Investigating landfill‐impacted groundwater seepage into headwater streams using stable carbon isotopes

The impact of landfill contaminated groundwater along a reach of a small stream adjacent to a municipal landfill was investigated using stable carbon isotopes as a tracer. Groundwater below the stream channel, groundwater seeping into the stream, groundwater from the stream banks and stream water were sampled and analysed for dissolved inorganic carbon (DIC) and the isotope ratio of DIC (δ¹³C_DIC). Representative samples of groundwater seeping into the stream were collected using a device (a ‘seepage well’) specifically designed for collecting samples of groundwater seeping into shallow streams with soft sediments. The DIC and δ¹³C_DIC of water samples ranged from 52 to 205 mg C/L and ?16·9 to +5·7‰ relative to VPDB standard, respectively. Groundwater from the stream bank adjacent to the landfill and some samples of groundwater below the stream channel and seepage into the stream showed evidence of δ¹³C enriched DIC (δ¹³C_DIC = ?2·3 to +5·7‰), which we attribute to landfill impact. Stream water and groundwater from the stream bank opposite the landfill did not show evidence of landfill carbon (δ¹³C_DIC = ?10·0 to ?16·9‰). A simple mixing model using DIC and δ¹³C_DIC showed that groundwater below the stream and groundwater seeping into the stream could be described as a mixture of groundwater with a landfill carbon signature and uncontaminated groundwater. This study suggests that the hyporheic zone at the stream–groundwater interface probably was impacted by landfill contaminated groundwater and may have significant ecological implications for this ecotone. Copyright © 2004 John Wiley & Sons, Ltd.     

沈阳市乡级人工增雨作业指标预报研究及系统的开发与应用

根据人工增雨作业对预报精细化的需求,结合辽宁部分城市天气预报和多普勒雷达回波实时资料,研究确定了沈阳市乡级人工增雨作业指标及其计算方法,并建立了相应的业务运行及发布系统,分别以文本和图形的形式定时或不定时地发布人工增雨作业指标预报信息,可为人工增雨作业提供更准确、更精细的指导预报,提高了人工增雨的作业效率。     

The Non-Gravitational Perturbations impact on the BepiColombo Radio Science Experiment and the key rôle of the ISA accelerometer: direct solar radiation and albedo effects

The paper is focused on the estimate of the impact of the non-gravitational perturbations on the orbit of the Mercury Planetary Orbiter (MPO), one of the two spacecrafts that will be placed in orbit around the innermost planet of the solar system by the BepiColombo space mission. The key rôle of the Italian Spring Accelerometer (ISA), that has been selected by the European Space Agency (ESA) to fly on-board the MPO, is outlined. In the first part of the paper, through a numerical simulation and analysis we have estimated, over a time span of several years, the long-period behaviours of the disturbing accelerations produced by the incoming direct solar radiation pressure, and the indirect effects produced by Mercury’s albedo. The variations in the orbital parameters of the spacecraft, together with their spectral contents, have been estimated over the analysed period. The direct solar radiation pressure represents the strongest non-gravitational perturbation on the MPO in the very complex radiation environment of Mercury. The order-of-magnitude of this acceleration is quite high, about 10^?6 m/s², because of the proximity to the Sun and the large area-to-mass ratio of the spacecraft. In the second part of the paper, we concentrated upon the short-period effects of direct solar radiation pressure and Mercury’s albedo. In particular, the disturbing accelerations have been compared with the ISA measurement error and the advantages of an on-board accelerometer are highlighted with respect to the best modelling of the non-gravitational perturbations in the strong radiation environment of Mercury. The readings from ISA, with an intrinsic noise level of about $10^{-9}\,m/s^{2}/\sqrt{Hz}The paper is focused on the estimate of the impact of the non-gravitational perturbations on the orbit of the Mercury Planetary Orbiter (MPO), one of the two spacecrafts that will be placed in orbit around the innermost planet of the solar system by the BepiColombo space mission. The key r?le of the Italian Spring Accelerometer (ISA), that has been selected by the European Space Agency (ESA) to fly on-board the MPO, is outlined. In the first part of the paper, through a numerical simulation and analysis we have estimated, over a time span of several years, the long-period behaviours of the disturbing accelerations produced by the incoming direct solar radiation pressure, and the indirect effects produced by Mercury’s albedo. The variations in the orbital parameters of the spacecraft, together with their spectral contents, have been estimated over the analysed period. The direct solar radiation pressure represents the strongest non-gravitational perturbation on the MPO in the very complex radiation environment of Mercury. The order-of-magnitude of this acceleration is quite high, about 10⁻⁶ m/s², because of the proximity to the Sun and the large area-to-mass ratio of the spacecraft. In the second part of the paper, we concentrated upon the short-period effects of direct solar radiation pressure and Mercury’s albedo. In particular, the disturbing accelerations have been compared with the ISA measurement error and the advantages of an on-board accelerometer are highlighted with respect to the best modelling of the non-gravitational perturbations in the strong radiation environment of Mercury. The readings from ISA, with an intrinsic noise level of about in the frequency band of 3·10⁻⁵–10⁻¹ Hz, guarantees a very significant reduction of the non-gravitational accelerations impact on the space mission accuracy, especially of the dominant direct solar radiation pressure.     

二维场地地震反应有限元分析的问题探讨

本文通过数值计算,在线弹性范围内初步分析了土层地震反应随不同侧向人工边界、有限土域取值范围、土介质的阻尼系数的变化情况;采用滞后阻尼假定,探讨了在土层时域分析中如何由滞后阻尼系数形成阻尼矩阵,给出了一个阻尼系数转换频率取值的经验公式。在此研究基础上编制了二维有限元SR2D计算程序。     

Modelling the cross-shore profiles of sand beaches using artificial neural networks

Abstract

Artificial neural networks (ANN) have been widely used successfully to solve coastal engineering problems. In this article, they are used to model the cross-shore profile of sandy beaches taking into account the possible effect of marine vegetation (Posidonia oceanica). Sixty ANNs were generated by modifying both the inputs and the number of neurons in the hidden layer. The best results were obtained with the following inputs: wave height perpendicular to the coast and the associated period and probability of occurrence, median sediment size, profile slope, and energy reduction factor due to P. oceanica. With these inputs and 10 neurons in the hidden layer, a mean absolute error of 0.22?m during training and 0.21?m during the test was obtained, which represents an improvement of 81.2% and 55.5% compared to models without and with P. oceanica.     

Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations (r ² = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.     

Gypsum karst intensification as a consequence of sulphur mining activity (Jaziv field,Western Ukraine)

The scope of this study is to evaluate the parameters of the gypsum–anhydrite strata karstification under the influence of the quarry exploitation of Jaziv sulphur field (West Ukraine) accompanying by drainage. The quarry drainage provoked the enormous depression cone forming with 100 km² in area and the corresponding intensification of chemical denudation of the gypsum–anhydrite strata. The gypsum karstification rate for the 29-year period of the quarry drainage was 0.06% with the dissolved rock volume of 1,328,507 m³ that is about 80 times higher than in natural conditions. For the drainage period (29 years) the karst denudation rate was 17,952 m³/km² against the natural background of 231.3 m³/km². The absolute value of artificial denudation for the studied massif area is 1.79 cm for 29 years or 0.062 mm/year while the denudation rate under natural conditions would be 0.231 cm for 29 years or 0.0008 mm/year. The forecasted volumes of the surface-evident collapses are evaluated using the relationship between the calculated amount of dissolved sulphate rocks and volumes of the current surface-evident collapses. According to this evaluation, the current collapses correspond to about 34% only from the dissolved rocks volume calculated for the drainage period.     

Application of neural network and MODIS 250m imagery for estimating suspended sediments concentration in Hangzhou Bay,China

Suspended sediments concentration (SSC) in surface water derived from bottom sediment resuspension or discharge of sediment-laden rivers is an important indication of coastal water quality and changes rapidly in high-energy coastal area. Since artificial neural networks (ANN) had been proven successful in modeling a variety of geophysical transfer functions, an ANN model to simulate the relationship between surface water SSC and satellite-received radiances was employed. In situ SSC measurements from the Hangzhou Bay and the Moderate-resolution Imaging Spectroradiometer (MODIS) 250 m daily products were adopted in this study. Significant correlations were observed between in situ measurements and band 1–2 reflectance values of MODIS images, respectively. Results indicated that application of ANN model with one hidden layer appeared to yield superior simulation performance (r ² = 0.98; n = 25) compared with regression analysis method. The RMSE for the ANN model was less than 10%, whereas the RMSE for the regression analysis was more than 25%. Results also showed that different tidal situations affect the model simulation results to some extent. The SSC of surface water in Hangzhou Bay is high and changes rapidly due to tidal flood and ebb during a tidal cycle. The combined utilization of Terra and Aqua MODIS data can capture the tidal cycle induced dynamic of surface water SSC. This study demonstrated that MODIS 250 m daily products and ANN model are useful for monitoring surface SSC dynamic within high-energy coastal water environments.     

利用人工神经网络模型预测西北太平洋热带气旋生成频数

通过对60年(1950~2009年)北半球夏、秋季(6~10月)热带气旋(TC)频数与春季(3~5月)大尺度环境变量的相关分析,挑选出8个相关性较高的前期预报因子建立人工神经网络(ANN)模型,对2010~2017年8年夏、秋季TC频数进行回报,并将回报结果与传统多元线性回归(MLR)方法所得结果进行对比分析。结果表明,ANN模型对60年历史数据的拟合精度高,相关系数高达0.99,平均绝对误差低至0.77。在8年回报中,ANN模型相关系数为0.80,平均绝对误差为1.97;而MLR模型相关系数仅为0.46,平均绝对误差为3.30。ANN模型在历史数据拟合和回报中的表现都明显优于MLR模型,未来可考虑应用于实际的业务预测中。