Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Batch sorption dynamics and equilibrium for the removal of lead ions from aqueous phase using activated carbon developed from coffee residue activated with zinc chloride

Lignocellulosic materials are good precursors for the production of activated carbon. In this work, coffee residue has been used as raw material in the preparation of powder activated carbon by the method of chemical activation with zinc chloride for the sorption of Pb(II) from dilute aqueous solutions.The influence of impregnation ratio (ZnCl₂/coffee residue) on the physical and chemical properties of the prepared carbons was studied in order to optimize this parameter. The optimum experimental condition for preparing predominantly microporous activated carbons with high pore surface area (890 m²/g) and micropore volume (0.772 cm³/g) is an impregnation ratio of 100%. The developed activated carbon shows substantial capability to sorb lead(II) ions from aqueous solutions and for relative impregnation ratios of 75 and 100%, the maximum uptake is practically the same. Thus, 75% represents the optimal impregnation ratio.Batch experiments were conducted to study the effects of the main parameters such as contact time, initial concentration of Pb(II), solution pH, ionic strength and temperature. The maximum uptake of lead(II) at 25 °C was about 63 mg/g of adsorbent at pH 5.8, initial Pb(II) concentration of 10 mg/L, agitation speed of 200 rpm and ionic strength of 0.005 M. The kinetic data were fitted to the models of pseudo-first order and pseudo-second order, and follow closely the pseudo-second order model. Equilibrium sorption isotherms of Pb(II) were analyzed by the Langmuir, Freundlich and Temkin isotherm models. The Freundlich model gives a better fit than the others.Results from this study suggest that activated carbon produced from coffee residue is an effective adsorbent for the removal of lead from aqueous solutions and that ZnCl₂ is a suitable activating agent for the preparation of high-porosity carbons.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

基于现场测试的斜拉桥有限元模型修正

由于多年使用,桥的结构材料等都已经发生变化,因此在研究桥的当前工作状态时,必须依照现场测试结果,将初始模型修正为符合现阶段实际情况的有限元模型。本文以黄河胜利大桥为工程背景,根据设计资料建立了该桥初始有限元模型,用ANSYS通用软件开发了索力修正程序,该程序通过调整拉索的初始应变和附加质量块,使模型索力与设计索力之间误差在5%以内。计算发现,有限元模型中的施工顺序对成桥状态有着很大影响,应通过单元生死技术进行施工模拟。本文以各级加载工况下挠度、索力的变化情况作为控制目标,最终建立了能够反映加载工况下挠度变化、索力变化的黄河胜利大桥基准有限元模型,为今后监测桥的索力变化情况进而了解整座桥的工作状态奠定了基础。     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.     

2,4-D Mineralization in soil profiles of a cultivated hummocky landscape in Manitoba,Canada

The objective of this study was to quantify 2,4-D (2,4-dichlorophenoxyacetic acid) mineralization in soil profiles characteristic of hummocky, calcareous-soil landscapes in western Canada. Twenty-five soil cores (8 cm inner diameter, 50 to 125 cm length) were collected along a 360 m transect running west to east in an agricultural field and then segmented by soil-landscape position (upper slopes, mid slopes, lower slopes and depressions) and soil horizon (A, B, and C horizons). In the A horizon, 2,4-D mineralization commenced instantaneously and the mineralization rate followed first-order kinetics. In both the B and C horizons, 2,4-D mineralization only commenced after a lag period of typically 5 to 7 days and the mineralization rate was biphasic. In the A horizon, 2,4-D mineralization parameters including the first-order mineralization rate constant (k ₁), the growth-linked mineralization rate constant (k ₂) and total 2,4-D mineralization at the end of the experiment at 56 days, were most strongly correlated to parameters describing 2,4-D sorption by soil, but were also adequately correlated to soil organic carbon content, soil pH, and carbonate content. In both B and C horizons, there was no significant correlation between 2,4-D mineralization and 2,4-D sorption parameters, and the correlation between soil properties and 2,4-D mineralization parameters was very poor. The k ₁ significantly decreased in sequence of A horizon (0.113% day^?1) > B horizon (0.024% day^?1) = C horizon (0.026% day^?1) and in each soil horizon was greater than k ₂. Total 2,4-D mineralization at 56 days also significantly decreased in sequence of A horizon (42%) > B horizon (31%) = C horizon (27%). In the A horizon, slope position had little influence on k ₁ or k ₂, except that k ₁ was significantly greater in upper slopes (0.170% day^?1) than in lower slopes (0.080% day^?1). Neither k ₁ nor k ₂ was significantly influenced by slope position in the B or C horizons. Total 2,4-D mineralization at 56 days was not influenced by slope positions in any horizon. Our results suggest that, when predicting 2,4-D transport at the field scale, pesticide fate models should consider the strong differences in 2,4-D mineralization between surface and subsurface horizons. This suggests that 2,4-D mineralization is best predicted using a model that has the ability to describe a range of non-linear mineralization curves. We also conclude that the horizontal variations in 2,4-D mineralization at the field scale will be difficult to consider in predictions of 2,4-D transport at the field scale because, within each horizon, 2,4-D mineralization was highly variable across the twenty-five soil cores, and this variability was poorly correlated to soil properties or soil-landscape position.     

Chlorinated solvents in a petrochemical wastewater treatment plant: an assessment of their removal using self-organising maps

The self-organising map approach was used to assess the efficiency of chlorinated solvent removal from petrochemical wastewater in a refinery wastewater treatment plant. Chlorinated solvents and inorganic anions (11 variables) were determined in 72 wastewater samples, collected from three different purification streams. The classification of variables identified technical solvents, brine from oil desalting and runoff sulphates as pollution sources in the refinery, affecting the quality of wastewater treatment plant influent. The classification of samples revealed the formation of five clusters: the first three clusters contained samples collected from the drainage water, process water and oiled rainwater treatment streams. The fourth cluster consisted mainly of samples collected after biological treatment, and the fifth one of samples collected after an unusual event. SOM analysis showed that the biological treatment step significantly reduced concentrations of chlorinated solvents in wastewater.     

2007年11-12月国内环境事件

简要统计了2007年11-12月国内发生的各种环境事件47起,包括沙尘天气2起,污染事件15起,地震22起,山体滑坡和泥石流6起,以及旱灾2起.对自然灾害进行了讨论.     

Nutrient dynamics between sediment and overlying water in the inner part of Izmir Bay, Eastern Aegean

In this article a concept is described in order to predict and map the occurrence of benthic communities within and near the German Exclusive Economic Zone (EEZ) of the North Sea. The approach consists of two work steps: (1) geostatistical analysis of abiotic measurement data and (2) calculation of benthic provinces by means of Classification and Regression Trees (CART) and GIS-techniques. From bottom water measurements on salinity, temperature, silicate and nutrients as well as from punctual data on grain size ranges (0–20, 20–63, 63–2,000 μ) raster maps were calculated by use of geostatistical methods. At first the autocorrelation structure was examined and modelled with help of variogram analysis. The resulting variogram models were then used to calculate raster maps by applying ordinary kriging procedures. After intersecting these raster maps with punctual data on eight benthic communities a decision tree was derived to predict the occurrence of these communities within the study area. Since such a CART tree corresponds to a hierarchically ordered set of decision rules it was applied to the geostatistically estimated raster data to predict benthic habitats within and near the EEZ.