Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province,Iran

Wind energy is a renewable energy resource that has increased in usage in most countries. Site selection for the establishment of large wind turbines, called wind farms, like any other engineering project, requires basic information and careful planning. This study assessed the possibility of establishing wind farms in Ardabil province in northwestern Iran by using a combination of analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) methods in a geographical information system (GIS) environment. DEMATEL was used to determine the criteria relationships. The weights of the criteria were determined using ANP and the overlaying process was done on GIS. Using 13 information layers in three main criteria including environmental, technical and economical, the land suitability map was produced and reclassified into 5 equally scored divisions from least suitable to most suitable areas. The results showed that about 6.68 % of the area of Ardabil province is most suitable for establishment of wind turbines. Sensitivity analysis shows that significant portions of these most suitable zones coincide with suitable divisions of the input layers. The efficiency and accuracy of the hybrid model (ANP-DEMATEL) was evaluated and the results were compared to the ANP model. The sensitivity analysis, map classification, and factor weights for the two methods showed satisfactory results for the ANP-DEMATEL model in wind power plant site selection.     

Transition index maps for urban growth simulation: application of artificial neural networks,weight of evidence and fuzzy multi-criteria evaluation

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.     

Daily Precipitation Extremes in Iran: Decadal Anomalies and Possible Drivers

This study focuses on the empirical statistical analysis of the anomalies in daily precipitation extremes by applying the quantile perturbation method (QPM) to data from 31 Iranian weather stations during the period between 1961 and 2005. The possible causes behind the anomalies in precipitation extremes are identified by analyzing their relationship with the anomalies in eight atmospheric indices (i.e., NAO, SOI, PDO, AMO, NCP, DMI, WeMO, SSN). In terms of decadal oscillations, the country was generally wet in the 1960s and 1970s with most stations exhibiting periods of higher quantile perturbations, whereas lower quantile perturbations were dominant in the 1980s and 1990s. The highest perturbation in extreme precipitation quantiles prevails in Central Iran during the early 1980s, in which the quantiles are about 50% higher than the ones based on the full time series. The frequency of significant precipitation anomalies for winter season was greater than that for spring and autumn seasons. For the summer season, the humid region in North Iran demonstrates strong positive anomalies. The results highlight the noticeable role of large‐scale climatic factors in the anomalous behavior of precipitation extremes in Iran. The atmospheric drivers of the quantile anomalies in extreme precipitation were found to differ from one season to another.     

Mathematical Model for Pollution Source Identification in Rivers

Release of pollution into rivers is required to be handled with special consideration to environmental standards. For this purpose, it is essential to specify the contribution of each pollution source in contamination of water resources. In this study, a mathematical model is proposed for determining locations and concentration release histories of polluting point sources using measured downstream river concentrations via an inverse problem framework. The inverse solution is based on the integral equation obtained from applying the Green's function method on the one-dimensional advection-dispersion contaminant transport equation. Discretization of this integral equation results in a linear, over-determined and ill-posed system of algebraic equations that are solved by using the Tikhonov regularization method. Several examples and some real field data are investigated to illustrate the abilities of the proposed model. Results imply that the proposed method is effective and can identify the pollution sources in rivers with acceptable accuracy.     

GIS aided prediction of CO2 emission dispersion from geothermal electricity production

CO₂ is the dominant constituent of non-condensable gases in the steam phase of most geothermal fluids. This paper attempts to present the results of a study conducted to develop prediction modeling of CO₂ dispersion in the surrounding atmosphere from a planned 50 MWe geothermal power plant prior to its production. Dispersion models are widely used for predicting future concentrations of environmental emissions on a range of geographic scales. The dispersion type for gases and their average removal rate depends on the meteorological conditions such as wind direction, wind speed, precipitation, atmospheric stability, and surface roughness and topography. Geographic Information System (GIS) capabilities were used for quality visualization of the model outputs and presenting an accurate numerical copy of the study area. The results by the prediction model show that the natural transfer of CO₂ will be from the power plant toward east and northeast and CO₂ concentration trends after the power plant utilization will be similar to the background conditions with minor changes. This dispersion test was carried out to validate and field test the GIS aided dispersion modeling approach described in the paper and the procedure may be applicable for other studies assessing the emission dispersion of pollutants from a point source.     

Minimum quantity lubrication in grinding: effects of abrasive and coolant–lubricant types

This research has been conducted to study the influence of the abrasive and coolant–lubricant types on the minimum quantity lubrication (MQL) grinding performance. One type of CBN and three types of conventional wheels (corundum) have been tested. The tests have been performed in presence of fluid, air jet and eleven types of coolant–lubricants, as well as, in dry condition. The results indicate that the finest surface quality and lower grinding forces could be obtained while grinding with CBN wheel. In case of conventional wheels, the coarser and high porosity wheels induce much proper grinding results. Furthermore, grinding when utilizing MQL with oil results in higher grinding performance.     

Bioaccumulation of heavy metals in fish species of Iran: a review

Environmental Geochemistry and Health - Accumulation of heavy metals (HMs) in fish tissues is an important factor in monitoring the health and safety of aquatic ecosystems. Furthermore, fish are...     

Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region

Environment, Development and Sustainability - Spatial simulation of land-use change scenarios in metropolitan areas is essential for analyzing both the causes and consequences of various future...