Investigating the hydromechanical behaviour of bentonite pellets by swelling pressure tests and discrete element modelling

Acta Geotechnica - Bentonite pellet mixtures are candidate material for the sealing of galleries in radioactive waste disposals. The hydromechanical behaviour of assemblies of bentonite pellets is...     

敦煌造山带南部红柳峡混杂带基质的沉积学、地球化学和年代学特征及其大地构造意义

敦煌造山带南部红柳峡混杂带基质的研究,为认识敦煌造山带的形成和演化提供了新的依据。本文从沉积学、地球化学和年代学等方面系统讨论了该混杂带基质的特征和形成环境。结果显示,基质的岩石类型主要包括变泥质岩(云母石英片岩)和变质砂岩,普遍发生强烈变形。局部弱变形变质的基质仍保留有原生沉积构造(如T_(ab)、T_(de)、T_(bde)组合的鲍马序列),反映原岩是一套浊积岩复理石。显微岩相学特征显示,基质碎屑组分以长石、石英和岩屑为主,长石和岩屑含量较高,分别为47%和27%,反映大量火成岩物质的加入,且碎屑颗粒的分选性和磨圆度较差,说明搬运距离较近。地球化学方面,低的化学蚀变指数(CIA=49~67),反映复理石基质物源区母岩经历的风化程度较低。高的成分变化指数(ICV0.8)以及Zr/Sc-Th/Sc投图结果显示,沉积物再循环程度低,为近物源区的初次沉积。基质Sc、Cr、Co、Ni含量低,Eu/Eu*、La/Sc、Th/Sc、La/Co、Th/Co和Cr/Th等元素比值类似于来自长英质源区的沉积物,暗示其物源区母岩以中-酸性岩石为主。La/Sc-Ti/Zr和Th-ScZr/10投图结果显示,复理石基质形成于陆缘弧或活动大陆边缘构造背景。弱变形浅变质砂岩的碎屑模式表明,基质的物源来自"切割型弧-过渡型弧"源区。综上,红柳峡混杂带基质在碎屑组成方面,以再循环程度低、近物源堆积的"切割型弧-过渡型弧"源区长英质碎屑组分为主,在沉积构造方面,发育鲍马序列和深水块体搬运沉积(MTD)构造,表明基质形成于陆缘弧或活动大陆边缘的俯冲带海沟环境。碎屑锆石年代学显示三组年龄:2300Ma、1850Ma和423Ma,结合区域地质背景分析,初步认为物源碎屑可能来自混杂带北侧的三危山弧和东巴兔-蘑菇台弧的古生代花岗岩类以及俯冲折返的变质基性岩岩块。复理石基质的变质砂岩中获得的最年轻的岩浆碎屑锆石年龄为389Ma,说明该砂岩形成于中泥盆世之后,暗示敦煌造山带南部红柳峡地区洋盆尚未俯冲完毕,碰撞作用尚未开始。     

Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively.     

Long-term riverbed response of lower Tedori River,Japan, to sediment extraction and dam construction

Effects of sediment extraction and dam construction on changes of riverbed characteristics over yearly to decadal scales in the lower Tedori River of Japan are clarified. Over 1950–1991, the riverbed degraded in excess of 0.5–3.5 m. Concurrently, riverbed sediment volume of the 0–16 km reach decreased by 12.7 × 10⁶ m³. Intensive sediment extraction was the dominant cause of riverbed degradation during the period. During 1991–2007, an increase in riverbed sediment volume of 0.6 × 10⁶ m³ resulted in accretion of the riverbed by average depth 0.04 m. The cessation of sand and gravel mining (SGM), coupled with Tedorigawa Dam operation since 1980, was responsible for that accretion. Temporal change in riverbed elevation during 1950–2007 indicates that there were five phases of vertical adjustment. Combination of nonlinear regression models described four of these phases well. During 1950–1979, the first four modes of empirical orthogonal function analysis successfully captured temporal and spatial responses of the riverbed to natural and anthropogenic impacts. That is, the first mode explained the mean riverbed profile and temporal variation in riverbed sediment volume. The second through fourth spatial eigenfunctions reflected spatial variation in vertical adjustment rate for phases II, III and I, respectively. The corresponding temporal eigenfunctions explained the respective effects on the riverbed of SGM, of imbalance between sediment transport capacity and sediment supply, and of dredging activity.     

Learning and climate change

Abstract

Learning—i.e. the acquisition of new information that leads to changes in our assessment of uncertainty—plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.     

Terrestrial water dynamics in the lower Ganges—estimates from ENVISAT and GRACE

In this study, the hydrodynamics of lower Ganges basin in India has been monitored using radar altimetry data from environmental satellite (ENVISAT) mission and microgravity data from the Gravity Recovery and Climate Experiment (GRACE) mission. River stage time series have been constructed for different virtual stations on the lower Ganges. Time series for the integrated water volume changes from microgravity measurements have also been constructed to characterize the seasonal and interannual fluctuation patterns in water storage and flux. The ENVISAT dataset indicates an average seasonal river stage fluctuation of 8 m in the lower Ganges River. The GRACE dataset reveals a seasonal fluctuation ranging from 0.18 to 0.40 m in the vertically integrated total water storage in the lower Ganges basin. The two independent datasets show broad similarity in the lower Ganges basin and outline the importance of space-based techniques for monitoring continental water resources.     

Recent contamination by persistent organochlorines in Baikal seal (Phoca sibirica) from Lake Baikal, Russia

Blubber samples of Baikal seals pups (Phoca sibirica) collected in 2001 and 2002 were analyzed for persistent organochlorines (OCs) such as PCBs (polychlorinated biphenyls), DDTs (dichlorodiphenyltrichloroethane and its metabolites), HCHs (hexachlorocyclohexane isomers), CHLs (chlordane related compounds), TCPMe [tris(4-chlorophenyl)methane] and TCPMOH [tris(4-chlorophenyl)methanol]. The distribution pattern of OCs was in the following order: DDTs>PCBs>CHLs>HCHs>TCPMOH>TCPMe. Organochlorine concentrations and composition patterns in eight pup blubber samples (2002) analyzed in the present study were compared with the corresponding levels in juvenile Baikal seals from 1992 survey, and it was found that residue levels of OC compounds have declined during the 10 year period. In particular, concentrations of PCBs, HCHs and CHLs in seals collected in 2002 were approximately two times lower, while DDTs levels were three times lower than the corresponding levels in 1992. Furthermore, higher ratios of beta-HCH/HCHs and p,p(')-DDE/DDTs were found in 2002 samples. While comparing the OC levels with those in other seal pups it appeared that all the OC concentrations were lower than the levels in seals from coastal California known as one of the most polluted areas. PCB and DDT concentrations in the present specimens were higher than that in Arctic seal pups. The proportions of tri-, tetra-, penta- and hexachlorobiphenyls in pups captured in 2002 were higher than those in 1992. TEQ concentrations of non- and mono-ortho coplanar PCBs in Baikal seals collected in 2002 were higher than those in seals collected from some locations in the world, where human and industrial activities are extensive, suggesting potential for toxic implications of coplanar PCBs in Baikal seals.     

Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers

Abstract

This study examines the potentials of remotely sensed data, GIS and some machine learning classifiers and ensemble techniques in the investigation of the non-linear relationship between malaria occurrences and socio-physical conditions in the Dak Nong province of Viet Nam. Accuracy assessment was determined with Receiver Operating Characteristic (ROC) curve and pair t-test. The results showed that the area under ROC of Random Subspace ensemble model performed better than the other models based on statistical indicators. Comparing pair t-test with Area Under Curve values showed a slight difference of about 1%. Therefore ensemble techniques had significantly improved the performance of the base classifier. However, the performances might vary according to geographic locations. It is concluded that the machine learning classifiers combined with remotely sensed data and GIS is promising for malaria vulnerability mapping, and the derived maps can be used as a fundamental basis for programmes on spatial disease control.     

Shrinkage Properties of Soft Clay Treated with Cement and Geofibers

In this study, effects of two types of geofibers, namely polypropylene and recycled carpet, on three dimensional shrinkage properties of cement treated kaolinite and bentonite clays are investigated. Cement treated clay specimens were prepared with cement contents of 5, 10, and 15 % by weight of dry soil for kaolinite samples, and 30, 40 and 50 % for bentonite samples. To investigate and understand the influence of different fiber types and contents, three different percentages of fiber content (i.e. 0.1, 0.2 and 0.5 % polypropylene fibers; and 0.5, 0.75 and 1 % carpet fibers) were adopted. The results of shrinkage tests on 126 cylindrical samples of cement treated clay with various cement and fiber contents were analysed to understand the relationships between these parameters and the shrinkage percentage of treated soil. Results of this study indicate that combination of cement and fiber is effective in reducing the volume change of clayey soils undergoing drying process. In the applied ranges of cement and fiber contents, the influence of cement addition on the shrinkage reduction is more significant than the addition of fibers for the treated kaolinite. However, addition of fibers in curtailing the shrinkage of bentonite clay is more significant than the cement addition.     

An Application of the Multi-Physics Ensemble Kalman Filter to Typhoon Forecast

This study examines the roles of the multi-physics approach in accounting for model errors for typhoon forecasts with the local ensemble transform Kalman filter (LETKF). Experiments with forecasts of Typhoon Conson (2010) using the weather research and forecasting (WRF) model show that use of the WRF’s multiple physical parameterization schemes to represent the model uncertainties can help the LETKF provide better forecasts of Typhoon Conson in terms of the forecast errors, the ensemble spread, the root mean square errors, the cross-correlation between mass and wind field as well as the coherent structure of the ensemble spread along the storm center. Sensitivity experiments with the WRF model show that the optimum number of the multi-physics ensemble is roughly equal to the number of combinations of different physics schemes assigned in the multi-physics ensemble. Additional idealized experiments with the Lorenz 40-variable model to isolate the dual roles of the multi-physics ensemble in correcting model errors and expanding the local ensemble space show that the multi-physics approach appears to be more essential in augmenting the local rank representation of the LETKF algorithm rather than directly accounting for model errors during the early cycles. The results in this study suggest that the multi-physics approach is a good option for short-range forecast applications with full physics models in which the spinup of the ensemble Kalman filter may take too long for the ensemble spread to capture efficiently model errors and cross-correlations among model variables.