High resolution simulation of recent Arctic and Antarctic stratospheric chemical ozone loss compared to observations

Simulations of polar ozone losses were performed using the three-dimensional high-resolution (1^∘ × 1^∘) chemical transport model MIMOSA-CHIM. Three Arctic winters 1999–2000, 2001–2002, 2002–2003 and three Antarctic winters 2001, 2002, and 2003 were considered for the study. The cumulative ozone loss in the Arctic winter 2002–2003 reached around 35% at 475 K inside the vortex, as compared to more than 60% in 1999–2000. During 1999–2000, denitrification induces a maximum of about 23% extra ozone loss at 475 K as compared to 17% in 2002–2003. Unlike these two colder Arctic winters, the 2001–2002 Arctic was warmer and did not experience much ozone loss. Sensitivity tests showed that the chosen resolution of 1^∘ × 1^∘ provides a better evaluation of ozone loss at the edge of the polar vortex in high solar zenith angle conditions. The simulation results for ozone, ClO, HNO₃, N₂O, and NO _y for winters 1999–2000 and 2002–2003 were compared with measurements on board ER-2 and Geophysica aircraft respectively. Sensitivity tests showed that increasing heating rates calculated by the model by 50% and doubling the PSC (Polar Stratospheric Clouds) particle density (from 5 × 10⁻³ to 10⁻² cm⁻³) refines the agreement with in situ ozone, N₂O and NO _y levels. In this configuration, simulated ClO levels are increased and are in better agreement with observations in January but are overestimated by about 20% in March. The use of the Burkholder et al. (1990) Cl₂O₂ absorption cross-sections slightly increases further ClO levels especially in high solar zenith angle conditions. Comparisons of the modelled ozone values with ozonesonde measurement in the Antarctic winter 2003 and with Polar Ozone and Aerosol Measurement III (POAM III) measurements in the Antarctic winters 2001 and 2002, shows that the simulations underestimate the ozone loss rate at the end of the ozone destruction period. A slightly better agreement is obtained with the use of Burkholder et al. (1990) Cl₂O₂ absorption cross-sections.     

苏州阳西高岭土矿床氢氧稳定同位素特征

苏州阳西南岭土矿床按成因可分为脉岩型高岭土及溶洞充填型高岭土.高岭土的氢氧稳定同位素组成变化大,取决于样品的采集部位.在δD—δ~(18)O相关图上,以δ~(18)O=19‰为界,可分为A、B两区.脉岩型的残斑状高岭土氢氧同位素组成位于A区(δ~(18)O<19‰);而溶洞充填型的致密块状高岭土的氢氧同位素组成位于B区(δ~(18)O>19‰).据此可以认为脉岩型高岭土以热液成矿作用为主,溶洞充填型高岭土以风化淋滤作用为主.并分别在成矿后又受到了后期作用的叠加改造.     

Probabilistic modelling of joint orientation

Observed frequencies of joint orientations are subject to error due to sampling bias. This error should be corrected before statistical inference is made on the distribution of orientation. Corrections (weighting functions) are developed for sampling bias in orientation for finite joints of different sizes and shapes intersecting rectangular exposures. Chi-square goodness-of-fit procedures available for hemispherical normal and bivariate normal distributions are modified to make them applicable for both raw data and data corrected for sampling bias. The aforementioned corrections and procedures were applied to a joint orientation cluster to study the effect of (a) joint orientation, (b) joint size and (c) joint shape, on the statistical distribution of the orientation. The influences of all these aforementioned factors were found to be significant. However, at present, joint sizes and shapes are not measured in field joint surveys. Therefore, it is suggested to make attempts to obtain joint sizes and shapes in field joint mapping surveys. Since the currently available probability distributions are not adequate to represent all joint orientation distributions, it is suggested either to look for new probability distributions or to develop procedures to use empirical distributions in modelling orientation distributions.     

Novel Extreme Learning Machine-Multi-Verse Optimization Model for Predicting Peak Particle Velocity Induced by Mine Blasting

Natural Resources Research - Peak particle velocity (PPV) is an important criterion for assessing the risk level of ground vibration induced by mine blasting. Based on this criterion, many efforts...     

区域铜元素的空间变异和自相关规律分析

研究地球化学元素在地球化学场中的空间最大变异距离、空间聚集以及空间异常分布情况。通过稳健半变差函数确定空间最大变异距离,在最大变异距离内分别对在不同的距离、原始数据和经过Box-Cox变换的数据使用局部空间自相关Moran′s Ii指数研究元素含量的空间聚集和空间异常,与基于C-A多重分形法的异常下限确定方法所圈定的异常范围进行对比。以面积为8 130km2的铜矿区所采集的1∶20万水系沉积物1 341个组合样点的Cu元素含量为例,局部Moran′s Ii能很好地划分空间聚集和空间异常,尤其是针对经过变换的数据能探测出比C-A法范围更大的聚集和异常,符合客观实际,与已知矿床(点)吻合很好。     

A new SPH-based approach to simulation of granular flows using viscous damping and stress regularisation

The smoothed particle hydrodynamics (SPH) method was recently extended to simulate granular materials by the authors and demonstrated to be a powerful continuum numerical method to deal with the post-flow behaviour of granular materials. However, most existing SPH simulations of granular flows suffer from significant stress oscillation during the post-failure process, despite the use of an artificial viscosity to damp out stress fluctuation. In this paper, a new SPH approach combining viscous damping with stress/strain regularisation is proposed for simulations of granular flows. It is shown that the proposed SPH algorithm can improve the overall accuracy of the SPH performance by accurately predicting the smooth stress distribution during the post-failure process. It can also effectively remove the stress oscillation issue in the standard SPH model without having to use the standard SPH artificial viscosity that requires unphysical parameters. The predictions by the proposed SPH approach show very good agreement with experimental and numerical results reported in the literature. This suggests that the proposed method could be considered as a promising continuum alternative for simulations of granular flows.     

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.     

Climate change and groundwater resources in Mekong Delta,Vietnam

Groundwater resources have considerable influences on the human population and socioeconomic development of Vietnam and the Mekong River Delta (MRD). This paper presents an overview of the relationship between climate change and groundwater in the MRD, including the challenges, strategies and technical measures. Our results showed that groundwater levels are related to other climate and hydrological variables (i.e., rainfall, river levels, etc.); therefore, the impacts of climate change on the groundwater resources of the Mekong delta are significant, especially on groundwater recharge. Based on the results of this study, it is recommended that groundwater development in the future should focus on reducing groundwater harvesting, enhancing groundwater quantity by establishing artificial works and exploiting surface water. This study suggests that the Artificial Neural Network (ANN) model is an effective tool for forecasting groundwater levels in periods of 1 month and 3 months for aquifers in the natural and tidal regime areas of the delta.     

A variationally consistent hyperstatic reaction method for tunnel lining design

In this technical note, a consistent finite element formulation of the Hyperstatic Reaction Method (HRM) for tunnel linings design is proposed by introducing a variational consistently linearized formulation. It permits to consider a nonlinear interaction between a lining structure and the surrounding ground. Recent advances of the HRM in regard to the consideration of the nonlinear response of the segmented tunnel lining exposed to design loads use an iterative algorithm for solving the nonlinear system of equations. In the proposed Variationally consistent Hyperstatic Reaction Method (VHRM), a distributed nonlinear spring model representing the interaction between the lining and the ground soils is considered in a variationally consistent format. Computing the tangential spring stiffness via consistent linearization, and using Newton-Raphson iteration, requires significantly smaller number of iterations as compared to the original HRM model based on nodal springs. Furthermore, the method is applicable for simulations using solid finite elements (2D and 3D), as well as beam or finite shell elements, respectively.