生物基可降解聚乳酸(PLA)沙障对沙丘表层沉积物粒度特征的影响

以吉兰泰盐湖北部流沙区铺设8 a的不同规格聚乳酸（PLA）沙障内沉积物为研究对象,利用激光衍射技术分析了沙丘表层0~3 cm沉积物粒度组成,计算并分析平均粒径、标准偏差等粒度参数,探讨了影响障内沉积物相对粗细的关键组分,分析PLA沙障的防沙效益。结果表明：PLA沙障内沉积物以细沙、中沙、极细沙为主。中沙、极细沙、细沙是障内沉积物的关键组分。PLA沙障的阻滞作用使得中沙、粗沙含量百分比升高,细颗粒含量百分比降低,障内沙粒分选性逐渐变差,峰态偏离正态分布,向负偏方向发展,频率分布曲线分布范围变宽,累积频率曲线变缓,表层颗粒组成趋于粗化。1 m×1 m规格PLA沙障内均以积沙为主;沙丘迎风坡和背风坡的2 m×2 m、3 m×3 m沙障内均为积沙状态,丘顶则为风蚀状态。综合考虑沙障铺设成本和防护效果,得出研究区域内2 m×2 m规格PLA沙障整体防沙效益最佳。     

Changes in heat wave characteristics over Extremadura (SW Spain)

Theoretical and Applied Climatology - Heat wave (HW) events are becoming more frequent, and they have important consequences because of the negative effects they can have not only on the human...     

夯扩挤密水泥土桩复合地基的应用研究

根据水泥土和夯扩挤密施工法的特点,结合工程实例,对夯扩挤密水泥土桩在地基处理设计与施工进行了一些探讨和研究,阐明了利用夯扩挤密水泥土桩进行地基处理是安全可行、经济合理的一种型式,以及设计与施工中应注意的几个问题,并在高层住宅中进行了尝试,取得了较好的效果。     

Carbon pools and fluxes in the China Seas and adjacent oceans

The China Seas include the South China Sea, East China Sea, Yellow Sea, and Bohai Sea. Located off the Northwestern Pacific margin, covering 4700000 km~2 from tropical to northern temperate zones, and including a variety of continental margins/basins and depths, the China Seas provide typical cases for carbon budget studies. The South China Sea being a deep basin and part of the Western Pacific Warm Pool is characterized by oceanic features; the East China Sea with a wide continental shelf, enormous terrestrial discharges and open margins to the West Pacific, is featured by strong cross-shelf materials transport; the Yellow Sea is featured by the confluence of cold and warm waters; and the Bohai Sea is a shallow semiclosed gulf with strong impacts of human activities. Three large rivers, the Yangtze River, Yellow River, and Pearl River, flow into the East China Sea, the Bohai Sea, and the South China Sea, respectively. The Kuroshio Current at the outer margin of the Chinese continental shelf is one of the two major western boundary currents of the world oceans and its strength and position directly affect the regional climate of China. These characteristics make the China Seas a typical case of marginal seas to study carbon storage and fluxes. This paper systematically analyzes the literature data on the carbon pools and fluxes of the Bohai Sea,Yellow Sea, East China Sea, and South China Sea, including different interfaces(land-sea, sea-air, sediment-water, and marginal sea-open ocean) and different ecosystems(mangroves, wetland, seagrass beds, macroalgae mariculture, coral reefs, euphotic zones, and water column). Among the four seas, the Bohai Sea and South China Sea are acting as CO_2 sources, releasing about0.22 and 13.86–33.60 Tg C yr~(-1) into the atmosphere, respectively, whereas the Yellow Sea and East China Sea are acting as carbon sinks, absorbing about 1.15 and 6.92–23.30 Tg C yr~(-1) of atmospheric CO_2, respectively. Overall, if only the CO_2 exchange at the sea-air interface is considered, the Chinese marginal seas appear to be a source of atmospheric CO_2, with a net release of 6.01–9.33 Tg C yr~(-1), mainly from the inputs of rivers and adjacent oceans. The riverine dissolved inorganic carbon (DIC) input into the Bohai Sea and Yellow Sea, East China Sea, and South China Sea are 5.04, 14.60, and 40.14 Tg C yr~(-1),respectively. The DIC input from adjacent oceans is as high as 144.81 Tg C yr~(-1), significantly exceeding the carbon released from the seas to the atmosphere. In terms of output, the depositional fluxes of organic carbon in the Bohai Sea, Yellow Sea, East China Sea, and South China Sea are 2.00, 3.60, 7.40, and 5.92 Tg C yr~(-1), respectively. The fluxes of organic carbon from the East China Sea and South China Sea to the adjacent oceans are 15.25–36.70 and 43.93 Tg C yr~(-1), respectively. The annual carbon storage of mangroves, wetlands, and seagrass in Chinese coastal waters is 0.36–1.75 Tg C yr~(-1), with a dissolved organic carbon(DOC) output from seagrass beds of up to 0.59 Tg C yr~(-1). Removable organic carbon flux by Chinese macroalgae mariculture account for 0.68 Tg C yr~(-1) and the associated POC depositional and DOC releasing fluxes are 0.14 and 0.82 Tg C yr~(-1), respectively. Thus, in total, the annual output of organic carbon, which is mainly DOC, in the China Seas is 81.72–104.56 Tg C yr~(-1). The DOC efflux from the East China Sea to the adjacent oceans is 15.00–35.00 Tg C yr~(-1). The DOC efflux from the South China Sea is 31.39 Tg C yr~(-1). Although the marginal China Seas seem to be a source of atmospheric CO_2 based on the CO_2 flux at the sea-air interface, the combined effects of the riverine input in the area, oceanic input, depositional export,and microbial carbon pump(DOC conversion and output) indicate that the China Seas represent an important carbon storage area.     

西秦岭甘南地区上二叠统毛毛隆组古环境演变:基于遗迹网络分析

西秦岭地区是二叠系—三叠系之交深水相保存较为连续的地区,西秦岭甘南地区上二叠统毛毛隆组发育丰富的遗迹化石(12属17种)。遗迹网络分析作为新兴的研究方法,侧重于用相互连接的遗迹分类群来重建遗迹群落系统,这为研究遗迹群落的行为学和生态学提供了丰富的手段。笔者以西秦岭甘南地区上二叠统毛毛隆组发现的遗迹化石为数据源,运用遗迹网络分析方法,揭示出毛毛隆组遗迹群落具结构化特征,遗迹群落体现出环境驱动的遗迹组合面貌特征: Palaeophycus-Lockeia-Arenicolites-Dictyodora-Planolites-Protovirgularia-Chondrites组合(C1,近源浊流区),Palaeophycus-Lockeia-Arenicolites-Dictyodora-Protovirgularia-Phycodes-Chondrites-Helminthopsis组合(C2,浊流过渡区—浊流远源区),和Palaeophycus-Nereites-Scolicia-Protovirgularia-Helminthopsis组合(C3,远源浊流区)。该遗迹群落由C1演变至C3的过程,揭示了古环境从近源浊流区至远源浊流区的演变历程。     

富营养化湖泊蓝藻-芦苇碎屑堆积分解过程及其潜在的共代谢效应

为探究富营养化浅水湖泊所富集的有机物对湖泊碳循环和水质的影响,本研究构建微宇宙系统,模拟蓝藻和芦苇碎屑单独分解及混合分解过程.通过测定各组上覆水营养盐浓度、有机质含量及结构的变化,揭示富营养化湖泊藻草残体混合分解过程中养分和有机碳的释放特征.结果表明,在实验0～88 h内,在添加相同的碳源条件下,蓝藻和芦苇混合处理组总碳(TC)释放量显著高于理论值,表明藻草碎屑混合分解存在共代谢效应.在培养初期,沉积物通过共代谢效应对水质产生了较大的影响,加速向水体中释放氮、磷物质.相较单独的植物分解,混合处理组中总氮(TN)、总磷(TP)的最大释放量分别提高了13.49%和26.84%;通过三维荧光光谱表征的类富里酸荧光强度变化也表明:较芦苇处理组,混合处理组中芦苇的分解速率更快.在培养开始后,各处理组均快速转变为厌氧状态,TC、TN、TP浓度随时间变化总体上呈先快速上升再逐渐平缓的趋势,分别在第228、108和324小时达最大值(372.4±2.98)、(138.45±2.97)和(7.95±1.11) mg/L.细菌特异性脂肪酸含量变化表明,将蓝藻碎屑添加到芦苇碎屑中,会增加芦苇碎屑中细菌的丰度,从而提高分解速率,激发共代谢效应.在全球气候变暖的背景下,随着富营养化湖泊藻类暴发频次增加,共代谢效应可能还会进一步加强,对富营养化湖泊水质将会持续产生影响.     

Features in REE and Methane Anomalies Distribution in the East China Sea Water Column: a Comparison with the South China Sea

Water Resources - This paper presents review of dissolved Rare Earth Elements (REE) and methane anomalies distribution in the East China Sea water column. In general, the REE concentrations of the...     

云南测震台网地震监测能力分析

2018年1月云南省测震台网示范工程20个新增测震台建设完成,选取原有48个台以及新增20个台连续48小时数据记录,基于近震震级公式,计算并网前后云南省测震台网的地震监测能力,对比测震台增加前后的地震监测能力,发现:云南省地震监测能力由ML 2.2提升到ML 1.6,局部地区由ML1.4提升到ML0.8。     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.