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Sand and dust storms are causing hazards and problems in aviation as well as the dangers and distresses they cause on living things. The low meteorological visibility, the presence of strong winds with gust, and the transport of dust and sand particles by the wind are dangerous on landing and departure of aircrafts, and even on planes that are parked. The main aim of the study is to examine the meteorological conditions causing the dust storm that took place in the Arabian Peninsula on February 28, 2009, and to determine the source of dust caused dust storm, sand storm, blown sand, and blown dust at the airports. Within the scope of the study, aviation routine weather report (METAR) and aviation selected special weather report (SPECI) reports have been assessed at many airports over Arabian Peninsula (AP), the northern part of the AP and North Africa. As model outputs; NCEP–DOE Reanalysis 2 data, BSC–DREAM8B, and HYSPLIT model were used. In the satellite images; METEOSAT MSG dust RGB images, MODIS, and Kalpana-1 data were used. According to obtained results, dust storms were detected in Bahrain, Kuwait, Saudi Arabia, and United Arab Emirates (UAE). At Al-Ahsa Airport in Saudi Arabia, the lowest visibility measured on February 28, 2009, dropping to 200 m, which was dominated by blowing sand. The source region of the dust observed at Dubai Airport in UAE is the eastern regions of the Rub’al Khali Desert located between Saudi Arabia, Oman, and UAE. 相似文献
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The soil 222Rn concentration non-linear patterns are investigated by the application of various chaos methodologies based on 70 272 measurement data from the East Anatolian Fault Zone, which is one of the world’s most active faults. Among these methodologies are Lyapunov exponent, surrogate data, rescaled range (R/S) analysis, Fourier spectrum, phase space reconstruction, mutual information, false nearest neighbors, and correlation dimension. The results indicate that the nonlinear dynamical approach is convenient for characterization and prediction of the 222Rn concentration dynamics, which are in turn usually used as an earthquake precursor. Behaviour of 222Rn gas is important in earthquake prediction researches. 相似文献
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We assessed the ambient noise level in the Aegean region and analyzed its diurnal variation and its relation to the earthquake detection capability of the Aegean Region Seismic Network (ARSN). We prepared probability density functions (PDFs) for 19 broadband stations in the Aegean region operated by the Earth and Marine Sciences Institute (EMSI) of the Marmara Research Center (MRC) of the Turkish Scientific Research Council (TÜB?TAK). The power spectral densities (PSDs) used to construct PDFs for each station were computed for the periods between ~?0.02 and 180 s. In addition, we generated noise map of the Aegean region for different periods using the PDFs to assess the origin of the noise. We analyzed earthquake activity in the region and found that there are more local events recorded at night than during the day for each station. This difference is strongly related to diurnal variation of background noise level for the period range mostly covering the frequency range for the local events. We observed daytime noise level ~?15 to 20 dB higher than that at the nighttime in high frequencies for almost all stations caused by its proximity to settled areas and roads. Additionally, we observed a splitting peak within the Double Frequency (DF) microseism band; it showed a clear noise increase around the short period DF band at all the stations, decreasing inland. This peak may be related to sea waves locally generated in the Aegean Sea. We also identified a prominent increase related to marble saw companies in some stations’ noise PDFs. 相似文献
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Levent Şaylan Barış Çaldağ Fatih Bakanoğulları Hüseyin Toros Mustafa Yazgan Orhan Şen Yunus Özkoca 《洁净——土壤、空气、水》2011,39(5):491-501
Pollutants affect not only the environment in which they originate since they are also transported by air currents to other locations. For this reason, air pollution is a global problem for all countries and the air and water quality need to be monitored carefully. More information on precipitation chemistry is required to determine the source of pollution as well as its effects on the ecosystems. In this study, precipitation chemistry has been analyzed for the first time by using simple bulk collectors located at four different sites in Northwest Turkey for a period of two years. About 650 sequential rainwater samples were collected and analyzed for pH, electrical conductivity, anions such as , , Cl?, and cations such as Na+, K+, Mg2+, Ca2+, and . The selected sites were under the effect of different environmental factors. Our results showed that the highest and concentrations were measured in the Northwest of the research area. Additionally, it has been found that Ca2+ ions are abundant within all rainwater samples. 相似文献
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A coal mine panel claimed to infringe on a permit boundary, or to create an hazard after subsidence has been the subject of lawsuits. We study at the Soma–Darkale coalfield (Manisa, Turkey), the footprint of a lignite coal mine panel at a depth of about 150–200 m by mapping all of the surface fractures we could observe, and by developing a post-subsidence density model that we verified through gravity measurements with positive Bouguer anomaly. With the analysis of the fracture map and the gravity data, we were able to identify the footprint of a mine panel from the effect of the anomalous mass due to denser overburden material filling up the space after the extraction of less-dense lignite. Whereas, using empirical methods like the so-called “limit line approach” to evaluate the extent of the area where mining can have subsidence-induced surface fracturing, one could not recognize for certain infringement of permit boundaries. The orientations of the fractures we mapped at the ground surface, the Bouguer gravity map, and a test borehole indicated the presence and dimensions of a coal panel in dispute. The presented approach based on gravity method and fracture observations may be an example to help settle conflicts related to the position of the longwall mine panel. 相似文献
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深度残差网络的多光谱遥感图像显著目标检测 总被引：2，自引：2，他引：0
本文侧重于介绍智能化摄影测量深度学习的深度残差方法。显著目标检测致力于自动检测和定位图像中人最感兴趣的目标区域。多波段遥感图像因其更加丰富的光谱信息和揭示观测目标物理属性的能力在目标检测中获得重要应用。传统的显著目标检测方法通过手工设计特征,计算图像各像素或者超像素与邻域像素或者超像素之间的对比度检测显著目标。随着深度学习的巨大发展,特别是全卷积神经网络的引入,基于深度卷积网络的显著目标检测算法取得重要进步。然而,由于数据获取和标记的困难,多波段遥感图像显著目标检测的研究依然主要采用手工设计特征。本文研究基于深度卷积神经网络的多波段遥感图像显著目标检测算法,提出一种基于深度残差网络的自上而下的多光波段遥感图像显著目标检测网络,该网络可以有效挖掘深度残差网络不同层次上的显著性特征,以端对端方式实现显著目标检测。为了应对多波段遥感图像数据量有限、无法训练深度残差网络的问题,本文提出通过浅层神经网络从RGB图像直接生成多波段遥感图像,实现光谱方向的超分辨率。在现有多波段遥感图像和可见光图像显著目标检测数据集上的试验结果超过当前最好方法10%以上,验证了本文方法的有效性。 相似文献
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ABSTRACTNumerous statistical downscaling models have been applied to impact studies, but none clearly recommended the most appropriate one for a particular application. This study uses the geographically weighted regression (GWR) method, based on local implications from physical geographical variables, to downscale climate change impacts to a small-scale catchment. The ensembles of daily precipitation time series from 15 different regional climate models (RCMs) driven by five different general circulation models (GCMs), obtained through the European Union (EU)-ENSEMBLES project for reference (1960–1990) and future (2071–2100) scenarios are generated for the Omerli catchment, in the east of Istanbul city, Turkey, under scenario A1B climate change projections. Special focus is given to changes in extreme precipitation, since such information is needed to assess the changes in the frequency and intensity of flooding for future climate. The mean daily precipitation from all RCMs is under-represented in the summer, autumn and early winter, but it is overestimated in late winter and spring. The results point to an increase in extreme precipitation in winter, spring and summer, and a decrease in autumn in the future, compared to the current period. The GWR method provides significant modifications (up to 35%) to these changes and agrees on the direction of change from RCMs. The GWR method improves the representation of mean and extreme precipitation compared to RCM outputs and this is more significant, particularly for extreme cases of each season. The return period of extreme events decreases in the future, resulting in higher precipitation depths for a given return period from most of the RCMs. This feature is more significant with downscaling. According to the analysis presented, a new adaption for regulating excessive water under climate change in the Omerli basin may be recommended. 相似文献