Einige Probleme bei der Anwendung des MPN-Verfahrens zur quantitativen Salmonellenbestimmung

So far, the MPN technique has been the method chosen for detecting pathogenic bacteria in waters, since it allows us to use larger volumes. In the evaluation, however, comparatively frequently (10 … 15%) nontypical codes occur. The validity of the Poisson model is confirmed for pure cultures by extensive laboratory experiments with pure cultures and controlled mixed cultures. In mixed cultures, however, a comparatively large number of atypical codes occur, since obviously a single organism is not sufficient for the formation of a growth-cover on the test tube, so that the true number of germs may be considerably underestimated. Obviously, the accompanying flora has an inhibiting effect on the salmonella detection.     

太湖人工生态系统中氮循环细菌分布

对太湖五里湖的以水生高等植物为主的湖泊人工生态系统,用最大可能数法,测定了该系统内各种生态类型水生高等植物群落内的４类氮循环细菌的分布。结果表明反硝化细菌的最大可能数,在水生高等植物群落内水体中较敞水区湖水高２－５个数量级差异极显著;漂服植物各落内水体的反硝化细菌ＭＰＮ值较沉水和浮叶植物群落内水体高２－３个数量级,差异极显著;     

太湖人工生态系统中氮循环细菌分布

对太湖五里湖的以水生高等植物为主的湖泊人工生态系统,用最大可能数（ＭＰＮ）法,测定了该系统内各种生态类型水生高等植物群落内的４类氮循环细菌的分布．结果表明,反硝化细菌的最大可能数（ＭＰＮ）,在水生高等植物群落内水体中较敞水区湖水高２－５个数量级,差异极显著（Ｐ＜０．０１）,漂浮植物群落内水体的反硝化细菌ＭＰＮ值较沉水和浮叶植物群落内水体高２－３个数量级,差异极显著（Ｐ＜０．０１）;硝化细菌ＭＰＮ值,敞水区湖水高于凤眼莲、水花生群落内水体,差异显著（ Ｐ＜ ０． ０５）,菱群落与其他群落比较,亦有极显著性差异（ Ｐ＜ ０． ０１）;亚硝化细菌ＭＰＮ值,在水生高等植物群落内的水体中较敞水区高 ３－ ４个数量级,差异极显著（ Ｐ＜ ０． ０１） ;氨化细菌ＭＰＮ值,在水生高等植物群落内的水体中高于敞水区水体．除硝化细菌外,反硝化、亚硝化及氨化细菌均在根际处最为密集,且由根际向外呈现递减趋势．     

基于地震对应概率谱的多参数综合异常研究

在单参数地震对应概率谱和累计滑动平均概率方法的基础上,研究了多参数滑动极值平均概率方法,用以定量识别地震前兆综合异常特征.首先,该方法基于地震对应概率谱分析,将原始数据时间序列转换成概率时间序列;其次,采用多点累计滑动平均方法得到不同参数滑动平均概率Pij;再次,求解不同参数的多点滑动极值概率Mij;最后,求解多参数滑动极值平均概率Pc.文中以新疆天山地区不同地震学参数的时间序列为原始数据进行算例分析,结果表明,当考察时段为18个月时,利用多参数滑动极值平均概率可以较好地识别地震前兆综合异常,其异常对应比例和地震对应比例分别为13/13和22/27,异常信度较高,但存在一定程度的漏报现象.     

Identifizierung ausgewählter Pestizide im Wasser mit Hilfe eines screening-Systems auf Grundlage der Dünnschichtchromatographie

After their extraction from water, 16 water pollutants of the families of triazines, urea herbicides, phenoxycarboxylic acids and organochlorine and organophosphorus insecticides are separated and identified by thin-layer chromatography. The method is suitable for routine analyses and was tested on model waters, inclusive of water from the Elbe river. The rate of redetection is up to 80%, the detection limit is at 0.5… 2 μg of the individual pollutants.     

Oregon Beach Monitoring Program: Bacterial exceedances in marine and freshwater creeks/outfall samples, October 2002-April 2005

A total of 3086 samples, both marine (2916) and freshwater creeks/outfall samples (170) were collected and analyzed for enterococci during October 2002-April 2005, from 52 designated beaches in Oregon. A total of 3.2% (99/3086) of the samples at 21 beaches exceeded 158 most probable number (MPN)/100 milliliters (mL). The average enterococci levels of these 99 exceedances was 559 MPN/100 mL, with a maximum of 4352 MPN/100 mL (Otter Rock and Ona) and a minimum of 160 MPN/100 mL (Sunset Bay State Park, Bastendorff, and Mill). For marine water, 77/2916 (2.6%) exceeded 158 MPN/100 mL. For freshwater, 22/170 (12.9%) exceeded 158 MPN/100 mL, with a maximum of 587 MPN/100 mL at Sunset Bay. Sixty percent of the marine and 9% of freshwater exceedances occurred during the winter. Seventy-two percent (55/77) of the marine exceedances occurred after rainfall events (0.01-60.0 mm). At Harris and Mill Beaches, cumulative rainfall was highly correlated with bacterial densities, R = 0.7. Rainfall, at both beaches, explained about one-half the variation in log10 bacteria density (R2 = 0.5). Additional monitoring is warranted to further characterize bacterial contamination in Oregon waters.     

Parameter and quantile estimation of the 2-parameter kappa distribution by maximum likelihood

Asymptotic properties of maximum likelihood parameter and quantile estimators of the 2-parameter kappa distribution are studied. Eight methods for obtaining large sample confidence intervals for the shape parameter and for quantiles of this distribution are proposed and compared by using Monte Carlo simulation. The best method is highlighted on the basis of the coverage probability of the confidence intervals that it produces for sample sizes commonly found in practice. For such sample sizes, confidence intervals for quantiles and for the shape parameter are shown to be more accurate if the quantile estimators are assumed to be log normally distributed rather than normally distributed (same for the shape parameter estimator). Also, confidence intervals based on the observed Fisher information matrix perform slightly better than those based on the expected value of this matrix. A hydrological example is provided in which the obtained theoretical results are applied.     

Random errors in the hydrogeological parameters of a two-layer stratum evaluated by multi-well pumping tests

Random errors in estimates of flow and storage parameters of a two-layer stratum, derived from multi-well pumping test data are examined. It was found that the relative random error with a confidence probability of 0.954 is appreciably less than 100% only for the estimates of aquifer transmissivity, while the estimates of the hydraulic conductivity and the specific yield of the overlying deposits show relative random errors with the same confidence probability in excess of 100%. Therefore, the random errors of parameters are to be given as multiples of the established values.     

震源最佳反演结果误差的随机加权估计

提出利用随机加权方法估计地震矩张量最佳反演结果的误差．这种方法不对误差来源作任何分布假设，只需重复产生一定数量的服从Ｄｉｒｉｃｈｌｅｔ分布的权向量．对于每个权向量，将其各分量分配到每条地震记录上，相应地确定一个震源参数解．对这些震源参数反演解进行基本的统计分析，便能估计出最佳解的方差、协方差和置信区间．还讨论了１９８６年台湾地震（Ｍｓ＝７．８）的震源矩张量中存在各向同性分量这一解答的可靠性．     

Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty

Stream flow predictions in ungauged basins are one of the most challenging tasks in surface water hydrology because of nonavailability of data and system heterogeneity. This study proposes a method to quantify stream flow predictive uncertainty of distributed hydrologic models for ungauged basins. The method is based on the concepts of deriving probability distribution of model's sensitive parameters by using measured data from a gauged basin and transferring the distribution to hydrologically similar ungauged basins for stream flow predictions. A Monte Carlo simulation of the hydrologic model using sampled parameter sets with assumed probability distribution is conducted. The posterior probability distributions of the sensitive parameters are then computed using a Bayesian approach. In addition, preselected threshold values of likelihood measure of simulations are employed for sizing the parameter range, which helps reduce the predictive uncertainty. The proposed method is illustrated through two case studies using two hydrologically independent sub‐basins in the Cedar Creek watershed located in Texas, USA, using the Soil and Water Assessment Tool (SWAT) model. The probability distribution of the SWAT parameters is derived from the data from one of the sub‐basins and is applied for simulation in the other sub‐basin considered as pseudo‐ungauged. In order to assess the robustness of the method, the numerical exercise is repeated by reversing the gauged and pseudo‐ungauged basins. The results are subsequently compared with the measured stream flow from the sub‐basins. It is observed that the measured stream flow in the pseudo‐ungauged basin lies well within the estimated confidence band of predicted stream flow. Copyright © 2013 John Wiley & Sons, Ltd.     

Kurzzeitermittlung der Ablaufqualität von Belebtschlammanlagen anhand der Atmungsaktivität

For checking the quality of the effluent from an activated sludge plant for waste-waters from the sulphite pulp production the respiration of an activated sludge sample in air-saturated water is determined initially without the addition of substrate (ground respiration) and after that with an addition of substrate (methanol, substrate respiration) by means of the Clark-electrode. The measuring device is designed by means of zero displacement in such a way that the difference of the oxygen concentration is indicated by a time relay as the difference quotient. Between the ground respiration and the effluent concentration of BOD₅ exists a significant positive regression, which can be used for controlling the plant. The determination of the substrate respiration serves only for checking with respect to perhaps given inhibition of the conversion of matter by the activated sludge. The determination of the activity of the activated sludge sample takes only 20 min; inclusive of the required determination of the dry matter of sludge, the technique provides a well reproducible measured value for the biochemical conversion in the activated sludge plant within 1… 1.5 h.     

Estimation of confidence interval for hydrological design value for some continuous distributions under complete and censored samples

The quantile of a probability distribution, known as return period or hydrological design value of a hydrological variable is the value corresponding to fixed non-exceedence probability and is very important notion in hydrology. In hydraulic engineering design and water resources management, confidence interval (CI) estimation for a population quantile is of primary interest and among other applications, is used to assess the pollution level of a contaminant in water, air etc. The accuracy on such estimation directly influences the engineering investments and safety. The two parameter Weibull, Pareto, Lognormal, Inverse Gaussian, Gamma are some commonly used probability models in such applications. In spite of its practical importance, the problem of CI estimation of a quantile of these widely applicable distributions has been less attended in the literature. In this paper, a new method is proposed to obtain a CI for a quantile of any distribution for which [or the probability distribution of any one-to-one function of the underlying random variable (RV)] generalized pivotal quantities (GPQs) exist for its parameters. The proposed method is elucidated by constructing CIs for quantiles of Weibull, Pareto, Lognormal, Extreme value distribution of type-I for minimum, Exponential and Normal distributions for complete as well as type II singly right censored samples. The empirical performance evaluation of the proposed method evinced that the proposed method has exact well concentrated coverage probabilities near the nominal level even for small uncensored samples as small as 5 and for censored samples as long as the proportion of censored observations is up to 0.70. The existing methods for Weibull distribution have poor or dispersed coverage probabilities with respect to the nominal level for complete samples. Applications of the proposed method in ground water monitoring and in the assessment of air pollution are illustrated for practitioners.     

Estimation of confidence intervals of quantiles for the Weibull distribution

 Estimation of confidence limits and intervals for the two- and three-parameter Weibull distributions are presented based on the methods of moment (MOM), probability weighted moments (PWM), and maximum likelihood (ML). The asymptotic variances of the MOM, PWM, and ML quantile estimators are derived as a function of the sample size, return period, and parameters. Such variances can be used for estimating the confidence limits and confidence intervals of the population quantiles. Except for the two-parameter Weibull model, the formulas obtained do not have simple forms but can be evaluated numerically. Simulation experiments were performed to verify the applicability of the derived confidence intervals of quantiles. The results show that overall, the ML method for estimating the confidence limits performs better than the other two methods in terms of bias and mean square error. This is specially so for γ≥0.5 even for small sample sizes (e.g. N=10). However, the drawback of the ML method for determining the confidence limits is that it requires that the shape parameter be bigger than 2. The Weibull model based on the MOM, ML, and PWM estimation methods was applied to fit the distribution of annual 7-day low flows and 6-h maximum annual rainfall data. The results showed that the differences in the estimated quantiles based on the three methods are not large, generally are less than 10%. However, the differences between the confidence limits and confidence intervals obtained by the three estimation methods may be more significant. For instance, for the 7-day low flows the ratio between the estimated confidence interval to the estimated quantile based on ML is about 17% for T≥2 while it is about 30% for estimation based on MOM and PWM methods. In addition, the analysis of the rainfall data using the three-parameter Weibull showed that while ML parameters can be estimated, the corresponding confidence limits and intervals could not be found because the shape parameter was smaller than 2.     

Assessment of the biological quality of riverine water using pathogenicity islands (PAIs) of coliform bacteria as pollution indicator

The biological methods of quality assessment of water include the determination of harmful biological entities. In this study, quality of water from two riverine study sites was monitored in terms of the most probable number (MPN index) and pathogenicity. MPN index was found to be 0.11 and 0.15/100 mL which was also confirmed by membrane filtration technique (MFT). The isolated pure cultures were subjected to various pathogenicity tests including blood haemolysis and biofilm formation. Out of ten isolates, six showed positive results and these potentially pathogenic isolates were found to be resistant to various antibiotics like erythromycin (E), vancomycin (VA), chloramphenicol (C), tetracyclin (TE), acidoclav (AC) and kanamycin (K). Genetic analysis of these six isolates confirms the presence of flanking regions of virulence cassettes pathogenicity island (PAI) in their genome. Hence, in addition to the MPN test, the pathogenicity tests using the amplification of flanking regions of PAI can be used as a marker for determination of biological quality of water.     

Assessing Oregon's twenty-six coastal beach areas for recreational water quality standards

Water samples from 26 Oregon beaches were analyzed for Escherichia coli (E. coli) and enterococci concentrations by the Oregon Department of Human Services (ODHS) Public Health Laboratory. Nine Oregon beaches exceeded US Environmental Protection Agency's (USEPA) single sample maximum density of 104 enterococci colony forming units (cfu) per 100 mL with levels ranging from 121 to 4325 most probable number (MPN)/100 milliliters (mL). Otter Rock at South Cove had the highest enterococci concentration at 4352 MPN/100 mL. The results from two Oregon beaches exceeded Oregon Department of Environmental Quality's (ODEQ) estuarine E. coli standard of 406 organisms/100 mL. Otter Rock at South Cove had the highest E. coli concentration at 1850 MPN/100 mL while Road's End had an E. coli density of 771 MPN/100 mL. Results of this study suggest that adopting USEPA's marine enterococci standard in lieu of the ODEQ's estuarine standard will lead to increased Oregon beach water standard failures.     

含噪声数据反演的概率描述

根据贝叶斯理论给出了对含噪声地球物理数据处理的具体流程和方法,主要包括似然函数估计和后验概率计算.我们将数据向量的概念扩展为数据向量的集合,通过引入数据空间内的信赖度,把数据噪声转移到模型空间的概率密度函数上,即获得了反映数据本身的不确定性的似然函数.该方法由于避免了处理阶段数据空间内的人工干预,因而可以保证模型空间中的概率密度单纯反映数据噪声,具有信息保真度高、保留可行解的优点.为了得到加入先验信息的后验分布,本文提出了使用加权矩阵的概率分析法,该方法在模型空间直接引入地质信息,对噪声引起的反演多解性有很强的约束效果.整个处理流程均以大地电磁反演为例进行了展示.     

A Bayesian approach to monitoring and assessing unexploded ordnance remediation progress from munitions testing ranges

We present a statistical methodology which aims to monitor and assess the progress of unexploded ordnance remediation. We explicitly quantify the probability that each buried sensor-identified anomaly is not a target of interest conditional on the information gleaned from anomalies which have been dug and identified. We provide a measure of confidence that the anomalies which remain onsite after remediation are not unexploded ordnance—this measure of confidence is gleaned through Monte Carlo methods. The methodology is iterative in that, at any point in the remediation process, we can assess remediation progress and compute the probability that no targets of interest remain given the available dig information.     

MOD-AGE: An age-depth model construction algorithm

We present MOD-AGE, a new system for chronology construction. MOD-AGE can be used for profiles that have been dated using other techniques. The system uses three basic measurements for input data: activities, the atomic ratio or age, and the depth measurement. Based on the measurement results (probability distributions), MOD-AGE estimates the confidence bands of the age-depth model and uses nonparametric methods to avoid the consequences of failure to meet assumptions. Using the normal distribution of the measured data (activities, atomic ratios, depths, etc.), we applied a Monte Carlo simulation to the model age and depth values. Several fitting models could be applied to estimate the relationships. Following multiple tests, we chose the locally weighted scatterplot smoothing (LOESS) method.The stratigraphic correction procedure applied in the MOD-AGE program uses a probability calculus, which assumes that all of the sample ages are correctly estimated. The probability age distribution from the samples was used to estimate the most probable sequence in accordance with the superposition rule.This study presents MOD-AGE as a tool for the chronology construction of speleothems that have been analyzed with the U-series method and compares MOD-AGE to the StalAge algorithm presented by Scholz and Hoffmann (2011).The MOD-AGE software was written in, made by Borland, RAD STUDIO 2007 and is available from the authors as freeware.     

The comparison of sensitivity analysis of hydrological uncertainty estimates by GLUE and Bayesian method under the impact of precipitation errors

The input uncertainty is as significant as model error, which affects the parameter estimation, yields bias and misleading results. This study performed a comprehensive comparison and evaluation of uncertainty estimates according to the impact of precipitation errors by GLUE and Bayesian methods using the Metropolis Hasting algorithm in a validated conceptual hydrological model (WASMOD). It aims to explain the sensitivity and differences between the GLUE and Bayesian method applied to hydrological model under precipitation errors with constant multiplier parameter and random multiplier parameter. The 95 % confidence interval of monthly discharge in low flow, medium flow and high flow were selected for comparison. Four indices, i.e. the average relative interval length, the percentage of observations bracketed by the confidence interval, the percentage of observations bracketed by the unit confidence interval and the continuous rank probability score (CRPS) were used in this study for sensitivity analysis under model input error via GLUE and Bayesian methods. It was found that (1) the posterior distributions derived by the Bayesian method are narrower and sharper than those obtained by the GLUE under precipitation errors, but the differences are quite small; (2) Bayesian method performs more sensitive in uncertainty estimates of discharge than GLUE according to the impact of precipitation errors; (3) GLUE and Bayesian methods are more sensitive in uncertainty estimate of high flow than the other flows by the impact of precipitation errors; and (4) under the impact of precipitation, the results of CRPS for low and medium flows are quite stable from both GLUE and Bayesian method while it is sensitive for high flow by Bayesian method.     

Zur statistischen Auswertung von mikroskopischen Plankton- und Partikelzählungen

The Poisson distribution is required for the evaluation of counting results. Its availability can be checked with the dispersion index and graphically with a probability sheet. It is shown by an example that with increasing particle density the deviation from the Poisson distribution increases and how these anomalies become visible in the dispersion index and the per-cent error of the count. For the calculation of confidence intervals, a simple approximation formula and the asymptotic behaviour with growing random sample size are represented. The phenomena of over- and under-distribution as well as of “contagion” (Neyman distribution) are mentioned, and the high-speed method of counting according to Tippett and the variance-analytical evaluation methods are referred to.