The performance of every plant in an environment is an indicator of how the plant can withstand the various environmental conditions. This study investigated the toxicity of crude oil on the growth performance, chlorophyll contents, enzymatic activities and oxidative stress biomarkers of eight accessions of Zea mays. The growth enzyme (amylase and invertase) activities, as well as oxidative stress biomarkers (malondialdehyde, peroxidase, superoxide dismutase and catalase) in Z. mays were assayed using spectrophotometric method. The maize accessions were grown in the experimental pots containing crude oil treatments (2%, 4%, 6%, 8% and 10%) and harvested after 14 days of seedling emergence. The results showed that the percentage seedling emergence, leaf area, root length, stem girth and shoot length of each accession were significantly reduced (p

The occurrence of contaminants in wastewaters, and their behavior during wastewater treatment and production of drinking water are key issues to re-use water resources. The present research aims to remove caffeine from aqueous solutions via adsorption technique, using Multi-Wall Carbon Nanotubes (MWCNTs) as an adsorbent under different experimental conditions. The processing variables such as pH (2-12), contact time (1-30 min), initial concentration of caffeine (2-314 mg/L), temperature (25, 50, 80 °C), and adsorbent mass (0.02-0.15 g) have been investigated with equilibrium and kinetic studies on adsorption of caffeine onto MWCNTs being also developed. Maximum caffeine removal has been obtained at pH=7 and adsorption equilibrium has been achieved in 5 min. The use of pseudo second-order kinetic model with determination coefficient of 99.3% (R2=0.993), has made the adsorption kinetics to be well fitted. The caffeine equilibrium adsorption data have been best fitted to Langmuir-Freundlich Model with a relatively high determination coefficient of 96.5% (R2=0.965) and maximum adsorption capacity of 35.61 mg/g of caffeine on MWCNTs. The thermodynamic parameters display that the adsorption of caffeine onto MWCNTs has been non-spontaneous and endothermic in nature.

Petroleum refining industries produce large amounts of toxic effluents, causing environmental pollution. Iran is an oil-rich country that encounters oil pollution in its soil and water. Bioremediation of these pollutants is an appropriate solution to tackle them, compared to physical and chemical remediation methods. There are some factors that increase the rate of biodegradation; therefore, this study aims to determine the rate of gasoil bioremediation by two indigenous bacterial isolates (from oil-contaminated soils of an oil refinery south of Tehran) in two different media, namely soil and soil-sawdust mixture. The two superior indigenous bacteria has been isolated through three steps with results indicating that in an optimal environmental condition (temperature= 27±2 °C, humidity of 60%, water holding capacity, and daily manual aeration), bacterial isolates are able to degrade about 78.87% and 93.53% of gasoil during 45 days in soil and soil-sawdust mixture media, respectively. These results imply the role of sawdust in improving aeration, water holding capacity, and-consequently- increasing bioavailability of gasoil to bacteria.

This research aims to study the effect of Mycorrizal fungus and Plant-Growth-Promoting Bacteria (PGPB) on Cadmium (Cd) uptake by one-year-old Eucalyptus Camaldulensis seedlings. The treatments have involved three levels of heavy metal (0, 30, and 60 mg/kg) for Cd, and three bacterial levels (no bacteria (B0), Bacillus (Ba105), and Pseudomonas (Ps36, Ps448)), inoculated with mycorrhizal fungus Glomus mosseae (M1) and non-inoculated with fungus (M0). Results show that absorption of these elements in plant increased as Cd concentration in soil became more. Inoculation by Ps448 bacteria had an incremental effect on Cd uptake by 90%, compared to the non-inoculated (control) samples. Moreover, inoculation of the plants with mycorrhizal fungus increased Cd uptake by 24%, compared to the control. Also, it has been observed that plant resistance to metal stress and plant growth under such conditions ascended in treatments wherein inoculation happened with mycorrhizal fungus and bacteria. The highest Cd heavy metal uptake has been observed in Eucalyptus (shoots and roots), treatment (C2B2M1) with 648.19 micrograms per one seedling in pot. According to the obtained results, Eucalyptus with biological factors (fungi and bacteria) has the ability to clean and purify the contaminated soil with Cd heavy metal.

Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines requires considerable amount of energy, it delivers a suitable surface slope with minimal soil deterioration as well as damage to plants and other organisms in the soil. Notwithstanding, in recent years researchers have tried to reduce fossil fuel consumption and its deleterious side effects, using new techniques such as Artificial Neural Networks (ANNs) and Adaptive Neuron-Fuzzy Inference System (Fuzzy shell-clustering algorithm) models that will lead to a noticeable improvement in the environment. The present research investigates the effects of various soil properties such as Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in energy consumption. The study consists of 90 samples, collected from three different regions. The grid size has been set on 20 m * 20 m from a farmland in Karaj Province, Iran. The aim is to determine the best linear model, using ANNs and ANFIS model to predict environmental indicatorsand find the best model for land leveling in terms of its output (i.e. Labor Energy, Fuel energy, Total Machinery Cost, and Total Machinery Energy). Results show that ANFIS can successfully predict labor energy, fuel energy, total machinery cost, and total machinery energy. All ANFIS-based models have R2 values above 0.995 and MSE values below 0.002 with higher accuracy in prediction, given their higher R2 value and lower RMSE value.

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.

Transportation is an important part of modern community life as well as one of the largest sources of greenhouse gas emissions in urban communities, the population growth of which can increase transportation capacity. Monorail systems are relatively new rail transportation systems which are currently being designed and constructed in different countries. We have applied the Intergovernmental Panel on Climate Change (IPCC) to deal with global warming and Center of Environmental Science of Leiden University (Centrum voor Milieukunde Leiden), CML 2001, to evaluate the potential of acid raining. In order to analyze both mentioned methods, Sima Pro7.1 has been used. Initially the research-related data have been prepared from Qom Monorail workplace. Moreover the sensitivity analysis has been performed on the results, which indicated that the potential of causing global warming in the construction phase for a period of 100 years was equal to 26875.07 kg CO2eq. /km. person. The reinforcement bar with 32%, concrete with 30%, and diesel fuel with 15% enjoyed the lion’s share in terms of global warming creation. The likelihood of acid raining formation was equal to 101.876 kg SO2eq. /km. person. Diesel fuel contributed the most portion to the formation of acid raining (31%) with reinforcement bar and concrete in the second (30%) and third (13%) places. For result validation, the BEES (Building for Environmental and Economic Sustainability) software has applied with the sensitivity analysis, indicating that the first and second effective parameters on the results were the amount of reinforcement bar and diesel fuel. Hence, reduction of reinforcement bars, concrete, and diesel (respectively) have the most influence on mitigation of global warming and acid raining effects of Qom monorail project.

Plastics, one of the most abundant materials in the world today, are one of the main sources of pollution. People’s perception of using and disposing plastic bags is primordial as they tend to be a major part of household solid waste. This study aims to determine community’s perception of usage and disposal of plastic bags in Eastern Ethiopia. A quantitative cross-sectional study has been conducted in Harar city of Eastern Ethiopia, using a multi-stage statistical sampling technique. The data concerning all variables have been collected via pretested questionnaire and observational checklist with a response rate of 91% which has then been into SPSS and analyzed by STATA 11.0. About 51% open dump their plastic bag wastes. It has been shown that the majority (88.7%) of the responders perceived plastic bags as having environmental impacts, with 82.8% feeling a growth in their plastic bags utilization and about 54% thinking that plastic bags should be banned. It has also been illustrated that perception of plastic bags' environmental impacts and the willingness to pay for solid waste collection service has an association with plastic bag utilization. This study has found that utilization and open dumping of plastic bags rests at a significantly higher level. Community’s perceptions regarding environmental impacts of plastic bags have been at a higher level, which still requires consideration. A considerably high number of residents believe that utilization of plastic bag should be continued; therefore, a great emphasis should be given to bringing the desired attitudinal change.

Environmental pollution consists of different types of pollutants in air, soil, and water. Due to the fact that plants can respond to environmental pollution, they can be used as bio-indicators for environmental monitoring. Air pollution in areas with oil and gas refineries nearby is an important problem. This study aims to survey the effects of gas refinery air pollutants on Conocarpus erectus so that it can be used for air-pollution monitoring. For this purpose, physiological indicators such as proline, protein, and malondealdehyde content as well as Air Pollution Tolerance Index (APTI value) have been used to evaluate these effects. Based on the obtained results, Conocarpus erectus in polluted area showed increased pH, protein, proline, malondealdehyde, and ascorbic acid contents while carotenoid quantity, total leaf chlorophyll content, and relative water content were decreased in comparison to the control plants. Therefore, it proves that air pollution affects the plant.

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.