乌鲁木齐大气混合层厚度和稳定度与大气污染的关系

利用乌鲁木齐市4座10层100 m梯度气象塔2013年6月~2014年4月气象观测资料和7个环境监测站[WTBX]AQI[WTBZ]资料，计算并分析了大气混合层厚度和稳定度特征，探讨了大气混合层厚度和稳定度与污染的关系。结果表明：乌鲁木齐市混合层厚度夏季郊区高、城区低，冬季从南郊—城区—北郊随地势降低依次降低；夏季和冬季分别在1 559～1 772 m和526～1 156 m之间。地面至2 km以上每500 m高度间隔统计混合层厚度，500～1 000 m出现频率最多；月变化为6～9月基本在500 m以上，且每个高度区间其概率均超过10%，10月～次年2月1 500 m以上区间概率明显减小；日变化为中午13:00～16:00达到最高值，下午和傍晚迅速下降。白天较大的感热输送提供充足的热力条件，这也体现出白天以不稳定层结为主，夜间则以稳定层结为主。大气稳定度分类结果，夏季郊区和城区不稳定（A～C类）所占比例差不多，冬季北郊稳定（E、F类）所占比较最大、城区最弱。[WTBX]AQI指数冬季最大，从南郊—城区—北郊依次增大，这与采暖期污染物多、南郊比北郊地势高有利于扩散输送有关。总体来看，乌鲁木齐大气混合层厚度空间分布与气象要素、大气稳定度、地形等密切相关，对AQI[WTBZ]指数分布有重要影响，这对近地层大气污染状况预报有着重要的指导意义。     

Geology and Mineral Chemistry of Uranium- and Thorium-bearing Minerals in Rare-Metal (NYF) Pegmatites of Um Solimate,South Eastern Desert,Egypt

The garnet muscovite granitic pegmatite of Um Solimate, in southern Egypt, represents a promising asset for strategic and economic metals, especially Bi–Ni–Ag–Nb–Ta as well as U and Th. The ore bodies occur as large masses, pockets and/or veins of very coarse-grained pegmatites, which consist mainly of K-feldspar, quartz and albite with subordinate muscovite, garnet, and biotite. Radiometric data revealed that eU- and eTh-contents of the pegmatites reach up to 39 ppm and 82 ppm, respectively. The studied pegmatites are enriched in primary U and Th minerals (uraninite, coffinite, thorianite and uranothorite) as well as Hf-rich zircon and monazite, which give rise to anomalous radioactive zones. Niobium-tantalium-bearing minerals (i.e. ferrocolumbite, microlite and uranopyrochlore), xenotime, barite, galena, fluorite, and apatite are ubiquitous, and, consequently, the studied pegmatites belong tothe Niobium–Yttrium–Fluorine-type (NYF) family. The noble metal mineralization includes argentite (Ag2S), native Ni and Bi as well as bismite and bismoclite. In addition, beryl and tourmaline are observed in pegmatites near the contact with metasediments and ultramafic bodies. The observed compositional variations of Ta/(Ta+Nb) and Mn/(Mn+Fe) ratios in columbite (0.08–0.45 and 0.11–0.57, respectively) and Hf contents in zircon (3.54–6.46 wt%) may reflectan extreme degree of magmatic fractionation leading to formation of the pegmatite orebody.     

Towards quantitative evaluation of gas injection using time‐lapse seismic data

Of particular concern in the monitoring of gas injection for the purposes of storage, disposal or improved oil recovery is the exact spatial distribution of the gas volumes in the subsurface. In principle this requirement is addressed by the use of 4D seismic data, although it is recognized that the seismic response still largely provides a qualitative estimate of moved subsurface fluids. Exact quantitative evaluation of fluid distributions and associated saturations remains a challenge to be solved. Here, an attempt has been made to produce mapped quantitative estimates of the gas volume injected into a clastic reservoir. Despite good results using three accurately repeated seismic surveys, time‐delay and amplitude attributes reveal fine‐scale differences though large‐scale agreement in the estimated fluid movement. These differences indicate disparities in the nature of the two attributes themselves, which can be explained by several possible causes. Of most impact are the effects of processing and migration, wave interference effects and noise from non‐repeatability of the seismic surveys. This subject highlights the need for a more careful consideration in 4D acquisition, amplitude processing and use of true amplitude preserving attributes in quantitative interpretation.     

Evidence for the occurrence of hydrogeochemical processes in the groundwater of Khoy plain,northwestern Iran,using ionic ratios and geochemical modeling

Rapid population growth, industrialization, and agricultural expansion in the Khoy area (northwestern Iran) have led to its dependence on groundwater and degradation of groundwater quality. This study attempts to decipher the major processes and factors that degrade the groundwater quality of the Khoy plain. For this purpose, 54 groundwater samples from unconfined and confined aquifers of the plain were collected in July 2017 and analyzed for major cations and anions (Na, K, Ca, Mg, HCO₃, SO_4, and Cl), minor ions (NO₃ and F), and Al. Magnesium and bicarbonate were identified as the dominant cation and anion, respectively. Several ionic ratios and geochemical modeling using PHREEQC indicated that the most important hydrogeochemical processes to affect groundwater quality in the plain were weathering and dissolution of evaporitic and silicate minerals, mixing, and ion exchange. There were smaller effects from evaporation and anthropogenic factors (e.g., industries). Results showed that the high salinity of the groundwater in the northeast area of the plain was due to the high solubility of the evaporitic minerals, e.g., halite and gypsum. Reverse ion exchange and the contribution of mineral dissolution were more significant than ion exchange in the northeastern part of the plain. Elevated salinity of the groundwater in the southeast was attributed mostly to reverse ion exchange and somewhat to evaporation.     

ISPH numerical simulation of tsunami generation by submarine landslides

The present investigation focused on the numerical simulation of the gravity currents of non-Newtonian fluids by means of the incompressible smoothed particle hydrodynamics (ISPH) approach. Firstly, to solve the rheological properties of the non-Newtonian Bingham model by ISPH method, the multi-viscodensity approach has been introduced. Then, this methodology has been used to simulate the annular viscometer and landslide deformation test cases. Through simulating these test cases, the viscoplastic behavior of the non-Newtonian viscosity and propagation of tsunami waves due to underwater landslide movement have been observed. Numerical results were in good agreement with the theoretical and experimental studies and showed that this methodology can be used to investigate precisely the generation and propagation of tsunami surface waves.     

拉萨地块和羌塘地块多个二叠纪基性岩及一个古特提斯海山的初步古地磁结果:对模拟东冈瓦纳晚古生代裂解的暗示

Detachment of the sliver-like Cimmerian terrane from eastern Gondwana in the Early Permian triggered mafic volcanism in many parts of the rift zone. To understand this tectonic episode we have carried out paleomagnetic investigations on mafic volcanic for-mations that were erupted on key terranes that now form part of Tibet. Specifically, we will present data from sections near Lhasa City (central Lhasa block) and Tuotuohe (central Qiangtang Block) as well as near Gyanyima (Paleotethyan sea-mount) that was emplaced onto the floor of Palaeotethys during the Late Permian. Paleomagnetic plots from each location will be used for tectonic calculations. Our new data will be used to evaluate regional scale models con-cerned with how the Cimmerian terranes in southern and SE Asia (from Iran-Tibet-SW China-Myanmar- Thailand-Sumatra) formerly abutted eastern Gond-wana.     

An ANN‐based model for spatiotemporal groundwater level forecasting

This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimal water and waste load allocation in reservoir–river systems: a case study

In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.