烟尘监测采样中一些问题的初步探讨

根据多年实践经验,对现场监测采样中出现的一些问题及异常情况进行分析探讨。     

Bayesian sample size determination for inspection of general corrosion of process components

A Bayesian approach-based method is proposed for calculating the minimum size of a sample to assess, with a specified precision, the integrity of process components suffering from general corrosion. The proposed method ensures that the error in the posterior estimate of the mean does not exceed a pre-defined acceptable margin of error at a specified confidence level. An analytical formula to estimate the sample size is introduced. The sample size obtained using the proposed method is smaller than a sample size obtained using the classical method with same confidence level. This reduces sampling inspection cost without affecting the precision of the estimate.     

引江入湖修复城市内湖自然生态环境

通过对环境监测部门长期监测数据的分析,探讨了“引江入湖,改善湖泊水质”设想的科学性以及实施过程中应注意解决的有关环境问题。文中探讨了治理湖泊富营养化,逐步修复湖泊自然生态环境应采取的对策。     

利用温度,压力变化进行样品前处理的新方法

文章介绍了亚临界水萃取，全蒸发技术和加速溶剂萃取这３种与温度压力有关的样品前处理的原理，及应用实例；通过与经典的索氏萃取法和超声以法的比较，表明这３种方法符合目前处理无溶剂化的潮流，具有萃取效率高，应用广泛的特点。     

Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys.     

中国国内救援队和国际地震灾害救援行动的简介与展望

本文介绍了国内地震灾害紧急救援队、国内其他救援队伍和国际地震灾害救援行动简要情况，并对国内、外救援的发展趋势作了展望。     

微波消解技术在金属元素分析样品制备中的应用

微波消解技术是一种快速而有效的样品制备方法，介绍了微波消解的原理、装置及特点、优点和在金属元素分析中的应用实例。     

Determining the optimum number of increments in composite sampling

Composite sampling can be more cost effective than simple random sampling. This paper considers how to determine the optimum number of increments to use in composite sampling. Composite sampling terminology and theory are outlined and a method is developed which accounts for different sources of variation in compositing and data analysis. This method is used to define and understand the process of determining the optimum number of increments that should be used in forming a composite. The blending variance is shown to have a smaller range of possible values than previously reported when estimating the number of increments in a composite sample. Accounting for differing levels of the blending variance significantly affects the estimated number of increments.

An assessment of sampling,preservation, and analytical procedures for arsenic speciation in potentially contaminated waters

This study was undertaken to ascertain optimal methods of sampling, preserving, separating, and analyzing arsenic species in potentially contaminated waters. Arsenic species are readily transformed in nature by slight changes in conditions. Each species has a different toxicity and mobility. The conventional field sampling method using filters of 0.45 μm in size could overestimate the dissolved arsenic concentrations, as passing suspended particles that can act as a sink or source of arsenic depending on the site condition. For arsenic species in neutral pH and iron-poor waters, the precipitation can be stable for up to 3 days without any treatment, but for longer periods, a preservative, such as phosphoric acid, is required. Also, the analytical procedure must be selected carefully because the levels and hydride generation efficiencies of arsenic in different species can vary, even for the same amount of arsenic. For arsenic speciation in samples that also include organic species, a hybrid high-performance liquid chromatography (HPLC) column and inductively coupled plasma mass spectrometry (ICP-MS) gave the best resolution and lowest detection limits. However, the procedure using a solid phase extraction (SPE) cartridge can be used economically and conveniently for analyzing samples containing only inorganic arsenic species, such as groundwater, especially that related to mine activity.     

The power of simulating experiments

Addressing complex ecological research questions often requires complex empirical experiments. However, due to the logistic constraints of empirical studies there is a trade-off between the complexity of experimental designs and sample size. Here, we explore if the simulation of complex ecological experiments including stochasticity-induced variation can aid in alleviating the sample size limitation of empirical studies. One area where sample size limitations constrain empirical approaches is in studies of the above- and belowground controls of trophic structure. Based on a rule- and individual-based simulation model on the effect of above- and belowground herbivores and their enemies on plant biomass, we evaluate the reliability of biomass estimates, the probability of experimental failure in terms of missing values, and the statistical power of biomass comparisons for a range of sample sizes. As expected, we observed superior performance of setups with sample sizes typical of simulations (n = 1000) as compared to empirical experiments (n = 10). At low sample sizes, simulated standard errors were smaller than expected from statistical theory, indicating that stochastic simulation models may be required in those cases where it is not possible to perform pilot studies for determining sample sizes. To avoid experimental failure, a sample size of n = 30 was required. In conclusion, we propose that the standard tool box of any ecologist should comprise a combination of simulation and empirical approaches to benefit from the realism of empirical experiments as well as the statistical power of simulations.