Power spectrum of geomagneticK p-index detected at Misallat magnetic station

TheK _p-indices measured at Misallat station [ø = 29°45 N; = 30°54 E] during the period 1958–1989, have been compared with the data of two geomagnetic stations; one of them has similar longitude as Misallat and the second has almost similar latitude. The spectra of daily and hourly data of the three stations are compared together to define the latitude and longitude-effects on the detected periodicities. Daily periodicities ofK _p-index activities at the values 32.7, 21.2, 12.9, 11.6 and 9.2 days have been obtained to be common in the spectra of the three stations. The existence of 5-days periodicity in these stations has also been detected.     

The chromite deposits associated with ophiolite complexes, Southeastern Desert, Egypt： Petrological and geochemical characteristics and mineralization

1 Introduction The association of massive Fe-Ni-Cu sulfides andchromite is a very unusual feature of podiformchromitites occurring in mantle tectonites of ophioliticcomplexes. It has only been described in theSoutheastern Desert, Egypt, where sulfides a…     

Water availability for the greening of Kuwait

Water resources for agricultural development and landscape enhancement in Kuwait are very scarce; the quality is saline (brackish), and the soil texture is sandy, incapable of holding nutrients and moisture. Most of the rainfall in Kuwait either evaporates due to the high temperatures or percolates through the soil. The rainfall is not sufficient to recharge the underground water supply. A variety of alternative water sources have been studied, including seawater desalination and wastewater treatment. Wastewater treatment proved to be convenient due to the relatively low salinity detected in the treated water. Furthermore, the amount of wastewater is expected to increase as more residential areas are connected to the sewage system as the population increases.

The development of a greenery plan for Kuwait requires intensive water management. This goal can be achieved through manpower training in landscape development, selection of plants tolerant to arid environments, usage of appropriate irrigation and drainage systems, promotion of greenery programs within public and the governmental agencies for their direct participation in urban area beautification, and extension of wastewater treatment techniques.     

浙江花岗岩中斜长石钾交代产生的条纹长石

对研究区内花岗岩类的60个薄片进行了岩相学研究.研究的目的是确定区内岩石中普遍存在的矿物组成及结构类型.     

俄罗斯贝加尔湖-日本石卷地学断面西段地质-地球物理基本特征

对于贝加尔湖-石卷地学断面(BAMSIP)的西段俄罗斯贝加尔湖-中国满洲里断面城内的地质构造背景、地震剖面波类型和基本特征等研究发现：(1)断面域中贝加尔裂谷带地震波速度结构存在异常地幔带；结晶地壳物质成分基性程度较高；基底顶面和Moho界面未观察到明显的镜象关系；(2)西伯利亚南部的复杂相故基底由古生代和前寒武纪岩层所组构；区域构造由古褶皱系、中生代沉积盆地、裂谷带构成。     

Comparative analysis of Lake Urmia and Lake Van water level time series

Recent decline in Lake Urmia water level makes it crucial to consider this issue more seriously. For this purpose, comparison of water level in Lake Urmia with Lake Van in Turkey, which is in relatively similar geographic and climate conditions, can be an effective approach. To follow this objective, trend analysis, regime shift, and coherency analyses are implemented. The results showed negative trend in Lake Urmia water level for the past 20 years, while in Lake Van, the trend is positive. Moreover, correlation of the lake level variations versus the basin rainfall during three common periods, identified by the regime shifts analysis, illustrated a decreasing trend in the correlation. These changes can be attributed to non-climatic factors such as different allocation disciplines in two lake’s catchments. Finally, the coherency analysis showed significant annual and inter-annual frequencies common between the two lake levels. Herein, the short-term period relations are associated with lags, while in long term, they act simultaneously.     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

Air pollution forecasting from sky images with shallow and deep classifiers

Air pollution is one of the most important problems in the new era. Detecting the level of air pollution from an image taken by a camera can be informative for the people who are not aware of exact air pollution level be declared daily by some organizations like municipalities. In this paper, we propose a method to predict the level of the air pollution of a location by taking an image by a camera of a smart phone then processing it. We collected an image dataset from city of Tehran. Afterward, we proposed two methods for estimation of level of air pollution. In the first method, the images are preprocessed and then Gabor transform is used to extract features from the images. At the end, two shallow classification methods are employed to model and predict the level of air pollution. In the second proposed method, a Convolutional Neural Network(CNN) is designed to receive a sky image as an input and result a level of air pollution. Some experiments have been done to evaluate the proposed method. The results show that the proposed 9 method has an acceptable accuracy in detection of the air pollution level. Our deep classifier achieved accuracy about 59.38% which is 10 about 6% higher than traditional combination of feature extraction and classification methods.     

Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques

In recent decades, attaining urban sustainability is the primary goal for urban planners and decision makers. Among various aspects of urban sustainability, environmental protection such as agricultural and forest conservations is very important in tropical countries like Malaysia. In this regard, compact urban development due to high density, rural development containment is known as the most sustainable urban forms. This paper attempts to propose an integrated modeling approach to predict the future land use changes by considering city compactness paradigms. First, the cellular automata (CA) were applied for calculating land use conversion. Next, weights-of-evidence (WoE) which is based on Bayes theory was utilized to calibrate CA model and to support the transitional rule assessment. Several urban-related parameters as well as compact city indicators were utilized to estimate the future land use maps. The results showed how compact development parameters and site characteristics can be combined using the WoE model to predict the probability of land use changes. The modeling approach supports the essential logic of probabilistic methods and indicates that spatial autocorrelation of various land use types and accessibility is the main drivers of urban land use changes.     

Precipitation rate climatology related to different cloud types using satellite imagery over Iran

Cloud types have a substantial influence on precipitation. This paper presents a study of the monthly variations of daytime different cloud types over Iran using data collected from Moderate-resolution Imaging Spectroradiometer (MODIS) aboard Terra during 2001–2015, MODIS aboard Aqua during 2002–2015, International Satellite Cloud Climatology Project (ISCCP) H-series cloud type data during 2001–2009 and precipitation rate associated with different cloud types using Tropical Rainfall Measuring Mission (TRMM) satellite products during 2001–2009. Different cloud types were determined using MODIS cloud optical thickness and cloud top pressure data based on ISCCP algorithm. The results showed that stratocumulus and cumulus clouds have maximum occurrence frequency over marine areas especially southern seas. The maximum frequency of nimbostratus and deep convective occurrence occurred over mountainous regions particularly at the time of Aqua overpass and cirrus and cirrostratus are observed over southeast of Iran during warm months due to monsoon system. Altostratus cloud is extended in each month except January, at the time of Terra overpass while nimbostratus is seen at the time of Aqua overpass during warm months in the study area. Cumulus and altocumulus clouds have shown remarkable frequency in all months especially over marine regions during warm and fall months. The higher value of precipitation rate is related to altostratus with a rate approximately 7 mm/h at the time of Terra overpass during April. Altostratus has the maximum recorded precipitation rate except in Nov., Dec., Sep., and Jan. at the time of Terra overpass, whereas the maximum precipitation rate is linked to nimbostratus cloud activity (up to 5 mm/h) except for March, April, and Sep. at the time of Aqua overpass. Deep convective (up to 1.32 mm/h), cirrostratus (up to 1.11 mm/h), and cirrus (0.02 mm/h) are observed only in warm months. The results were compared with ISCCP cloud types so that precipitation rate classified from low to high and Spearman rank correlation was calculated. The results showed good agreement between these two cloud type data; however, there were few notable difference between them.