Modeling on adsorption–desorption of trace metals to suspended particle matter in the Changjiang Estuary

The uptake and release of trace metals (Cu, Ni, Zn, Cd, and Co) in estuaries are studied using river and sea end-member waters and suspended particulate matter (SPM) collected from the Changjiang Estuary, China. The kinetics of adsorption and desorption were studied in terms of environmental factors (pH, SPM loading, and salinity) and metal concentrations. The uptake of the metals studied onto SPM occurred mostly within 10 h and reached an asymptotic value within 40 h in the Changjiang Estuary. As low pH river water flows into the high pH seawater and the water become more alkaline as it approaches to the seaside of estuary, metals adsorb more on SPM in higher pH water, thus, particulate phase transport of metal become increasingly important in the seaward side of the estuary. The percentage of adsorption recovery and the distribution coefficients for trace metals remained to be relatively invariable and a significant reduction only occurred in very high concentrations of metals (>0.1 mg L⁻¹). The general effect of salinity on metal behavior was to decrease the degree of adsorption of Cu, Zn, Cd, Co, and Ni onto SPM but to increase their adsorption equilibrium pH. The adsorption–desorption kinetics of trace metals were further investigated using Kurbatov adsorption model. The model appears to be most useful for the metals showing the conservative behavior during mixing of river and seawater in the estuary. Our work demonstrates that dissolved concentration of trace metals in estuary can be modeled based on the metal concentration in SPM, pH and salinity using a Kurbatov adsorption model assuming the natural SPM as a simple surfaced molecule.     

Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain)

In the surroundings of Zaragoza, karstification processes are especially intense in covered karst areas where fluvial terraces lie directly on Tertiary evaporites. Since the beginning of Quaternary, these processes have lead to the development of collapse and subsidence dolines with a wide range of sizes, which have significant economic impacts. To reduce economic impact and increase safety, a regional analysis of this phenomenon is needed for spatial management. Therefore, a probability map of dolines was developed using logistic regression and geographic information system (GIS) techniques. This paper covers the selection of input data, manipulation of data using the GIS technology, and the use of logistic regression to generate a doline probability map. The primary variable in the doline development in this area is geomorphology, represented by the location of endorheic areas and different terrace levels. Secondary variables are the presence of irrigation and the water table gradient.     

Rain-induced debris and mudflow triggering factors assessment in the Santiago cordilleran foothills,Central Chile

Debris and mudflows are some of the main geological hazards in the mountain foothills of the Chilean capital city of Santiago. There, the risk of flows triggered in the basins of ravines that drain the range into the city increases with time due to the city growth. A multivariate statistical study based on the logistic regression method is presented. The model provides equations that allow the computation of combined meteorological triggering factors associated with a probability of rain-induced flow occurrence. Daily rainfall, accumulated rainfall and the snowfall level, traditionally considered as the relevant factors, are analysed for a 25-year period. The results show a strong relevance of the rainfall on the day of the flow event over the other factors. However, the relatively low probabilities returned for some real flow events suggest that the model does not capture all the significant variables and the problem is more complex than as it has been traditionally assumed, and further investigations are needed to develop predictive models of flow triggering.     

基于群桩基础沉降估算确定压缩层厚度的探讨

在现行规范中,由于桩基沉降估算模式的不同,压缩层厚度的确定方法也不完全相同。对这些方法进行归纳、总结,并通过具体的工程实例,对由不同估算模式确定的压缩层厚度及沉降估算结果进行探讨与分析,认为对于工程场地地质资料掌握较为翔实的情况下,压缩层厚度可采用变形比法确定,否则,宜根据应力比法确定。     

钱二块铀矿床采铀注液结垢趋势的理论分析与预测

用离子系数矩阵法对钱二块铀矿床地浸采铀试验注液中存在的独立离子反应进行了确定,由溶度积规则及反应平衡原理,借助Matlab编程,估算出了钱二块铀矿床地浸采铀试验注液可能会生成的沉淀物种类及数量,为防治结垢提供了一定的理论依据.     

一种计算台风风场的方法

揭示Ｒａｎｋｉｎｅ涡风场模式和Ｊｅｌｅｓｎｉａｎｓｋｉ风场模式之间的联系，并设计了一种台风风场分布模式，它的风速分布曲线落在Ｊｅｌｅｓｎｉａｎｓｋｉ和Ｒａｎｋｉｎｅ涡两个风场模式的风速分布曲线之间，具有一个既优于Ｊｅｌｅｓｎｉａｎｓｋｉ又优于Ｒａｎｋｉｎｅ涡的风速衰减速率，因此它同时克服了Ｒａｎｋｉｎｅ涡模式计算风速偏小和Ｊｅｌｅｓｎｉａｎｓｋｉ模式计算风速偏大的缺点，以一种比较合理的变化趋势向远方衰减，成为一个比较切合实际的台风风场分布模式。同时，文中提出的移行台风风场计算方法对宫崎正卫、上野武夫和Ｊｅｌｅｓｎｉａｎｓｋｉ模式都有一定的改进。     

Prediction of local scour around circular piles under waves using a novel artificial intelligence approach

Abstract

The scour phenomena around vertical piles in oceans and under waves may influence the structure stability. Therefore, accurately predicting the scour depth is an important task in the design of piles. Empirical approaches often do not provide the required accuracy compared with data mining methods for modeling such complex processes. The main objective of this study is to develop three data-driven methods, locally weighted linear regression (LWLR), support vector machine (SVR), and multivariate linear regression (MLR) to predict the scour depth around vertical piles due to waves in a sand bed. It is the first effort to develop the LWLR to predict scour depth around vertical piles. The models simulate the scour depth mainly based on Shields parameter, pile Reynolds number, grain Reynolds number, Keulegan–Carpenter number, and sediment number. 111 laboratory datasets, derived from several experimental studies, were used for the modeling. The results indicated that the LWLR provided highly accurate predictions of the scour depths around piles (R?=?0.939 and RMSE = 0.075). Overall, this study demonstrated that the LWLR can be used as a valuable tool to predict the wave-induced scour around piles.     

Imaging of Buried Archaeological Materials: The Reflection Properties of Archaeological Wood

Effective marine archaeological site management demands detailed information on not only the spatial distribution of artefacts but also the degradation state of the materials present. Although sonar methods have frequently been used in an attempt to detect buried wooden shipwrecks they are currently unable to indicate their degradation state. To assess the sensitivity of acoustic measurements to changes in the degradation state of such material, and hence the potential for sonars to quantify degradation, laboratory measurements of compressional wave velocity, as well as bulk density for oak and pine samples, in varying states of decay, were undertaken. These data enabled the calculation of theoretical reflection coefficients for such materials buried in various marine sediments. As wood degrades, the reflection coefficients become more negative, resulting in the hypothesis that the more degraded wood becomes, the easier it should be to detect. Typical reflection coefficients of the order of −0.43 and −0.52 for the most degraded oak and pine samples in sand are predicted. Conversely, for wood exposed to seawater the predicted reflection coefficients are large and positive for undegraded material (0.35 for oak, 0.18 for pine) and decrease to zero or slightly below for the most degraded samples. This indicates that exposed timbers, when heavily degraded, can be acoustically transparent and so undetectable by acoustic methods. Corroboration of these experimental results was provided through comparison with high resolution seismic reflection data that has been acquired over two shipwrecks.     

Drag Coefficient,Dynamic Roughness and Reference Wind Speed

Surface waves are the roughness element of the ocean surface. The parameterization of the drag coefficient of the ocean surface is simplified by referencing to wind speed at an elevation proportional to the characteristic wavelength. The dynamic roughness is analytically related to the drag coefficient. Under the assumption of fetch limited wave growth condition, various empirical functions of the dynamic roughness can be converted to equivalent expressions for comparison. For datasets covering a wide range of the dimensionless frequency (inverse wave age), it is important to account for the variable rate of wave development at different wave ages. As a result, the dependence of the Charnock parameter on wave age is nonmonotonic. Finally, the analysis presented here suggests that the significant wave steepness is a sensitive property of the ocean surface and a single variable normalization of the dynamic roughness using a wavelength or wave height parameter actually produces more robust functions than bi-variable normalizations using wave height and wave slope.