Fractionation of Cu, Pb and Zn in certified reference soils SRM 2710 and SRM 2711 using the optimized BCR sequential extraction procedure

Sequential extraction of metals from solid media is a common analytical tool used in environmental and exploration geochemistry. A number of procedures exist, but without harmonization and standardization, meaningful comparisons are tenuous without baseline data. A newly developed, standardized sequential extraction procedure (optimized BCR) was applied to two contaminated certified reference soils from Montana, US (SRM 2710 and SRM 2711) for Cu, Pb and Zn. Four operationally defined fractions were isolated, acid extractable, reducible, oxidizable, and residual (by aqua regia). Fraction-specific concentrations, percentages and recoveries for Cu, Pb and Zn were used to explore differences between the optimized BCR procedure and three other sequential extraction schemes with published data for SRM 2710 and 2711 (i.e. Tessier scheme, Geological Survey of Canada scheme and the original BCR scheme). Results indicate significant differences between the four schemes, even for schemes that are closely associated (i.e. the original and optimized BCR schemes). Order-of-magnitude fraction-specific concentration differences were observed, especially for Pb in the reducible fraction. Differences between schemes are worrisome because trends varied between metals, between fractions and between reference soils. These data reinforce the need for increased adoption of standardized sequential extraction procedures and further examination of different solid media.     

紫外分光光度法在水中阴离子表面活性剂标准样品研制中的应用

研究了阴离子表面活性剂水溶液的紫外吸收特性,并将研究结果用于阴离子表面活性剂水质标样的稳定性,均匀性和定值工作,简化了操作手续,获得了准确的分析结果。     

Scanning proton microprobe microanalysis for the assessment of homogeneity of IAEA urban dust reference materials

In order to develop new reference materials for microanalytical nuclear techniques, scanning proton microprobe(SPM) technique was used to determine homogeneity level within 100x200 μm2 micro-area on the small pieces of IAEA urban dust reference materials. The experimental methods were described in detail. The results show that IAEA-396A/M Vienna urban dust is homogeneous enough for small sample analysis of standard reference material (SRM).     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Safety Evaluation for a Biodiesel Process Using Prion-Contaminated Animal Fat as a Source (6 pp)

Background Due to the bovine spongiform encephalopathy (BSE), specified risk material (SRM) as well as animal meat and bone meal (MBM) are banned from the food and feed chain because of a possible infection with pathogenic prions (PrPSc). Nowadays, prions are widely accepted to be responsible for TSE(transmissible spongiform encephalopathies)-caused illnesses like BSE and scrapie, and especially for the occurrence of the new variant of CJD in humans. Presently, SRM and MBM are burnt under high temperatures to avoid any hazards for humans, animals or the environment. The aim of this study was to evaluate a method using animal fat separated from Category I material which includes SRM and the carcasses of TSE-infected animals, or animals suspected of being infected with TSE, as a source for producing biodiesel by transesterification, analogous to the biodiesel process using vegetable oil. Methods For this purpose, animal fat was spiked with scrapie-infected hamster brain equivalents – as representative for a TSE-infected animal – and the biodiesel manufacturing process was downscaled and performed under lab-scale conditions. Results and Discussion The results analysed by Western blotting showed clearly that almost each single step of the process leads to a significant reduction of the concentration of the pathogenic prion protein (PrPSc) in the main and side-products. Conclusion The data revealed that the biodiesel production, even from material with a high concentration of pathogenic prions, can be considered as safe. Recommendations and Outlook The obtained results indicated that biodiesel produced from prion-contaminated fat was safe under the tested process conditions. However, it has to be pointed out that the results cannot be generalized because a different process control using other conditions may lead to different results and then has to be analysed independently. It is clear that the production of biodiesel from high risk material represents a more economic usage than the combustion of such material.     

APPLICATION OF THE SNOWMELT RUNOFF MODEL USING MULTIPLE-PARAMETER LANDSCAPE ZONES ON THE TOWANDA CREEK BASIN,PENNSYLVANIA1

ABSTRACT: The Snowmelt Runoff Model (SRM) is designed to compute daily stream discharge using satellite snow cover data for a basin divided into elevation zones. For the Towanda Creek basin, a Pennsylvania watershed with relatively little relief, analysis of snow cover images revealed that both elevation and land use affected snow accumulation and melt on the landscape. The distribution of slope and aspect on the watershed was also considered; however, these landscape features were not well correlated with the available snow cover data. SRM streamflow predictions for 1990, 1993 and 1994 snowmelt seasons for the Towanda Creek basin using a combination of elevation and land use zones yielded more precise streamflow estimates than the use of standard elevation zones alone. The use of multiple-parameter zones worked best in non-rain-on-snow conditions such as in 1990 and 1994 seasons where melt was primarily driven by differences in solar radiation. For seasons with major rain-on-snow events such as 1993, only modest improvements were shown since melt was dominated by rainfall energy inputs, condensation and sensible heat convection. Availability of GIS coverages containing satellite snow cover data and other landscape attributes should permit similar reformulation of multiple-parameter watershed zones and improved SRM streamflow predictions on other basins.     

HTPB药柱老化对SRM内弹道性能的影响及偏差分析

为了定性和定量的描述药柱燃面、密度、燃速等因素对固体火箭发动机（SRM）内弹道性能的影响,在HTPB复合固体推进剂老化研究和SRM内弹道性能预估研究的基础上,分析了HTPB推进剂因贮存老化引起的SRM内弹道性能偏差,建立了性能偏差计算模型。结果表明,老化引起的药柱燃面、密度、燃速变化以及侵蚀燃烧引起的喷喉烧蚀是SRM贮存过程中影响内弹道性能偏差的直接因素,老化引起它们微小的波动都会引起发动机内弹道性能较大的偏离。在发动机寿命预估和进行可靠性分析时必须考虑老化对内弹道性能的影响。     

贮存老化条件下固体火箭发动机内弹道性能变化

研究了某型固体火箭发动机在贮存期间内弹道性能受推进剂药柱老化影响的变化规律。通过热加速老化试验．研究了HTPB推进荆的燃烧速度受老化影响情况：基于药柱材料的粘弹性本构关系,并考虑贮存环境温度波动、内压载荷和自重效应。对复合固体推进剂药柱完全固化后贮存老化条件下的变形进行了三维有限元分析。基于加速老化试验数据和数值仿真分析结果．对老化后固体火箭发动机的内弹道性能进行了分析。     

Structural reliability analysis of contact fatigue design of gears in wind turbine drivetrains

Advances in the design of large scale wind turbines have raised an interest for more optimized life cycle design of mechanical components (e.g. gears, bearings, shafts, etc.), with a better understanding of their performance over time. Development of the non-systematic, often deterministic design methods followed by the application of corresponding design standards that incorporate experience from current common practice have managed to produce mechanical components that comply with minimum safety requirements. However, such approaches cannot achieve optimized mechanical components, neither account for the performance over their service lives. Reliability-based design currently represents the most advanced method in order to achieve the targets mentioned above, by explicitly considering uncertainty of design variables. This paper describes a structural reliability method (SRM) for fatigue analysis of mechanical components of wind turbines. The method is based on the ‘so-called’ limit state functions of relevant failure modes. Two gear tooth fatigue failure modes (surface and subsurface pitting) were considered. The method is exemplified by a time-domain based gear contact fatigue analysis of the National Renewable Energy Laboratory's 750 kW land-based wind turbine. The sensitivity of the reliability index on some random parameters used for reliability-based gear contact fatigue analysis is estimated. The effect of inspection on reliability analysis is also briefly investigated.