Marine Sr (Ca) input in Quaternary volcanic rock weathering profiles from the Mediterranean coast of Morocco: Sr isotopic approach

Carbonate commonly accumulates in weathering profiles developed on noncalcareous parent material under arid and semiarid climatic conditions. Such weathering profile types are widespread in Morocco, making it critical to know the sources of the calcium in order to understand the genesis of carbonate-rich soils. In this study, we use ⁸⁷Sr/⁸⁶Sr as a geochemical tracer of the Ca sources in weathering profiles developed on volcanic rocks (basalt, tuff, andesite and latite) in the Mediterranean coast of Morocco. The results show that the most of the Sr, and hence Ca, is of an external origin, derived from (i) sea-water for the profiles located near to the coast or (ii) sea-water plus Liassic carbonate outcropping in the catchment for the profiles more distant from the coastline. The contribution of the host rock is small, being less than 25%. The ratio of the Sr (Ca) from the parent material to that from the external sources is controlled by the degree of porosity developed in the profile.     

Convective support of long-wavelength topography in the Apennines (Italy)

Bio-alteration of basaltic glass in the oceanic crust has lately attained much attention. One of the many questions related to this topic is the depth at which bio-alteration presently takes place in the oceanic crust. For this purpose we have investigated samples from the deepest drill hole, i.e. Hole 540B at the Costa Rica Rift in the eastern equatorial Pacific. The glassy rim of pillow lava samples show alteration textures and δ¹³C values compatible with microbial activity throughout the upper 500 m part of the volcanic succession. The concentration and distribution patterns of carbon and potassium within the microbially altered parts, however, indicate that microbes presently interact with the fresh glass to depths of about 380 m into the volcanic basement, at temperatures up to ≈100 °C.     

Heat transport in the Red Lake Bog,Glacial Lake Agassiz Peatlands

We report the results of an investigation on the processes controlling heat transport in peat under a large bog in the Glacial Lake Agassiz Peatlands. For 2 years, starting in July 1998, we recorded temperature at 12 depth intervals from 0 to 400 cm within a vertical peat profile at the crest of the bog at sub‐daily intervals. We also recorded air temperature 1 m above the peat surface. We calculate a peat thermal conductivity of 0·5 W m^?1 °C^?1 and model vertical heat transport through the peat using the SUTRA model. The model was calibrated to the first year of data, and then evaluated against the second year of collected heat data. The model results suggest that advective pore‐water flow is not necessary to transport heat within the peat profile and most of the heat is transferred by thermal conduction alone in these waterlogged soils. In the spring season, a zero‐curtain effect controls the transport of heat through shallow depths of the peat. Changes in local climate and the resulting changes in thermal transport still may cause non‐linear feedbacks in methane emissions related to the generation of methane deeper within the peat profile as regional temperatures increase. Copyright © 2006 John Wiley & Sons, Ltd.     

Crowdsensing smart ambient environments and services

Whether it be Smart Cities, Ambient Intelligence, or the Internet of Things, current visions for future urban spaces share a common core, namely the increasing role of distributed sensor networks and the on‐demand integration of their data to power real‐time services and analytics. Some of the greatest hurdles to implementing these visions include security risks, user privacy, scalability, the integration of heterogeneous data, and financial cost. In this work, we propose a crowdsensing mobile‐device platform that empowers citizens to collect and share information about their surrounding environment via embedded sensor technologies. This approach allows a variety of urban areas (e.g., university campuses, shopping malls, city centers, suburbs) to become equipped with a free ad‐hoc sensor network without depending on proprietary instrumentation. We present a framework, namely the GeoTracer application, as a proof‐of‐concept to conduct multiple experiments simulating use‐case scenarios on a university campus. First, we demonstrate that ambient sensors (e.g. temperature, pressure, humidity, magnetism, illuminance, and audio) can help determine a change in environment (e.g. moving from indoors to outdoors, or floor changes inside buildings) more accurately than typical positioning technologies (e.g. global navigation satellite system, Wi‐Fi, etc.). Furthermore, each of these sensors contributes a different amount of data to detecting events. for example, illuminance has the highest information gain when trying to detect changes between indoors and outdoors. Second, we show that through this platform it is possible to detect and differentiate place types on a university campus based on inferences made through ambient sensors. Lastly, we train classifiers to determine the activities that a place can afford at different times (e.g. good for studying or not, basketball courts in use or empty) based on sensor‐driven semantic signatures.     

Assessing the effectiveness of different visualizations for judgments of positional uncertainty

Many techniques have been proposed for visualizing uncertainty in geospatial data. Previous empirical research on the effectiveness of visualizations of geospatial uncertainty has focused primarily on user intuitions rather than objective measures of performance when reasoning under uncertainty. Framed in the context of Google’s blue dot, we examined the effectiveness of four alternative visualizations for representing positional uncertainty when reasoning about self-location data. Our task presents a mobile mapping scenario in which GPS satellite location readings produce location estimates with varying levels of uncertainty. Given a known location and two smartphone estimates of that known location, participants were asked to judge which smartphone produces the better location reading, taking uncertainty into account. We produced visualizations that vary by glyph type (uniform blue circle with border vs. Gaussian fade) and visibility of a centroid dot (visible vs. not visible) to produce the four visualization formats. Participants viewing the uniform blue circle are most likely to respond in accordance with the actual probability density of points sampled from bivariate normal distributions and additionally respond most rapidly. Participants reported a number of simple heuristics on which they based their judgments, and consistency with these heuristics was highly predictive of their judgments.     

Response of Growing Season Gross Primary Production to El Ni?o in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

Gross primary production (GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems. A set of validated monthly GPP data from 1957 to 2010 in 0.5° × 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging (BMA). The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Ni?o years indicated that GPP response to El Ni?o varies with Pacific Decadal Oscillation (PDO) phases: when the PDO was in the cool phase, it was likely that GPP was greater in northern China (32°–38°N, 111°–122°E) and less in the Yangtze River valley (28°–32°N, 111°–122°E); in contrast, when PDO was in the warm phase, the GPP anomalies were usually reversed in these two regions. The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon. The previously published findings on how El Ni?o during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Ni?o in this study theoretically credible. This paper not only introduces an effective way to use BMA in grids that have mixed plant function types, but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Ni?o and PDO.     

喜马拉雅山南地区地热水和钙华地球化学特征与成因机制

喜马拉雅山南地区拥有丰富的地热资源。开展地热水和钙华成因机制的研究,有助于了解地热资源特征和古气候变迁信息,对丰富山南地热资源的系统性研究和青藏高原气候环境变化研究均具有重要理论意义。本文以喜马拉雅东段山南地区的邛多江、古堆和曲卓木的三个温泉为研究对象,通过采集温泉地热水和钙华数据,综合分析了地热水水-岩作用特征、热储温度估算、补给来源追溯和温泉钙华的成因类型、形成年代、古气候意义等。结果表明：邛多江温泉的水化学类型为HCO₃·Cl-Na·Ca型;古堆日若沸泉的水化学类型为HCO₃·SO₄-Ca·Na型;古堆茶卡沸泉的水化学类型为Cl-Na型;曲卓木热泉的水化学类型为Cl·SO₄-Na·Ca型。温泉地热水中的阴阳离子来源主要是硅酸盐岩的溶解以及部分碳酸盐岩和盐岩的溶解。由于温泉地热水均未达到水-岩平衡状态,利用石英地热温标得出浅部热储温度为129～148℃,利用硅-焓图解得出深部热储温度和冷水混合比例为181～221℃和58%～65%;氢氧同位素显示地热水补给高程为4467～5303 m。在山南地区,地热水受到高海拔大气降水和冰雪融水的补给,通过主要断裂构造运移到深部加热并在高温高压下沿着裂隙、节理上升,然后与浅层冷水混合,最后沿浅部地表松散破碎带出露形成温泉。温泉钙华CaO占比43.43%～56.66%,且显示出轻稀土元素富集的特点;δ¹³C指示温泉钙华为热成因,钙华中的碳主要来自于深部碳酸盐岩的变质成因,仅古堆日若沸泉有部分地幔碳;¹⁴C测年显示钙华的年龄为21280±70～43500年,Mg/Ca、Mg/Sr比值指示降雨量在43.50 ka年由峰值迅速减弱,并在42 ka～21.28 ka年期间降雨量又逐渐增强。     

支持向量机组合方法在砂泥岩储层岩性识别中的应用

岩性是表征储油物性、建立各类地质模型的重要参数,其与测井参数的函数关系很复杂。基于支持向量机一类对一类法对岩性识别分类精度不高的现状,结合二叉树法初始分类精度较高、分类速度快等优点,提出了一种新的组合方法——一类对一类法与二叉树法的结合应用。该方法对样本数据较少的类别设置权重系数,减少样本不平衡的影响,并利用几何平均准确率作为评价岩心识别效果的指标,其对岩性的分类效果远优于单一方法。具体步骤为:首先对不均衡的样本设置相应的权重系数,然后利用二叉树法将易于与砂泥岩区分的灰岩区分开来,再利用一对一分类法将剩下的砂泥岩样本进行分类。运用此方法对某油田测井数据进行岩性分类,分类的整体准确率以及几何平均准确率均有很大的提高。     

碳排放和减碳的社会经济代价研究进展与方法探究

碳排放和减碳的社会经济影响与代价评估日益受到学术界和决策者的关注,本文综合分析了全球范围内碳排放与减碳社会经济影响的重要科学问题和国内外研究现状,基于卫星最新观测到的全球CO₂非均匀分布的事实,针对温控1.5 ℃和2 ℃阈值情景,探讨了全球二氧化碳非均匀动态分布与地表温度时空关系,全球二氧化碳非均匀动态分布状况下主要国家碳排放空间评价以及温控1.5 ℃和2 ℃阈值情景下中国碳排放和减碳社会经济代价评估等问题可采取的技术模型方案,并提出了构建新气候变化经济学的理论方法与技术体系。本文构建的技术路线和研究方法拟为国家制订各项应对碳排放和减碳战略与对策、减缓气候变化并实现可持续转型、提升中国在应对气候变化领域的国际话语权等方面提供决策支持。