象山港海水养殖区贝叶斯网络水质评价及预测

水质评价是水环境保护与管理的重要环节,传统的评价方法在处理评价中的不确定性、大量信息处理等方面存在局限性。贝叶斯网络可以有效地表达和分析不确定性问题,实现定性分析与定量分析的有机结合。以近10a来象山港海水养殖区的水质监测数据为样本数据,采用贝叶斯网络技术,建立反映各水质指标及水质级别之间相互关系和相互影响强度的贝叶斯网络模型。模型结构表明直接影响水质级别的水质指标为氨氮、化学需氧量、硝酸盐、无机磷和叶绿素a,而其他亚硝酸盐、无机氮等4个水质指标与水质级别存在间接的因果关系。对200条监测数据进行模型精度检验,结果表明,其预测精度达94.8%,Kappa指数为0.892,这说明采用贝叶斯网络技术对水质进行评价及预测是可行的。     

Evaluation of nitrate analysis using test strips: Comparison with two analytical laboratory methods

Abstract

Nitrate remains a contaminant of concern for users of well water. Well‐water evaluation, either to assess nitrate contamination or to evaluate sites prior to including them in a larger water quality study, often involves costly laboratory analysis. A cost effective alternative to laboratory analysis are dip‐style test strips. However, the accuracy of these types of products must be reliable, as failure to identify the contaminant may, for example, persuade a homeowner to neglect to have a potential problem further investigated. The testing of nitrate using such strips typically involves dipping the strip into the water sample and reading the color development after a specific period of time. The color development is then compared to a color scale which corresponds to concentration provided with the test. The results of these types of analysis are especially open to interpretation by the evaluator of the results. An experiment was conducted to evaluate test strips in which individuals or “readers” tested water samples collected in the field and nitrate standards prepared in the laboratory with nitrate test strips. The results obtained by the “readers” were compared to analysis of nitrate by high performance liquid chromatography and colorimetric analysis using a colorimetric ion analyzer. There was a good agreement between the “readers” results and the analytical methodologies used. Use of the test strips by non‐technical persons, such as homeowners, could provide an accurate determination of nitrate in well water without the expense involved in a detailed laboratory analysis. The test strips can also be relied upon to accurately determine nitrate concentration when screening wells prior to designing field experiments.     

Limited-Information Modeling of Loggerhead Turtle Population Size

We attempt to estimate the size of a population of female loggerhead turtles. In traditional capture-recapture experiments to estimate the size of an animal population, individual animals are tagged and the information about which individuals are captured repeatedly is crucial. For these loggerhead turtle data, information about individual turtles is not available. Rather, we observe only the counts of successful and failed nestings at a location over a series of days (in our case, three). We view the turtles’ nesting behavior as an alternating renewal process, model it using parametric distributions, and then derive probability distributions that describe the behavior of the turtles during the three days via a 3-way contingency table. We adopt a Bayesian approach, formulating our model in terms of parameters about which strong prior information is available. We use a Gibbs sampling algorithm to sample from the posterior distribution of our random quantities, the most crucial of which is the number of turtles remaining offshore during the entire sampling period. We illustrate the method using data sets from loggerhead turtle sites along the South Carolina coast. We provide a simulation study which illustrates the quality and robustness of the method and investigates sensitivity to prior parameter specification.     

Spatial prediction of categorical variables with the Bayesian Maximum Entropy approach: the Ooypolder case study

Categorical variables such as water table status are often predicted using the indicator kriging (IK) formalism. However, this method is known to suffer from important limitations that are most frequently solved by ad hoc solutions and approximations. Recently, the Bayesian Maximum Entropy (BME) approach has proved its ability to predict categorical variables efficiently and in a flexible way. In this paper, we apply this approach to the Ooypolder data set for the prediction of the water table classes from a sample data set. BME is compared with IK using global as well as local criteria. The inconsistencies of the IK predictor are emphasized and it is shown how BME permits avoiding them.     

Ecological standards for eutrophication and desiccation: Perspectives for a risk assessment

This paper describes an assessment of ecological standards for eutrophication and desiccation using the ecotoxicological approach of standard setting. In ecotoxicological risk assessment single-species toxicity data, such as No Observed Effect Concentration (NOEC), are used to extrapolate maximum tolerable concentrations (MTC) which protect 95% of the ecosystem's species. In our approach single-species data consist of the species-response function for both nutrient availability and soil moisture. These data were used to calculate the maximum levels at which 95% of the plant species are protected. A case study, using data on 275 plant species of usable grasslands in the province of South Holland, has been carried out. Ecological standards are calculated for maximum nitrogen load and changes in groundwater level. The influence of nitrogen limitation and moisture shortage on the 95% species protection level is considered. Differences in methodology between our approach and the assessment of ecological standards for micro-pollutants and critical loads are evaluated.     

Testing for trends when there are changes in methods

Data from monitoring projects often include sampling or analytical changes that preclude trend analysis on the entire period of record. A modification of the nonparametric Kendall's test for monotonic trends, which accounts for such changes in the period of record, is described here. This approach blocks the data so that only data collected or analyzed under similar circumstances are compared. Alternatively, when appropriate data exist, data collected using the old method may be calibrated to values expected from the new method. Traditional trend tests may then be applied to resulting data sets. Results from simulations assessing both the power of the blocked test and the standard test performed on calibrated data are presented. The power of the blocked test exceeded the power of the calibration approach only when the calibration error was extremely large. Both the blocking and calibration approaches were applied to and compared for chemical data from Vermont lakes.     

A Bayesian Approach to Estimate OJD Prevalence From Pooled Fecal Samples of Variable Pool Size

This paper describes a Bayesian approach to prevalence estimation based on pooled samples that accommodates variation in pool size and adjusts for test imperfection. A logistic model was developed for pooled fecal culture (PFC) sensitivity as a function of pool size and a logistic mixed model for ovine Johne’s disease (OJD) prevalence as a function of covariates that were found significant in a recent OJD risk factor study conducted in Australia. Available data on these factors and prior information about prevalence and sensitivity were incorporated into a Bayesian model to estimate OJD prevalence from PFC data. Overall, posterior cohort OJD prevalence was estimated to be 0.16 (range of prevalences across cohorts 0.002 to 0.72). The average prevalence was higher in wethers than ewes. PFC sensitivities for pool sizes 10, 30 and 50 were estimated to be 0.91 (95% probability intervals 0.80, 0.96), 0.85 (0.80, 0.90) and 0.77 (0.65, 0.88), respectively. Posterior specificity of PFC was almost perfect though based primarily on the prior. Results suggest the Bayesian model successfully estimated the animal-level prevalence after accounting for variable pool size and imperfect test parameters. The method can be easily adapted for other conditions and diseases where pooled samples are collected. WinBugs code for the article is available online.     

Determining the infection status of a herd

This article presents hierarchical models for determining infection status and prevalence of infection within a herd given a hypergeometric or binomial sample of animals that have been screened with an imperfect test. Expert prior information on the infection status of the herd, diagnostic test accuracy, and herd prevalence is incorporated into the model. Posterior probabilities versus prior probabilities of infection are presented in the novel form of a curve, summarizing the probability of infection over a range of possible prior probability values. We demonstrate the model with serologic data for Mycobacterium paratuberculosis (Johne’s disease) in dairy herds.     

A soil fauna index for assessing soil quality

The characteristics of the faunal community in soil are closely related to soil quality. Using the soil faunal community as an indicator of soil quality is not cost-effective because of the complex taxonomic distinction and identification required, moreover; the power of the assessment is weak in relation to soil function. Recently, a functional method incorporating eco-morphological traits has been proposed, but it depends upon presence/absence data of soil arthropod community exclusively. To overcome the limitation, we designed a novel index using the diversity of the soil faunal community along with its functional traits, and the abundance of its members (Abundance-based Fauna Index, referred as FAI). The FAI method supports both the presence/absence data and its abundance. Using real and simulated data sets with eco-morphological and niche breadth traits, two taxonomic independent functional traits, we tested associations of FAI values with soil quality and found a good relationship. In addition to its usefulness for measuring soil quality, FAI may provide valuable information for linking functional traits of soil fauna and the belowground environmental condition.     

抽水蓄能电站建设期水土流失及其次生灾害风险评价

[目的]对抽水蓄能电站建设期水土流失及其次生灾害风险进行评价,对涉及的要素特征进行筛选分析,为提高工程建设期绿色安全施工水平提供科学支持。[方法]采用层次分析法结合模糊综合评价法,从3个维度共选取了8个一级指标和34个二级指标进行分析评价。[结果]构建了抽水蓄能电站建设期水土流失及其次生灾害风险评价模型,并划分为5个风险等级,将模型应用于安徽省绩溪抽水蓄能电站的研究表明该电站在建设期水土流失风险灾害等级为3级,属一般安全等级。结合该工程建设期的实情,验证了模型的可行性。[结论]抽水蓄能电站选址在很大程度上决定着工程建设期水土流失及其次生灾害风险等级。采用该方法评价风险等级为3级及以下的工程,可在确保工程水土保持措施按照要求落实建设的基础上,增强建设期水土保持监测水平。应重点关注建设期弃渣堆置不合规及取弃土过程中的潜在土壤流失量,防微杜渐,避免水土流失及其次生灾害现象的发生。     

土地整治中底泥质耕作层土壤的构建方法及应用效果

开展底泥质耕作层土壤的构建方法研究,是确保土地整治工程质量和效益的重要举措。该研究以河流疏浚底泥土地利用为主线,从底泥质耕作层土壤构建方法的基础理论、底泥环境肥力调查评价、底泥重金属稳定化修复、底泥改造土壤工程及底泥应用案例等方面系统地探讨了土地整治工程中底泥质耕作层土壤构建方法的基础、内容、设计方法、施工工艺、工程参数。污染底泥修复后大田种植空心菜试验表明:重金属含量可达到无公害蔬菜安全标准、Cd降低率在38.61%~85.69%,稳定化修复明显降低蔬菜重金属含量;底泥质耕层土壤适宜开展种植,可提蔬菜高产量8.7%~13%,增加土壤有机质含量0.95~2.18倍。     

A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model

Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151–5165, 2014) provide direct inference on overall exposure effects. Unlike standard zero-inflated models, marginalized models specify a regression model component for the marginal mean in addition to a component for the probability of an excess zero. This study proposes a score test for testing a marginalized zero-inflated Poisson model against a marginalized zero-inflated negative binomial model for model selection based on an assessment of over-dispersion. The sampling distribution and empirical power of the proposed score test are investigated via a Monte Carlo simulation study, and the procedure is illustrated with data from a horticultural experiment. Supplementary materials accompanying this paper appear on-line.     

Estimating Uncertainty in the Revised Universal Soil Loss Equation via Bayesian Melding

Deterministic simulation models are used to understand environmental processes and guide policy development by decision makers. In order to make informed decisions, uncertainty about input and output of these models needs to be incorporated into the modeling. We use a method known as Bayesian melding to quantify the uncertainty in the Revised Universal Soil Loss Equation (RUSLE), an important component of water quality models. This technique allows for this uncertainty through prior distributions on both the input parameters and the outcomes of interest. There have been relatively few applications of this methodology to complex problems and none to date in soil loss modelling. Moreover, land based spatial data, which are now commonly available in environmental research as well as many other disciplines, have not previously been used to inform Bayesian melding. The results demonstrate that the slope steepness factor of the RUSLE is the main contributor to total uncertainty. We conclude that Bayesian melding provides a good method for exploring the sources of uncertainty in a deterministic model.     

A hierarchical model for compositional data analysis

This article introduces a hierarchical model for compositional analysis. Our approach models both source and mixture data simultaneously, and accounts for several different types of variation: these include measurement error on both the mixture and source data; variability in the sample from the source distributions; and variability in the mixing proportions themselves, generally of main interest. The method is an improvement on some existing methods in that estimates of mixing proportions (including their interval estimates) are sure to lie in the range [0, 1]; in addition, it is shown that our model can help in situations where identification of appropriate source data is difficult, especially when we extend our model to include a covariate. We first study the likelihood surface of a base model for a simple example, and then include prior distributions to create a Bayesian model that allows analysis of more complex situations via Markov chain Monte Carlo sampling from the likelihood. Application of the model is illustrated with two examples using real data: one concerning chemical markers in plants, and another on water chemistry.     

Statistical Assessment of the Surface Water Quality Monitoring Network in Saskatchewan

Surface water quality monitoring is one of the responsibilities of a number of provincial and federal environmental departments in Canada. In Saskatchewan, the Ministry of Environment is responsible for the province water quality monitoring network. The sampling effort was initiated 40 years ago and has been ongoing since, with varying degrees of spatial and temporal coverage. The main objective of the Saskatchewan monitoring network is the assessment of ambient water quality status. In addition, one of the main uses of the generated water quality data is the calculation of a Water Quality Index. The adequacy of the monitoring network to perform these tasks needs to be validated. The objective of this study is to provide a statistical assessment of two of the monitoring network main aspects, the water quality variables and their sampling frequency. A new rationalization approach is applied for the assessment and reselection of water quality variables. The proposed approach provides, in a systematic way, the optimal combinations of variables to continue measuring, variables that may be redundant and could be considered for discontinuance, and variables that may need to be added to the list of variables being measured. The confidence interval around the mean is used as the main criterion for the sampling frequency assessment. A design chart is provided for the sampling frequency assessment, which is easy to use, and provides an initial assessment of the number of samples required to provide a mean value with a predefined error percentage.     

Satellite remote sensing for water erosion assessment: A review

Water erosion creates negative impacts on agricultural production, infrastructure, and water quality across the world. Regional-scale water erosion assessment is important, but limited by data availability and quality. Satellite remote sensing can contribute through providing spatial data to such assessments. During the past 30 years many studies have been published that did this to a greater or lesser extent. The objective of this paper is to review methodologies applied for water erosion assessment using satellite remote sensing. First, studies on erosion detection are treated. This comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes. Second, the assessment of erosion controlling factors is evaluated. Four types of factors are discussed: topography, soil properties, vegetation cover, and management practices. Then, erosion mapping techniques are described that integrate products derived from satellite remote sensing with additional data sources. These techniques include erosion models and qualitative methods. Finally, validation methods used to assess the accuracy of maps produced with satellite data are discussed. It is concluded that a general lack of validation data is a main concern. Validation is of utmost importance to achieve regional operational monitoring systems, and close collaboration between the remote sensing community and field-based erosion scientists is therefore required.     

贝叶斯最大熵地统计方法研究与应用进展

以克里格估算为基础的插值和随机模拟为代表的经典地统计方法是目前研究地理属性空间分布的主要方法,但仍存在精度不高及不能有效利用其他有价值信息的缺陷。近年来贝叶斯最大熵地统计方法在国外逐渐流行,该方法能够在有效利用多源数据的基础上,提高空间分布研究精度,是一种新的非线性方法。本文详细阐述了贝叶斯最大熵方法的数据内容、实施步骤、一般算法及计算结果,并介绍了该方法的应用情况,最后对该方法的优点和不足作出了评价。     

Investigating translated data of the German soil‐quality assessment using pinpoint field validation

In contrast to modern soil‐profile characterization, alternative soil classifications, such as the German soil‐quality assessment (Bodenschätzung), bear a lower degree of scientific quality. However, despite originally created to determine the tax value of arable land and grassland, its high spatial resolution and complete areal coverage makes soil‐quality assessment a valuable tool. To assess its performance in a mountainous setting soil‐layer data of 60 soil pits, recorded in Bavaria (SE Germany) in the course of the soil‐quality assessment, were translated into German soil‐science terminology using the translation program NIBIS®. With regard to soil type and texture the translation was checked using pinpoint field validation based on soil‐science terminology. 57% of soil types and 61% of texture were correctly translated by NIBIS®. To obtain information about probable parameters that can explain the different results readily available parameters such as elapsed time between soil‐quality assessment and validation, altitude, slope, aspect, horizon thickness, lower edge of horizon, as well as weathering surface and silicate‐weathering rate derived from geological maps were used. Differences in topsoil texture were somewhat related to petrographic parameters, those of the lower subsoil showed a weak dependence to topographic parameters. The NIBIS® translation overrated the silt content to the expense of sand. Clay was the best‐matched texture class. The shift towards silty texture classes was the dominant factor for the differences of texture‐related values of the available water capacity and hydraulic conductivity. Both parameters as derived from the NIBIS® translation on the one and from field validation on the other hand were used to evaluate the water‐retention capacity of individual soil profiles. Despite differing input data the soils' water‐retention capacity was rated identical. Thus, a certain degree of disagreement between the texture data obtained from NIBIS® translation and from field validation is tolerable, if the eventual soil‐function evaluation is based on wide classes of texture or of secondary parameters derived from texture.     

Assessment of Water Quality in Association with Land use in the Tillamook Bay Watershed, Oregon, USA

The water quality in tributaries to Tillamook Bay, Oregon, frequently exceeds standards for fecal coliform bacteria (FCB) and temperature. FCB inputs to the bay have forced periodic closure of the oyster shellfish industry. In addition, impaired water quality may be contributing to reduced salmonid populations in the bay and its tributaries through reduction in the quality of the habitat. Because of these concerns, the Tillamook Bay National Estuary Project (TBNEP) conducted several characterization studies and a long-term water quality monitoring program for the tributary rivers. This paper summarizes data collected to date within these efforts, including storm-based data on FCB and total suspended solids (TSS), and bimonthly data on nutrient concentrations in selected rivers. Monitoring data from 1996 to 2002 are summarized. Results for FCB and TSS are reported by storm, which are classified according to season and precipitation patterns in order to minimize intra-annual variability. There are not indications of large changes in water quality throughout the period of record, but it is too early in the program for trends analysis. Storms that exhibited the highest FCB concentrations tended to be those that occurred during fall and/or those that were preceded by relatively dry conditions and included high rainfall intensity. Implementation of storm-based monitoring and classification of storms according to season effectively reduces the large variability inherent in the FCB monitoring data, thereby facilitating future trends analysis. Continued storm-based monitoring of FCB and TSS, and also continued collection of rainfall and river discharge data, will provide the database that will be needed to determine to what extent on-the-ground remediation actions and best management practices (BMPs) within the Tillamook Basin are having their desired effects.     

Silicate Minerals as a Source of Arsenic Contamination in Groundwater

The dynamic behavior of water quality and quantity in the Egyptian drains is often viewed as a disruption to the normal operation and performance of the process of water reuse in irrigation. The control of such behavior has been challenging and often elusive in practice. Therefore, this paper presents a framework to advance the understanding and opportunities for improving the reuse process by developing a multivariate process control model. The model starts with preliminary analysis for water quality data that are collected at the reuse site on the examined drain. This phase comprises investigating data distribution and dependency. Then, univariate control charts are used to investigate the state of control for the independent and normally distributed variables. For dependent variables, principal components analysis is used as a method of synthesizing the variables information. In this case, principal component scores are displayed using multivariate control charts. If in-control case existed, process capability index is used to provide a numerical measure of whether or not the reuse process is capable of producing water that satisfies the irrigation quality standards. Since the model will only detect assignable causes if out-of-control or in-capable case existed, management, operational, and/or engineering action will usually be necessary to sustain the reuse process. In these cases, an action plan in response to the model signals will be vital. The main function of the proposed model is to safely manage the reuse practice using statistical quality control techniques. The model was demonstrated using water quality data collected during the period from January 2006 to July 2011 from Hanut (EH02) and El-Salam 3 (ESL03) pump stations along Hadus drain, Eastern Nile Delta-Egypt. The recommended model is automatic, algorithmic, self-tuning, and computerizable.