Phytoremediation of an aged petroleum contaminated soil using endophyte infected and non-infected grasses

Phytoremediation is a promising technique for cleaning petroleum contaminated soils. In this study, the effects of two grass species (Festuca arundinacea Schreb. and Festuca pratensis Huds.), infected (E(+)) and non-infected (E(-)) by endophytic fungi (Neotyphodium coenophialum and Neotyphodium uncinatum, respectively) on the degradation of petroleum hydrocarbons in an aged petroleum contaminated soil was investigated. Plants were grown in the soil for 7 months and unplanted soil considered as control. At the end of the experiment, total and oil-degrading bacteria, dehydrogenase activity, water-soluble phenols, total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs) contents were measured in the soil. The results demonstrated that E(+) plants contained more root and shoot biomass than E(-) plants and created higher levels of water-soluble phenols and dehydrogenase activity in the soil, while there was no significant difference in bacterial counts of planted soils. Planting stimulated total and oil-degrading bacterial numbers, dehydrogenase activity and the soil content of water-soluble phenols. Regardless of endophyte infection, PAH and TPH removal in the rhizosphere of plants were 80-84 and 64-72% respectively, whereas the removals in controls were 56 and 31%, respectively. It was revealed that TPHs in retention time range of n-alkanes with C(10)-C(25) chain lengths and TPH were more degraded in the rhizosphere of E(+) plants compared to E(-) ones. Thus, grasses infected with endophytic fungi could be more efficient for removal of TPH from oil-contaminated soils.     

A strategic management model for evaluation of health,safety and environmental performance

Strategic health, safety, and environmental management system (HSE-MS) involves systematic and cooperative planning in each phase of the lifecycle of a project to ensure that interaction among the industry group, client, contractor, stakeholder, and host community exists with the highest level of health, safety, and environmental standard performances. Therefore, it seems necessary to assess the HSE-MS performance of contractor(s) by a comparative strategic management model with the aim of continuous improvement. The present Strategic Management Model (SMM) has been illustrated by a case study and the results show that the model is a suitable management tool for decision making in a contract environment, especially in oil and gas fields and based on accepted international standards within the framework of management deming cycle. To develop this model, a data bank has been created, which includes the statistical data calculated by converting the HSE performance qualitative data into quantitative values. Based on this fact, the structure of the model has been formed by defining HSE performance indicators according to the HSE-MS model. Therefore, 178 indicators have been selected which have been grouped into four attributes. Model output provides quantitative measures of HSE-MS performance as a percentage of an ideal level with maximum possible score for each attribute. Defining the strengths and weaknesses of the contractor(s) is another capability of this model. On the other hand, this model provides a ranking that could be used as the basis for decision making at the contractors’ pre-qualification phase or during the execution of the project.     

Biosorption and biodegradation of Acid Orange 7 by Enterococcus faecalis strain ZL: optimization by response surface methodological approach

Reactive dyes account for one of the major sources of dye wastes in textile effluent. In this study, decolorization of the monoazo dye, Acid Orange 7 (AO7) by the Enterococcus faecalis strain ZL that isolated from a palm oil mill effluent treatment plant has been investigated. Decolorization efficiency of azo dye is greatly affected by the types of nutrients and the size of inoculum used. In this work, one-factor-at-a-time (method and response surface methodology (RSM) was applied to optimize these operational factors and also to study the combined interaction between them. Analysis of AO7 decolorization was done using Fourier transform infrared (FTIR) spectroscopy, desorption study, UV–Vis spectral analysis, field emission scanning electron microscopy (FESEM), and high performance liquid chromatography (HPLC). The optimum condition via RSM for the color removal of AO7 was found to be as follows: yeast extract, 0.1 %?w/v, glycerol concentration of 0.1 %?v/v, and inoculum density of 2.5 %?v/v at initial dye concentration of 100 mg/L at 37 °C. Decolorization efficiency of 98 % was achieved in only 5 h. The kinetic of AO7 decolorization was found to be first order with respect to dye concentration with a k value of 0.87/h. FTIR, desorption study, UV–Vis spectral analysis, FESEM, and HPLC findings indicated that the decolorization of AO7 was mainly due to the biosorption as well as biodegradation of the bacterial cells. In addition, HPLC analyses also showed the formation of sulfanilic acid as a possible degradation product of AO7 under facultative anaerobic condition. This study explored the ability of E. faecalis strain ZL in decolorizing AO7 by biosorption as well as biodegradation process.     

Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes

This study develops and tests a novel optimization method for optimally selecting and sizing stormwater control measures (SCMs) in urban landscapes for selected design storms. The developed methodology yields SCMs that capture and retain stormwater via onsite percolation, remove stormwater pollutants, and minimize stormwater control expenditures. The resulting environmental optimization problem involves integer and real variables imbedded in an objective function that is subjected to multiple constraints. This study's methodology aims at practicality and ease of implementation in the solution of the SCM sizing and selection optimization problem while taking into account the main factors that govern stormwater management in urban landscapes. The near‐optimal global solution of the SCM selection and design problem is obtained with nonlinear programming and verified with the average of multiple solutions calculated with multiple runs of an optimization evolutionary algorithm. The developed methodology is illustrated with one stormwater project in the City of Los Angeles, California.     

Epidemiological and biomechanical evaluation of airline baggage handling

Objectives. Musculoskeletal disorders (MSDs) are prevalent among airline baggage handlers due to manual materials handling. In this study, the Nordic musculoskeletal questionnaire (NMQ), the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation, and the University of Michigan 3D Static Strength Prediction Program? (3DSSPP) were used to analyze MSDs among baggage handlers. Methods. The NMQ was filled out by 209 baggage handlers and 46 arbitrarily selected baggage handlers were evaluated using the NIOSH method and 3DSSPP. Results. The obtained results showed that the most common MSDs occurred in the lower back region. The next risky regions included knees, neck, and upper back, respectively. The NIOSH results confirmed that the subjects lifted loads heavier than the permitted limit and their lifting postures were inappropriate. The results of the 3DSSPP also indicated that compression forces exceeded the NIOSH limit in these awkward postures. Conclusions. Relying on this study, holding compulsory ergonomic lifting training courses could be proposed for workers and regulations adjusting an upper limit for maximum baggage weight must be also enacted in order to improve occupational health and prevent the prevalence of increasing MSDs.     

The role of burnout syndrome as a mediator for the effect of psychosocial risk factors on the intensity of musculoskeletal disorders: a structural equation modeling approach

This study examined the hypothesis that burnout syndrome mediates effects of psychosocial risk factors and intensity of musculoskeletal disorders (MSDs) among hospital nurses. The sample was composed of 415 nurses from various wards across five hospitals of Iran's Hamedan University of Medical Sciences. Data were collected through three questionnaires: job content questionnaire, Maslach burnout inventory and visual analogue scale. Results of structural equation modeling with a mediating effect showed that psychosocial risk factors were significantly related to changes in burnout, which in turn affects intensity of MSDs.     

Biosynthesis of iron nanoparticles and their application in removing phosphorus from aqueous solutions

This paper reports the biosynthesis of nanoscale zero-valent iron (nZVI) using the extracts of Shirazi thyme leaf (Th-nZVI) and pistachio green hulls (P-nZVI). Scanning electron microscopy verified the successful synthesis of the poorly crystalline nZVI with a spherical shape and diameter in the range of 40–70 nm. According to X-ray diffraction and Fourier transform infrared spectroscope analyses, the synthesised nZVI were composed of iron oxides nanoparticles and ployphenol obtained from Shirazi thyme leaf and pistachio green hulls extracts acting as both reducing and capping agents. The phosphorus removal efficiency of Th-nZVI and P-nZVI increased with time and reached equilibrium at about 4 and 2h, respectively. Sorption of phosphorus on both sorbents was observed to be pH-dependent with maximum phosphorus removal occurring in the pH range of 2–5. Langmuir, Freundlich, Redlich–Peterson, and Temkin models were used to describe phosphorus sorption at pH 5 and maximum sorption capacity for Th-nZVI and P-nZVI was about 40.52 and 29.33?mg?g^?1, respectively. Correlation coefficient (R²) and standard errors of estimate showed that the Elovich model was better than other models at describing the kinetic data. These results suggested that the synthesised nZVI with Shirazi thyme leaf and pistachio green hulls extracts could be employed as an efficient sorbent for the remediation of phosphorus from contaminated water sources.     

Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network

The purpose of this article is Artificial Neural Network (ANN) modeling using ecological and associated factors with forest degradation to predict the degradation of ecosystem, thereby enabling us to assess the environmental impacts of forest projects as an Environmental Decision Support System (EDSS). Results of the Multi-Layer Feed-Forward Network (MLFN), trained for Optimized Forest Degradation Model (OFDM), indicate that the performance of OFDM is more than other degradation models. Changes in forest management activities with higher value in sensitivity analysis help forest managers to decrease OFDM entity and environment impacts. The system is an intelligent EDSS, which allows the decision-maker to model criteria in forest degradation in order to reach and employ the optimal allocation plan. Considering results, multi criteria decision analysis (MCDA) approaches based on ANN, is an encouraging and robust method for solving MCDA problems.