Simultaneous Cr (VI) reduction and diazinon oxidation with organometallic sludge formation under photolysis: kinetics,degradation pathways,and mechanism

Environmental Science and Pollution Research - In this study, simultaneous removal of an organic matter (diazinon, DIZ) and an inorganic substance (chromium, Cr) was used. Breaking down of organic...     

Simultaneous effect of shift work on blood pressure and lipid profile: a path analysis

The aim of this study was to assess the direct and indirect effects of shift work (SW) on the systolic blood pressure (SBP) and lipid profile of male workers. For this purpose, a cross-sectional study was conducted involving 6539 male workers of Esfahan Mobarakeh Steel Company. From all of the participants, 3065 (46.8%) were day workers and the remainder were shift workers. The results of path analysis revealed that the most effective variables on SBP were body mass index, fasting blood sugar, triglycerides and SW with a total effect of 0.241, 0.095, 0.064 and 0.056, respectively.     

Assessment of mobile and potential mobile trace elements extractability in calcareous soils using different extracting agents

• DTPA and NH₄OAc, HNO₃ and EDTA, and MgCl₂ and NH₄NO₃ had similar behavior. • In NH₄OAc, DTPA, and EDTA, the possibility of re-adsorption of trace elements is low. • CaCl₂ may be more suitable than other extracts in calcareous soils. Understanding trace elements mobility in soils, extracting agents, and their relationships with soil components, are essential for predicting their movement in soil profile and availability to plants. A laboratory study was conducted to evaluate extractability of cadmium (Cd), cobalt (Co), copper (Cu), nickel (Ni), and zinc (Zn) from calcareous soils utilizing various extracting agents to be specific CaCl₂, DTPA, EDTA, HNO₃, MgCl₂, NaNO₃, NH₄NO₃, and NH₄OAc. Cluster analysis indicated that DTPA and NH₄OAc, HNO₃ and EDTA, and MgCl₂ and NH₄NO₃ extracting agents yielded comparative values, whereas NaNO₃ and CaCl₂ have shown different behavior than other extracting agents for all studied trace elements. The speciation of extracted trace elements in solutions indicated that in the CaCl₂, NaNO₃, NH₄NO₃, and MgCl₂ extracting agents most extracted Cd, Co, Ni, Zn, and part of Cu were as free ions and may be re-adsorbed on soils, leading to lower extractability, whereas, in the case of HNO₃ extracting agent, the likelihood of re-adsorption of trace elements may be little. The results of speciation of trace elements using NH₄OAc, DTPA, and EDTA extracting agents showed that Me-(Acetate)₃^–, Me-(Acetate)₂(aq), Me(DTPA)³⁻, Me(EDTA)²⁻, and MeH(EDTA)^– complexes dominated in solutions indicating that the extracted trace elements may not be re-adsorbed on soils, leading to higher extractability. The results of this study are useful for short and long-term evaluations of trace elements mobility and further environmental impacts.     

The Comparison of Lagrangian and Gaussian Models in Predicting of Air Pollution Emission Using Experimental Study,a Case Study: Ammonia Emission

Ammonia, as a colorless gas with a sharp odor, is considered as one of the created odors in the composting and landfill of solid waste. We used experimental data to study the robustness of AERMOD and the forward Lagrangian stochastic (FLS) in predicting ammonia emission in short range. The study area was Kahrizak landfill and composting plants, Tehran, Iran. The boundary layer parameters for the FLS were calculated on the basis of mean values of temperature, wind speed, and direction. While, the boundary layers of AERMOD were computed on the basis of exact meteorological data. The results depicted that AERMOD prediction at distances less than 1000 m from the sources and the locations inside the sources were poor. However, the results of FLS indicated more agreement with the field measurement, which the coefficient of determination was 0.83. Both models predicted, in the distance of 2000 m from the source, the ammonia concentration would be insignificant.     

Modeling biodegradation and kinetics of glyphosate by artificial neural network

An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (< 8) exhibited the inhibitory effect of glyphosate on bacteria growth. The value of K(i)/K(s) increased when the mixed inoculum size was increased from 10(4) to 10(6) bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed.     

Molecular diversity of arbuscular mycorrhizal fungi in relation to soil chemical properties and heavy metal contamination

Abundance and diversity of arbuscular mycorrhizal fungi (AMF) associated with dominant plant species were studied along a transect from highly lead (Pb) and zinc (Zn) polluted to non-polluted soil at the Anguran open pit mine in Iran. Using an established primer set for AMF in the internal transcribed spacer (ITS) region of rDNA, nine different AMF sequence types were distinguished after phylogenetic analyses, showing remarkable differences in their distribution patterns along the transect. With decreasing Pb and Zn concentration, the number of AMF sequence types increased, however one sequence type was only found in the highly contaminated area. Multivariate statistical analysis revealed that further factors than HM soil concentration affect the AMF community at contaminated sites. Specifically, the soils’ calcium carbonate equivalent and available P proved to be of importance, which illustrates that field studies on AMF distribution should also consider important environmental factors and their possible interactions.     

Measurement and management of hospital waste in southern Iran: a case study

One of the requirements for development of human societies is the establishment of new healthcare centers. A variety of wastes are generated in healthcare centers depending on the type of activities. This study was conducted to identify, measure and manage different types of hospital wastes as a case study in a hospital located in southern Iran. For this purpose, a questionnaire was initially designed and distributed among the relevant experts to survey the current trend of waste management in the hospital in terms of waste collection, storage and disposal. Afterwards, the hospital waste was sampled during two seasons of fall and winter. The samples were weighted for seven consecutive days in the middle of each season. Approximately, 10 % of the total waste bags per day collected round the clock were selected for further analysis. The obtained results indicated that infectious-hazardous and pseudo-household wastes were, respectively, about 3.79 kg/day/bed, 1.36 kg/day/bed and 2.43 kg/day/bed of the total generated waste in the hospital. As the research findings suggest, proper separation of infectious and pseudo-household wastes at the source would be an essential step towards mitigating environmental and health risks and minimizing the cost of the hospital waste management.     

Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites

In this research, an experimental study of reinforcing alumina nano-particles into the aluminum alloy matrix was implemented to verify the accuracy of modeling results obtained by feed forward neural networks. Artificial neural network combined with numerical technique were used to predict the various parameters of mechanical properties such as hardness, tensile and compressive yield stress, UTS and elongation percentage. Much experimentation were taken to discover a suitable number of hidden neurons, avoid detraction from the trainability and enable feed forward neural networks to solve more complex problems. The predictions were found to be consistent with experimental measurements.