The spatial distribution dynamic and convergence of CO2 emissions in Iran’s provinces

Environmental Science and Pollution Research - It is essential to study CO2 emissions intensity as the most critical factor affecting temperature increase and climate change in a country like Iran,...     

Mitigating Impact of Devils Lake Flooding on the Sheyenne River Sulfate Concentration

Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m³/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.     

Factors Affecting Predator-Prey Distribution in a Protected Area,Tehran, Iran (a Case with Wolves and Wild Sheep)

We tried to model habitat suitability of two prey and predator species including wild sheep (Ovis orientalis) and wolf (Canis lupus) in Varjin protected area located in northern east of Tehran using a presence only method, maximum entropy (MaxEnt). Totally 11 environmental variables were measured in the species presence points which can be classified in three groups including topographical, vegetation and distal variables. Resulted maps indicated that habitat variables such as slope (ranging from 35 to 40 percent) and elevation (lower than 1700 meters above sea level) are both institute those factors which mostly affect studied prey and predator habitat use. Our results regarding prey and predator geographical range of used habitat indicated that wolves cover most area than wild sheep which show more dispersed habitat resources for the prey species. ENMTools test revealed that wolf’s niche breadth is more than twice as much as wild sheep’s. Wild sheep in Varjin protected area has a relatively narrow geographical extent and shows a tendency to marginal habitats while wolves cover obviously more areas which denotes its high mobility and low dependency to specific habitats.     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

Examining Relationships Between Socioeconomic Factors and Landscape Metrics in the Southern Basin of the Caspian Sea

Socioeconomic forces are not only among the main drivers of landscape dynamics; they are also influenced by landscape patterns. Landscape structure and functions are closely related to natural and social factors. The objective of this study was to investigate the relationships among some human-related factors and landscape ecological metrics as landscape pattern indicators and to identify suitable metrics for modeling these relationships. To this goal, landscape ecological metrics were calculated for each of the 32 counties of Mazandaran and Guilan provinces located in the southern basin of the Caspian Sea using land use/cover maps in class level. Stream network metrics were calculated using a digital elevation model, road density metrics were calculated using map of main roads separately, and significant metrics were selected according to results of correlation tests and factor analysis. The correlations between these metrics and socioeconomic factors were tested, and their relationships were modeled with multiple linear regressions. Significant relationships were found among socioeconomic factors and landscape ecological metrics, and land use/cover data are applicable for modeling socioeconomic factors, especially demographic and employment structure factors. Among the landscape metrics applied in this study, road density, mean patch size, mean nearest neighbor distance, and percentage of a land use/cover class in landscape were important metrics for predicting socioeconomic factors. Our findings indicated that road density metric and percentages of urban class are useful for predicting urban socioeconomic factors and percentage of agriculture and forest classes in the landscape are suitable metrics for predicting rural socioeconomic factors.     

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.     

Investigating effect of proton-exchange membrane on new air-cathode single-chamber microbial fuel cell configuration for bioenergy recovery from Azorubine dye degradation

Environmental Science and Pollution Research - One of the biggest challenges of using single-chamber microbial fuel cells (MFCs) that utilize proton-exchange membrane (PEM) air cathode for...     

Modification of photocatalytic property of BaTiO3 perovskite structure by Fe2O3 nanoparticles for CO2 reduction in gas phase

In this work, perovskite structure of BaTiO₃ was coupled with Fe₂O₃ in different molar ratios achieving the best photocatalytic performance of CO₂ reduction in the presence of CH₄ as reducing agent; both of them are main greenhouse gases. The photocatalysts were synthesized by facile hydrothermal method. The samples were characterized by XRD, FTIR, FESEM, EDX, UV–Vis DRS, and photoluminescence (PL) analyses. The BaTiO₃ synthesized in this research showed a weak PL signal which is due to the intrinsic ferroelectric property as has been observed in previous reports. Compared to the pure BaTiO₃ and Fe₂O₃, the heterojunctions exhibited enhanced photocatalytic activity. The maximum CO₂ reduction under visible light irradiation was obtained to be 22% during 60 min process time. The enhanced photocatalytic activity could be attributed to the increased optical absorption, the good separation, and immigration of photogenerated charge carriers that decreased the recombination rate of charge carriers in the n–n heterojunction.