthesized and their performance in Acid Orange 7 (AO7) photodegradation, compared with pure TiO2. According to the results, the compound with 25 wt% Zn shows 97% degradation of AO7 as a model pollutant. Also, it has been shown that after three tests with EDTA, benzoic acid, and under Ar gas, photodegradation of AO7 with Zn-TiO2 photocatalyst mainly depends on photogenerated holes.

Indoor radon and thoron have been measured in the houses ofMoradabad District, Uttar Pradesh India, by means of solid state nuclear trackdetectors. Radon, an invisible radioactive gas, occurs naturally in indooratmospheres and along with thoron is the most important contribution of humanexposure to natural sources. Radon exists in soil gas building materials and indooratmosphere to name but a few. Risk of lung cancer depends on the concentrationof radon and thoron and their decay products in the environment aboverecommended levels. The present article measures the concentration of indoorradon and thoron in 60 dosimeters by means of a solid state nuclear track detectorin different house types of Moradabad district, Uttar Pradesh. The measurementshave been carried out in residential buildings at a height of 2 m from the sea level,using a twin chamber radon dosimeter. The value of radon concentration in thepresent study varies between 10.5 Bq/m3 and 29.5 Bq/m3 with an average of 19.8Bq/m3 while that of thoron is between 5.6 Bq/m3 and 24 Bq/m3 with an average of14.9 Bq/m3 respectively. Results, obtained with twin cup radon/thoron dosimeter,show that the concentration of indoor radon and thoron have been within therecommended level, with all the values staying under the safe limits, decreed bythe International Commission on Radiological Protection (ICRP) and UnitedNations Scientific Committee on the Effect of Atomic Radiation (UNSCEAR).

Biodegradation of polycyclic aromatic hydrocarbons, toxic compounds widely distributed in the environment by bacteria, is a cheap and safe cleaning up method. The present study attempts to isolate and characterize dioxygenase-producing bacteria which are able to degrade phenanthrene and pyrene from refinery soils. It also aims to assess in vitro biodegradation. To do so, two contaminated soil samples were collected from Isfahan-Iran refinery. The population of phenanthrene and pyrene degrading bacteria were 2.17 × 103 and 1.19 × 103 CFU/g in sample 1 and  21.50 × 103 and 19.40 × 103 CFU/g in sample 2. A sum of 18 phenanthrene and pyrene degrading bacteria were isolated using enrichment culture technique, three of them getting selected which had dioxygenase activity and produced biosurfactant. Identified as Pseudomonas plecoglossicida ATAI18, Pseudomonas aeruginosa ATAI19, and Pseudomonas stutzeri ATAI21, they were submitted to GenBank under the accession number of KF113842, KF113843, and KF113845 respectively. The degradation rate of pyrene (50 mg/L) by strains ATAI18 and ATAI19 was 45.32% and 31.23%, respectively. The strain ATAI21 degraded 39.38% of phenanthrene (50 mg/L) after 9 days. These isolated bacteria can be used to improve microbial population of other hydrocarbon-polluted soils for faster bioremediation of such areas.

The present study has been conducted on Caspian Roach (Rutilus rutilus) so that the impact of different concentrations lead on blood hematological, biochemical, and immunological indicators could be investigated. The roach (Rutilus rutilus), used in this study, was 3.3±0.3 g heavy and 4±0.80 cm long in average. The fish were exposed to a low concentration of 10% LC50 and high concentration of 50% LC50 lead for a period of 0, 24, 48, and 96 h. The experiment was done in a static toxicity condition, within tanks of 400L, each including 14 fish. In the 96-hour period (10% LC50 Lead), the parameters of WBC and RBC dropped significantly in comparison to the control group (0 h) (P<0.05). MCV and MCH elevated as the exposure time ascended up to 96 h (P<0.05), but the cortisol decreased in the meantime. In the 96-hour period (50% LC50 Lead) RBC, Hb and Hct parameters plummeted towards the control group (P<0.05). Heavy metals can change physiological and biochemical parameters in fish blood. The results show that lead (10% LC50 and 50% LC50 concentration Lead) may poison the fish, causing their death. The major toxicity belongs to the 50% LC50 concentration Lead.

Conserving water resources and protecting them from pollution are of high account in the natural cycle of our life. This study has tried to determine the refining potential and capacity of water hyacinth (Eichhornia crassipes) in order to remove the cadmium from water, studying the influence of factors such as initial concentration of cadmium, contact time, absorbent mass, and pH. Results have shown that the best efficiency of cadmium, more than 99%, was obtained in the optimum conditions (i.e. retention time of 30 hours, adsorbent dose of three plants (12 stems), and pH=6.6). By increasing the initial concentration of cadmium from 0.28 to 8.28 mg/L, the elimination efficiency did not change; moreover, by increasing the absorbent mass, the elimination efficiency increased from 98.4 to 99.8 and the lowest retention time was obtained for the balance. All experiments have been repeated three times, showing in the end that water hyacinth is able to absorb cadmium up to 8.28 mg/L. This process follows Freundlich isotherm (R2=0.98). Results of this study indicate that this plant can grow well at high levels of cadmium and the growth of water hyacinth is better in the presence of cadmium than control conditions (city water). Finally, it can be concluded that it is necessary to provide a reliable, cheap, and fast method to eliminate pollution. Eichhornia crassipes, a promising plant with great functionality, can be used as a refiner in order to eliminate the heavy metals in wastewater (sewage) effluents, particularly industrial sewage.

Kumasi, Sekondi-Takoradi, and Accra are the major recipients of agricultural commodities or productions in Ghana, primarily due to their population and ready markets. To ensure food security, meet food demands, and mitigate the threats posed by pests and diseases, pesticides are used when cultivating vegetables and fruits in Ghana. The present study has been conducted to assess the concentration of various pesticide residues in tomatoes, obtained from three market centers (namely Bantama, Central, and Ayigya Markets) in Kumasi, dealing with potential health risks for the consumers. Analyzed samples have revealed high percentage of organophosphorous pesticide residues (45%) in Ayigya market with Bantama market recording high percentage of organochlorines and pyrethroid. Laboratory analysis of tomato samples for various pesticides residues has indicated that all the pesticide residues pose no threat to human’s health with all estimated hazard indices being below 1; however, heptachlor (HI=0.85) and dieldrin (HI=0.74) have shown the highest risk levels in children. The study reveals that there is some need for strict monitoring of heptachlor and dieldrin in tomato, especially in case of children.

One of the main aims of water resource planners and managers is to estimate and predict the parameters of groundwater quality so that they can make managerial decisions. In this regard, there have many models developed, proposing better management in order to maintain water quality. Most of these models require input parameters that are either hardly available or time-consuming and expensive to measure. Among them, the Artificial Neural Network (ANN) Models, inspired from human brain, are a better choice. The present study has simulated the groundwater quality parameters of Ramhormoz Plain, including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), and Total Dissolved Solids (TDS), via ANN and ANN+ Particle Swarm Optimization (PSO) Models and at the end has compared their results with the measured data. The input data for TDS quality parameter is consisted of EC, SAR, pH, SO4, Ca, Mg, and Na, while for SAR, it includes TDS, pH, Na, and Hco3, and as for EC, it involves So4, Ca, Mg, SAR, and pH; all of them, gathered from 2009 to 2015. Results indicate that the highest prediction accuracy for SAR, EC, and TDS is related to the ANN + PSO model with the tangent sigmoid activation function so that both MAE and RMSE statistics have the minimum and R2 the maximum value for the model. Also the highest prediction accuracy is respectively related to EC, TDS, and SAR parameters. Considering the high efficiency of artificial neural network model, by training the PSO algorithm, it can be used in order to make managerial decisions and ensure monitoring and cost reduction results.

The present research analyzes air quality in Ahvaz, a city in the south of Iran, paying special attention to sulfur dioxide (SO2). In order to prepare the average data in the city, measurements have been carried out between 2009 and 2010 in two different locations. Relations between sulfur dioxide and some meteorological parameters have been calculated statistically, using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation and rainfall have been considered as independent variables. The RMSE Test showed that among different prediction models, the stepwise one is the best option. The average concentrations have been calculated for every 24 hours, during each month and each season. Results show that the highest concentration of sulfur dioxide occurs generally in the morning while the lowest concentration is found before the sunshine. In case of the monthly concentrations of sulfur dioxide, the highest value belongs to January, while the lowest one occurs in October. And as for the seasonal concentrations, it has been shown that the highest amounts belong to winter. Results show that quantities of SO2 in different seasons as well as the entire year can be estimated by climate parameters. Results also indicate that the relations between the SO2 and meteorological parameters are stronger than the entire year during the seasons.