黄渤海海域贝类麻痹性贝毒的检测与分析

采用小白鼠生物测试法和高效液相色谱法对2002~2005年我国黄渤海海域采集的贝类样品进行了麻痹性贝毒毒性检测,结果显示大连海域的虾夷扇贝含有麻痹性贝毒,有毒样品均出现在5月和6月,部分虾夷扇贝样品的毒素含量已经超过食用安全标准。通过高效液相色谱法分析了有毒虾夷扇贝体内的毒素成分,共检出了6种麻痹性贝毒组分,主要以毒性较低的C1和C2毒素为主,GTX3和GTX2次之,STX和neoSTX含量很低。通过高效液相色谱法分析得到的各毒素组分毒性总和与小白鼠生物测试法毒性测试结果基本相当。     

唐山祥云湾海洋牧场海域网采浮游植物群集特征研究

为研究唐山祥云湾海洋牧场海域网采浮游植物群集特征, 于2020年11月至2021年11月在祥云湾海洋牧场海域进行了浮游植物及环境因子的周年逐月调查。共鉴定浮游植物41属78种, 其中硅藻33属62种, 甲藻7属15种, 硅鞭藻1属1种, 年均丰度为205.58×10⁴ cells/m³, 多样性指数H''为2.88。与近岸非增殖海域不同, 该海域浮游植物的丰度及群落结构指数在春夏季水生生物繁生期达到全年最低。优势类群的季节演替明显, 其中, 3~5月以诺氏海链藻最占优势, 9~10月以角毛藻和中肋骨条藻最占优势, 周年优势类群以圆筛藻和角毛藻最占优势, 其优势度变动在6月前以圆筛藻显著为高, 之后则以角毛藻显著为高; 此外,甲藻的优势度在泛冬季(11~2月)达到最高。鱼礁区与对照区的对比结果显示, 两区域浮游植物的群落变化均可划分为泛冬季低温期(11~2月)、春夏季繁生期(3~6月)、泛秋季降温期(7~11月)三个时期。链状浮游植物的丰度在礁区明显高于对照区, 而非链状浮游植物则相反; 与生物作用关系密切的溶解有机碳、pH值在礁区低于对照区, 而总磷和溶解态硅则相反。Pearson相关及冗余分析(RDA)显示, 两区域浮游植物与环境因子的显著相关关系在不同时期差异明显。春夏季繁生期牡蛎礁上的贝类滤食活跃, 浮游植物与环境因子的关系最为密切, 浮游植物与环境因子的相关关系达到显著水平的数量最多。礁区与对照区浮游植物的群集特征差异可能受到礁体附着生物活动及潮汐往复流混合作用的影响。     

Full probabilistic design of slopes in spatially variable soils using simplified reliability analysis method

ABSTRACT

A simplified reliability analysis method is proposed for efficient full probabilistic design of soil slopes in spatially variable soils. The soil slope is viewed as a series system comprised of numerous potential slip surfaces and the spatial variability of soil properties is modelled by the spatial averaging technique along potential slip surfaces. The proposed approach not only provides sufficiently accurate reliability estimates of slope stability, but also significantly improves the computational efficiency of soil slope design in comparison with simulation-based full probabilistic design. It is found that the spatial variability has considerable effects on the optimal slope design.     

青海日龙沟锡多金属矿床地质特征及矿床成因探讨

日龙沟锡多金属矿床位于鄂拉山多金属成矿带中的塞什塘—铜峪沟—日龙沟多金属成矿亚带西端;矿区内的下二叠统地层为矿床的主要赋矿层位,矿体的产出明显受一定的地层层位控制,虽然不连续,但总体呈层状,页层间破碎带产出;矿化类型总体属蚀变岩型,矿床成因为沉积一变质改造型。     

Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation

Random finite element method (RFEM) provides a rigorous tool to incorporate spatial variability of soil properties into reliability analysis and risk assessment of slope stability. However, it suffers from a common criticism of requiring extensive computational efforts and a lack of efficiency, particularly at small probability levels (e.g., slope failure probability P _f ?<?0.001). To address this problem, this study integrates RFEM with an advanced Monte Carlo Simulation (MCS) method called “Subset Simulation (SS)” to develop an efficient RFEM (i.e., SS-based RFEM) for reliability analysis and risk assessment of soil slopes. The proposed SS-based RFEM expresses the overall risk of slope failure as a weighed aggregation of slope failure risk at different probability levels and quantifies the relative contributions of slope failure risk at different probability levels to the overall risk of slope failure. Equations are derived for integrating SS with RFEM to evaluate the probability (P _f ) and risk (R) of slope failure. These equations are illustrated using a soil slope example. It is shown that the P _f and R are evaluated properly using the proposed approach. Compared with the original RFEM with direct MCS, the SS-based RFEM improves, significantly, the computational efficiency of evaluating P _f and R. This enhances the applications of RFEM in the reliability analysis and risk assessment of slope stability. With the aid of improved computational efficiency, a sensitivity study is also performed to explore effects of vertical spatial variability of soil properties on R. It is found that the vertical spatial variability affects the slope failure risk significantly.     

Remediation of saturated Shanghai sandy silt contaminated with p-xylene using air sparging

Air sparging is an effective technique for the remediation of soil and groundwater polluted by volatile organic compounds. In this paper, this technique was investigated by conducting air-sparging test in the laboratory on the Shanghai sandy silt that was artificially contaminated with p-xylene. A test tank was designed for this purpose. During the air-sparging process, aqueous p-xylene solutions were extracted from the observation holes, and their concentrations were quantified by the spectrophotometric detection method. The mechanism of mass transfer process of p-xylene in the vicinity of sparging well and the remediation of the contaminated groundwater by air sparging were explored. The results showed that the removal zone of the p-xylene was mainly located within a radius of about 20?cm around the air injection well, with 90?% p-xylene removed after 20-day air sparging. Within the initial 5-day sparging, the concentration of p-xylene decreased rapidly in the mass transfer zone. By contrast, in the area far from the injection well, the p-xylene concentration decreased evenly and slowly. Thus, the remediation of contaminated soil and groundwater by air sparging is space?Ctime dependent. For further analysis, the adsorption of silt was taken into account, and the distribution coefficient, K _d , was introduced to the modified Shackleford??s mass transfer model. The comparison between the simulated and measured results indicates that the modified model can satisfactorily describe the p-xylene mass transfer observed in this study.     

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.     

Efficient and flexible Bayesian updating of embankment settlement on soft soils based on different monitoring datasets

Acta Geotechnica - In situ monitoring provides valuable information to update the predictions of the embankment settlement on soft soils. Observational data obtained at different monitoring moments...     

珠江口及其邻近海域沉积物甲烷-硫酸根界面分布深度及影响因素

利用化学和稳定同位素化学等方法分析研究区沉积物间隙水甲烷和硫酸根、pH和∑CO2以及δ^13C—CH4和δ^13C—ECO2的垂直剖面分布。结果显示,间隙水硫酸根浓度呈线性梯度减小,至沉积物甲烷-硫酸盐界面（sulfate-methane interface,SMI）附近,硫酸盐几乎全部消耗而甲烷浓度急剧增大;与此同时,间隙水pH和∑CO2在该深度位置明显升高。间隙水地球化学特征揭示了沉积物发生了AOM作用。在AOM过程中,由于^12CH4氧化速率较^13CH4快,故引起沉积物间隙水剩余甲烷的碳同位素偏重,而δ^13C—ZCO2值变为极负,珠江口QA11—2、QA12-9、QA12—14和GS-1四个站位SMI对应深度分别为12cm、38cm、50cm和204cm,而南海BD-7站位由间隙水硫酸根剖面变化推算约为600cm。从珠江河口到南海沉积物,由于受陆源输入的减少,表层沉积物有机质含量呈降低趋势。有机质输入量及其活性的高低是制约了沉积物SMI分布深浅的关键因素,这是由于高含量的活性有机质一方面可加速间隙水硫酸根通过有机质再矿化分解作用途径消耗;另一方面可引起向上扩散进入AOM反应带的甲烷通量增大,使得通过AOM作用的硫酸根消耗通量相应增大,其结果造成沉积物SMI的上移。根据沉积物C／N比值以及^13C剖面变化,推断AOM作用的可能发生机制是由于在沉积物表层再矿化作用过程中,因一部分活性有机质被大量消耗,导致进入沉积物硫酸根还原带底部的活性有机质数量相应减少,从而引起部分硫酸根转为与甲烷发生反应,并在微生物的作用下完成AOM过程。