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Management of air pollution in Tehran, Iran, has been a significant challenge for urban authorities in recent years owing to the number and complexity of the factors affecting the formation and spread of the pollutions. The present study used an integrated modeling approach involving Spatio, Temporal, Uncertainty Decision Support Systems (STUDSS) using Multi Criteria Decision Analysis (MCDA) and an Artificial Neural Network (ANN) for the virtual simulation and strategy assessment of air pollution. Since sources of air pollution and associated pollution control strategies are dependent on location, time and uncertain variables, Multi-Dimensional Decision Support System (MDDSS) can be efficient tool for urban air quality decision-making process. In order to model and evaluate air pollution, time-series data over a period of four years, screened and classified management strategies as well as other structural and environmental data such as land uses, terrain topography, heights of buildings, climatic conditions, population density and pollution sources were modeled using advanced software packages of MCDA and ANN. They were ultimately simulated and evaluated using the MDDSS. The results obtained from the implementation of the STUDSS showed that this tool could be used to provide sustainable solutions to air quality in metropolises and could respond to social satisfaction and economic development.
In recent years humans have endured increasing numbers of natural disasters, of which flooding is the greatest and most common throughout the world. Iran is also exposed to floods, considering the severe damage recently incurred in Golestan province, particularly Gorganroud watershed. Due to the importance of the subject and lack of comprehensive studies on flood risk in the country's watersheds, it is crucial to perform flood risk assessment using appropriate tools, such as Landsat ETM+ imaging and digital elevation model data collections in geographic information system throughout the region. For this purpose, database maps of 6 subwatersheds in Gorganroud watershed were prepared in 5 layers affecting flooding in the region. By overlaying and weighing three layers in GIS software, a layer of flood hazard intensity was obtained. Next, by means of obtained numbers and scoring, the overuse layer priorities were determined. Then, these two flooding layers were overlaid with the help of a two-dimensional matrix, and the final map of flood risk was obtained. Finally, it was found that Chelichay and Sarab Gorganroud, making up to 24.59% of the Gorganroud watershed, are the most risky sub-watersheds. In light of the fact that the data pertaining to Gorganroud watershed have never been entirely used to sort out the risk priorities in the region, the new method presented in this paper can lead to a more accurate and comprehensive understanding about what is really taking place in it.
This is a two-phase study to investigate the socioeconomic impacts of the Caspian Sea Level Rise (CSLR) on Anzali International Wetland at the southern fringe of the Caspian Sea. In the first phase, a Landsat satellite image (2013) and digital elevation model (DEM) of the wetland were used to determine the areas vulnerable to the CSLR-induced flooding under four water level rise scenarios of 0.2 m, 0.6 m, 1 m, and 1.4 m. Then in the second phase, the possible effects of the CSLR on some market values of Anzali Wetland and the livelihood of the wetland-dependent communities were assessed based on the loss of agricultural and fishing products (as two main sources of livelihood for local people), as well as the loss of different land uses surrounding the wetland. According to the results, under the most optimistic CSLR scenario of 0.2 m, the wetland area will be expanded from 19,095 to 24,942 ha, while an expansion of 19,353 ha (from the current area of 19,095 ha to 38,448 ha) is expected under the most pessimistic CSLR scenario of 1.4 m. This will affect a minimum number of eight villages, including 4,518 inhabitants (under the CSLR scenario of 0.2 m) and a maximum number of 41 villages including 22,493 inhabitants (under the 1.4 CSLR scenario). These people will have to displace and move from their homes, which leads to several social ills. Depending on the severity of water level rise under various scenarios, 545, 646, 670, and 699 ha of the total area (790 ha) of fish ponds will be destructed, and total numbers of 70, 76, 83, and 93 units out of the 172 active industrial units are predicted to be inundated. In sum, the total loss values (damage to agriculture and fish farming) under the CSLR scenarios of 0.2, 0.6, 1, and 1.4 m were estimated to be $63 million (USD), $117 million, $151 million, and $184 million, respectively. Our research findings can help policy makers develop proper adaptation measures to prohibit or reduce the possible socio-economic damage caused by the CSLR in the future.
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