基于三明治抽样的市域耕地土壤重金属监测点位优化布设方法

如何平衡成本和精度之间的矛盾是区域土壤环境监测网构建亟待解决的技术难题。以广东省江门市耕地土壤砷含量为研究对象,开展基于三明治抽样的土壤环境监测点位优化研究。按照1 km×1 km网格在研究区域均匀布设3 375个点位,采用三明治抽样方法对其点位进行抽样优化,并运用Cochran公式、空间插值表征、交叉验证、独立验证等验证方法对不同期望精度下的抽样样本进行验证。结果表明,(1)期望精度为5%、10%、15%、20%的三明治抽样样本量分别为278、147、102、80个。(2)抽样结果的4种验证方法表明,期望精度为5%和10%的抽样样本能满足监测精度要求。该精度下样本量均满足利用Cochran经典公式在相应置信水平和相对误差条件下获取的合理采样数量,并且相应的半变异函数模型拟合度好、均方根误差值(RMSE)小、克里金插值代表性强。(3)期望精度为10%的抽样样本在保证监测精度不变的同时,大大降低了监测成本,其监测点位从3 375个降至147个,点位数量降低了96%。总之,抽样样本的期望精度值越小,监测精度越高。在江门市市域尺度上,期望精度为10%的三明治抽样是一种高精度、低成本、代表性强的土壤环境监测点位优化方法。     

肥料抽样的有关问题

肥料质量监督抽查是进行肥料打假工作的直接手段。近年来为防止假冒伪劣肥料产品坑农害农，各级肥料管理部门都加大了肥料抽样力度。但在肥料抽样过程中，不规范操作时有出现。为保证抽样工作质量，抽样人员应注意如下问题。     

除草剂苯噻草胺土壤滞留因子的蒙特卡洛模拟

采用简单随机抽样和拉丁超立方抽样两种不同方法对除草剂苯噻草胺的土壤滞留因子模型进行蒙特卡洛模拟。结果表明，不同的土壤条件下，苯噻草胺的滞留因子（R）的数值呈Beta分布，最大极值为176．5，最小极值为3．3，最可能的取值在20－30的范围内。结果还显示了采用拉丁超立方抽样方法能够显著降低抽样的不均匀性，进而能降低Beta分布拟合的不确定性。本文还通过色轴在4维图上形象地表征了R值的变化。     

环境质量分析的数理统计方法

我们掌握的环境数据都是从环境总体抽样出来的样本，必须有一个科学方法去处理样本才能获得对环境总体的正确认识，正确指导环境决策，才能从样本中获取尽可能多的信息。     

利用人体肺部PM浓度模型定量评估广州市夏、冬季抽样人群PM2.5的暴露

为了定量评价细粒子PM2.5的人体肺部暴露水平,首先,对暴露、暴露量、剂量、暴露评价等多个概念进行了明确界定,在此基础上,重点引入国际辐射防护委员会(ICRP)的人体肺部PM浓度模型对人体肺部PM2.5的浓度进行了模拟,定量化研究了广州市抽样人群PM2.5的作用剂量,作用剂量直接反映了进入人体肺部污染物的量.结果表明,广州市抽样人群平均PM2.5的作用剂量在肺部咽喉以外部分(ET)、支气管部分(BB)、肺泡空隙区(AI)分别为576.8￣975.9μg·d-1、357.5￣619.8μg·d-1、154.4￣290.1μg·d-1.     

长春市主要蔬菜中农药残留分析

采用速测法和气相色谱法研究了长春市市场销售的主要蔬菜中的农药残留问题。2002年秋季对长春市的9个蔬菜批发市场的16种蔬菜进行抽样检测，蔬菜中有机磷类和氨基甲酸酯类农药平均超标率为11．05％，残留的主要农药种类是甲胺磷、氧化乐果和敌敌畏。检测16种蔬菜中有9种超标。2003年春季同样对9个蔬菜市场的17种蔬菜进行抽样检测，农药残留平均超标率为11．0％，残留的农药主要种类是甲基对硫磷、对硫磷、甲拌磷、敌敌畏、氯氰菊酯和氰戊菊酯等，检测的17种蔬菜中有3种严重超标。由此可见蔬菜中农药残留还是很严重的问题，政府与有关部门仍需加强管理。     

宁波市常用家电产品回收回用调查分析

通过对宁波市居民的家电产品系列问题的随即抽样问卷调查,阐述了宁波家电拥有量、使用寿命、更新原因和速率、居民购买家电和处理废旧家电时环保意识和行为,了解了本地区废旧家电的基本情况以及废旧家电处理的途径,在对调查数据进行分析的基础上,给出了结论和建议。     

广州白云山风景区植被景观异质性分析

采用信息论的线性抽样方法对白云山风景区的景观要素进行了景观异质性分析,结果表明,白云山风景区景观的异质性较大,并与景观要素的空间分布密切相关。阔叶林类型的相对频率与平均信息量最大,已在白云山风景区处于景观基质的地位,对白云山的景观结构和景观生态的变化起着决定性的作用。     

闽北次生常绿阔叶林主要树种空间分布格局及其应用研究

应用聚集度指标、Ｉｗａｏ 方程和Ｔａｙｌｏｒ 幂法则模型等测定方法,研究了闽北次生常绿阔叶林９ 个主要树种的空间分布格局．研究结果表明,闽北次生常绿阔叶林９ 个主要树种的空间分布呈聚集分布,分布的基本成份为个体群,个体群分布聚集．根据Ｉｗａｏ 的Ｍ＊ 与ｘ 的回归方程,计算了９ 个树种在不同密度和允许误差下的理论抽样数．     

我国鸟类监测的现状、问题与对策

鸟类是开展生物多样性监测的重要指示类群.对鸟类的长期、系统监测工作在欧美已经有百余年历史,且野鸟多样性指数已经成为欧美一些国家开展生物多样性监测工作的官方指标.通过对国内鸟类监测现状的分析,结合监测对象、指标、抽样策略和监测方法等监测工作中应重点关注的几个方面,提出了我国鸟类监测存在的问题,并结合国情提出了下一步开展鸟类监测工作的对策与建议.建议我国有关部门尽快制定鸟类监测技术规程和标准,统一数据收集和分析平台,逐步构建政府引导,科研院所、高等院校、自然保护区、观鸟协会和志愿者等多方参与的鸟类监测网络.     

Inferring linear feature use in the presence of GPS measurement error

Global Positioning System (GPS) collars are increasingly used to study animal movement and habitat use. Measurement error is defined as the difference between the observed and true value being measured. In GPS data measurement error is referred to as location error and leads to misclassification of observed locations into habitat types. This is particularily true when studying habitats of small spatial extent with large amounts of edge, such as linear features (e.g. roads and seismic lines). However, no consistent framework exists to address the effect of measurement error on habitat classification of observed locations and resulting biological inference. We developed a mechanistic, empirically-based method for buffering linear features that minimizes the underestimation of animal use introduced by GPS measurement error. To do this we quantified the distribution of measurement error and derived an explicit formula for buffer radius which incorporated the error distribution, the width of the linear feature, and a predefined amount of acceptable type I error in location classification. In our empirical study we found the GPS measurement error of the Lotek GPS_3300 collar followed a bivariate Laplace distribution with parameter ρ = 0.1123. When we applied our method to a simulated landscape, type I error was reduced by 57%. This study highlights the need to address the effect of GPS measurement error in animal location classification, particularily for habitats of small spatial extent.     

Zum Stichprobenfehler im Human-Biomonitoring

Goal and Scope

Human biomonitoring determines the concentration of xenobiotics in populations by means of smaller samples, thus necessarily arising sampling errors. These are determined.

Methods

For a fictitious population of 200,000 persons, differently broad xenobiotic concentration distributions were simulated. Samples of varying size were randomly drawn and the sampling error, defined as the proportional difference between the geometric means of sample and population, was determined.

Results and Conclusions

The sampling error depends on the sample size and the width of the concentration distribution; its estimation is possible for any xenobiotic, given it has lognormal distribution, and the sample size is between 10 and 50,000. For its estimation an equation was derived.

Outlook

When presenting and interpreting results of human biomonitoring, the sampling error must be considered, together with the uncertainty of the measurement.     

Evaluation of sampling methods for the estimation of structural indices in forest stands

The paper is about the accurate (i.e. unbiased and precise) and efficient estimation of structural indices in forest stands. We present SIAFOR, a computer programme for the calculation of four nearest-neighbour indices, which describe the spatial arrangement of tree positions, the distribution pattern of species, and the size differentiation between trees. The study uses SIAFOR as a sampling simulator in eight completely stem-mapped forest stands of varying area and structural complexity. We statistically evaluate two sample types (distance and plot sampling), comparing sampling error, bias and minimum sample size for index estimation. We introduce the concepts of measurement expansion factor (MEF) and design expansion factor (DEF) for the technical evaluation of sample type efficiency (optimal sample type). Results indicate that sampling error can reach high levels and that minimum sample sizes for index estimation often amply exceed the limit of 20% of tree density or 20 trees per species per hectare, that we set as the highest feasible sample size in normal situations. We found clear feasibility limits (in terms of minimal tree densities and reachable accuracy levels) for the estimation of all investigated indices. Generally, equal or higher sample sizes are needed for plot sampling than for distance sampling to reach equal accuracy levels. Nevertheless, plot sampling resulted more efficient for the estimation of tree size differentiation at low to medium accuracy levels. For all other investigated indices distance sampling resulted more efficient than plot sampling. Minimum sample size increases with accuracy and is negatively correlated with tree density. At a given accuracy level minimum sample size is highest for the estimation of relative mingling and lowest for tree size differentiation; furthermore it is generally lower in large stands than in small ones. Because of the consistency of our conclusions in all of the investigated stands, we think they apply in most stands of similar area (between 1 and 10 ha) and species diversity (not more than four species).     

Prospective evaluation of designs for analysis of variance without knowledge of effect sizes

Estimation of design power requires knowledge of treatment effect size and error variance, which are often unavailable for ecological studies. In the absence of prior information on these parameters, investigators can compare an alternative to a reference design for the same treatment(s) in terms of its precision at equal sensitivity. This measure of relative performance calculates the fractional error variance allowed of the alternative for it to just match the power of the reference. Although first suggested as a design tool in the 1950s, it has received little analysis and no uptake by environmental scientists or ecologists. We calibrate relative performance against the better known criterion of relative efficiency, in order to reveal its unique advantage in controlling sensitivity when considering the precision of estimates. The two measures differ strongly for designs with low replication. For any given design, relative performance at least doubles with each doubling of effective sample size. We show that relative performance is robustly approximated by the ratio of reference to alternative $\alpha $ quantiles of the $F$ distribution, multiplied by the ratio of alternative to reference effective sample sizes. The proxy is easy to calculate, and consistent with exact measures. Approximate or exact measurement of relative performance serves a useful purpose in enumerating trade-offs between error variance and error degrees of freedom when considering whether to block random variation or to sample from a more or less restricted domain.     

Effects of uncertainty and variability on population declines and IUCN Red List classifications

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age‐structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.     

大气颗粒物“二重源解析”技术的误差分析

"二重源解析"模型计算结果的误差是采样误差、样品处理误差、化学组分分析误差、数据处理误差以及数学模型误差等所有误差的积累。提出了"二重源解析"解析结果的相对误差和标准偏差表达式,并用之计算了某市利用"二重源解析"模型计算的源贡献值的相对误差和标准偏差,还针对从源排放出来的初始态颗粒物在传输过程中发生的扬尘态变化提出了扬尘转化率的概念和计算方法。     

Estimation of the CDF of a finite population in the presence of acalibration sample

We compare the performance of a number of estimators of the cumulative distribution function (CDF) for the following scenario: imperfect measurements are taken on an initial sample from afinite population and perfect measurements are obtained on a small calibration subset of the initial sample. The estimators we considered include two naive estimators using perfect and imperfect measurements; the ratio, difference and regression estimators for a two-phasesample; a minimum MSE estimator; Stefanski and Bay's SIMEX estimator (1996); and two proposed estimators. The proposed estimators take the form of a weighted average of perfect and imperfect measurements. They are constructed by minimizing variance among the class of weighted averages subject to an unbiasedness constraint. They differ in the manner of estimating the weight parameters. The first one uses direct sample estimates. The second one tunes the unknown parameters to an underlying normal distribution. We compare the root mean square error (RMSE) of the proposed estimator against other potential competitors through computer simulations. Our simulations show that our second estimator has the smallest RMSE among thenine compared and that the reduction in RMSE is substantial when the calibration sample is small and the error is medium or large.     

Coral architecture affects the habitat choice and form of associated gobiid fishes

Gobiid fishes of the genus Gobiodon live in strong association with certain reef-building corals that vary considerably in size and architecture. These fishes hence are excellent model systems for studying evolutionary adaption to specific microhabitats. Using a sample of Gobiodon histrio and G. rivulatus and their most important host corals (Acropora digitifera and A. gemmifera) from the northern Red Sea, we assess (1) how corals that are occupied by gobies differ in their architecture from colonies that are not occupied and (2) how fish body shape is associated with the architecture of their host coral. Fish body shape was assessed by geometric morphometric techniques. Coral measurements included colony size, branch length (BL), and interbranch as well as branch tip distance of adjacent branches, for which we applied a new and non-destructive measurement technique based on casts of two-component epoxy resin. The most important factor influencing the occupation of corals was a BL of more than 5 cm. The distance between coral branches was clearly related to the width of the fishes and hence constrained overall fish size. G. histrio and G. rivulatus differ in adult body shape as well in their allometric development of lateral body compression, resulting in different maximum body sizes attainable in the restricted interbranch space of corals. The strong dependence of coral-associated fishes on large coral colonies with specific architectures increases the extinction risk of fishes within deteriorating coral reefs.     

A critique of statistical aspects of ecological studies in spatial epidemiology

In this article, the mathematical assumptions of a number of commonly used ecological regression models are made explicit, critically assessed, and related to ecological bias. In particular, the role and interpretation of random effects models are examined. The modeling of spatial variability is considered and related to an underlying continuous spatial field. The examination of such a field with respect to the modeling of risk in relation to a point source highlights an inconsistency in commonly used approaches. A theme of the paper is to examine how plausible individual-level models relate to those used in practice at the aggregate level. The individual-level models acknowledge confounding, within-area variability in exposures and confounders, measurement error and data anomalies and so we can examine how the area-level versions consider these aspects. We briefly discuss designs that efficiently sample individual data and would appear to be useful in environmental settings.     

Difficulties with the lognormal model in mean estimation and testing

A frequent assumption in environmental risk assessment is that the underlying distribution of an analyte concentration is lognormal. However, the distribution of a random variable whose log has a t-distribution has infinite mean. Because of the proximity of the standard normal and t-distribution, this suggests that a distribution such as the gamma or truncated normal, with smaller right tail probabilities, might make a better statistical model for mean estimation than the lognormal. In order to assess the effect of departures from lognormality on lognormal-based statistics, we simulated complete lognormal, truncated normal, and gamma data for various sample sizes and coefficients of variation. In these cases, departures from lognormality were not easily detected with the Shapiro-Wilk test. Various lognormal-based estimates and tests were compared with alternate methods based on the ordinary sample mean and standard error. The examples were also considered in the presence of random left censoring with the mean and standard error of the product limit estimate replacing the ordinary sample mean and standard error. The results suggest that in the estimation of or tests about a mean, if the assumption of lognormality is at all suspect, then lognormal-based approaches may not be as good as the alternative methods.