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91.
This paper describes a statistical modelling framework for the characterisation of rainfall extremes over a region of interest. Using a Bayesian hierarchical approach, the data are assumed to follow the generalised extreme value distribution, whose parameters are modelled as spatial Gaussian processes in the latent process layer. We also integrate a parametric relationship between precipitation maxima accumulated over increasing durations. The inference of the model parameters is thus improved by pooling information across both space and accumulation duration. In addition, we propose and investigate two different approaches for the integration of daily and sub‐daily rainfall data within the framework. We also demonstrate how information from a regional climate model can be integrated to enable the investigation of future projections of extreme rainfall characteristics. We apply the proposed methodology to precipitation datasets from two large‐scale study regions located on the east coast of Australia. The models are fitted using Markov chain Monte Carlo simulations, and we present estimated model parameters and posterior inferences of return levels at various durations and sites of interest. We demonstrate the effectiveness of the framework in spatially extrapolating the inference to locations other than those at which direct rainfall measurements are available. We also provide comparisons between rainfall extremes at various durations obtained for the current climate and those based on future projections from a regional climate model. Both methods proposed for the integration of daily and sub‐daily records were found to yield similar results in terms of model performance and computational requirements. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
92.
In environmental cancer risk assessment of a toxic chemical, the main focus is in understanding induced target organ toxicity that may in turn lead to carcinogenicity. Mathematical models based on systems of ordinary differential equations with biologically relevant parameters are tenable methods for describing the disposition of chemicals in target organs. In evaluation of a toxic chemical, dose–response assessment often addresses only toxicodynamics (TD) of the chemical, while its toxicokinetics (TK) do not enter into consideration. The primary objective of this research is to integrate both TK and TD in evaluation of toxic chemicals while performing dose–response assessment. Population models, with hierarchical setup and nonlinear predictors, for TK concentration and TD effect measures are considered. A one‐compartment model with biologically relevant parameters, such as organ volume, uptake rate and excretion rate, or clearance, is used to derive the TK predictor while a two parameter Emax model is used as a predictor for TD measures. Inference of the model parameters with nonnegative and assay's Limit of Detection (LOD) constraints was carried out by Bayesian approaches using Markov Chain Monte Carlo (MCMC) techniques. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
93.
Rajarshi Guhaniyogi Andrew O. Finley Sudipto Banerjee Alan E. Gelfand 《Environmetrics》2011,22(8):997-1007
Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low‐rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower‐dimensional subspace. This subspace is determined by a judicious choice of ‘knots’ or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial‐temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
94.
Petra M. Kuhnert Tara G. Martin Kerrie Mengersen Hugh P. Possingham 《Environmetrics》2005,16(7):717-747
Many studies on birds focus on the collection of data through an experimental design, suitable for investigation in a classical analysis of variance (ANOVA) framework. Although many findings are confirmed by one or more experts, expert information is rarely used in conjunction with the survey data to enhance the explanatory and predictive power of the model. We explore this neglected aspect of ecological modelling through a study on Australian woodland birds, focusing on the potential impact of different intensities of commercial cattle grazing on bird density in woodland habitat. We examine a number of Bayesian hierarchical random effects models, which cater for overdispersion and a high frequency of zeros in the data using WinBUGS and explore the variation between and within different grazing regimes and species. The impact and value of expert information is investigated through the inclusion of priors that reflect the experience of 20 experts in the field of bird responses to disturbance. Results indicate that expert information moderates the survey data, especially in situations where there are little or no data. When experts agreed, credible intervals for predictions were tightened considerably. When experts failed to agree, results were similar to those evaluated in the absence of expert information. Overall, we found that without expert opinion our knowledge was quite weak. The fact that the survey data is quite consistent, in general, with expert opinion shows that we do know something about birds and grazing and we could learn a lot faster if we used this approach more in ecology, where data are scarce. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
95.
Global warming and the greenhouse effect are two of the most important environmental problems. Carbon dioxide, methane, and nitrous oxide emissions are the main greenhouse gas emissions in wastewater treatment plants. In this study, the greenhouse gas emission sources in a wastewater treatment plant were determined. Direct (from fossil fuel combustion, methane emissions, and process emissions of the other greenhouse gases) and indirect emissions (primarily from electricity use) in the plant were monitored. The optimum influent characteristics and operating conditions have been defined by using Monte Carlo simulation to minimize the emissions. The results revealed that the highest direct greenhouse gas emission was observed in August with the value of 23.328 kg CO2‐eq d–1 and the lowest emission was 7.56 kg CO2‐eq d–1 measured in January. The aeration tank is a major source of greenhouse gas emissions. Indirect emission has occurred because of the anaerobic digester but the biogas has been cogenerated in the plant, so it has been ignored for the calculation. According to the simulation study, if the plant is operated under optimum operating conditions, it can emit the lowest amount of greenhouse gas emissions. The optimum removal values required for the minimum greenhouse gas emissions are 79% for chemical oxygen demand, 75% for biochemical oxygen demand, and 82% for total suspended solid. The optimum operating conditions for the aeration tank, which is the major source of emission, are 5.33 h of hydraulic retention time, 0.215 d of solid retention time, and 0.999 for food/microorganisms. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd. 相似文献
96.
Environmental integrated assessments are often carried out via the aggregation of a set of environmental indicators. Aggregated
indices derived from the same data set can differ substantially depending upon how the indicators are weighted and aggregated,
which is often a subjective matter. This article presents a method of generating aggregated environmental indices in an objective
manner via Monte Carlo simulation. Rankings derived from the aggregated indices within and between three Monte Carlo simulations
were used to evaluate the overall environmental condition of the study area. Other insights, such as the distribution of good
or bad values of indicators at a watershed and/or a subregion, were observed in the study. 相似文献
97.
传统的健康风险评价主要是进行单值点计算的确定性评价,为了解决再生水回用对人体健康风险影响的不确定性,利用Logistic混沌系统方法来产生可靠、简单、高效的随机数,进而改进传统蒙特卡罗方法中利用线性同余产生随机数的方法,以提高随机数输入的可靠性程度,最终提出了一种基于改进蒙特卡罗算法的再生水回用健康风险评价方法.最后,以西安市某污水处理厂再生水中的风险指标(As、Cd、Cr、Mn、Pb和Zn)为研究对象进行蒙特卡罗模拟计算.结果表明,各指标满足终生可接受风险水平的概率分别为97.25%、93.48%、97.86%、98.69%、97.13%和96.38%,影响各指标进入人体的单位体重日均暴露剂量和终生风险水平的主要因素为污染物浓度和胃肠吸收因子.研究结果可为再生水回用的不确定性健康风险评价提供一定的依据. 相似文献
98.
Extreme weather affects the timing and intensity of infectious outbreaks, the resurgence and redistribution of infections, and it causes disturbances in human‐environment interactions. Environmental stressors with high thermoregulatory demands require susceptible populations to undergo physiological adaptive processes potentially compromising immune function and increasing susceptibility to infection. In assessing associations between environmental exposures and infectious diseases, failure to account for a latent period between time of exposure and time of disease manifestation may lead to severe underestimation of the effects. In a population, health effects of an episode of exposure are distributed over a range of time lags. To consider such time‐distributed lags is a challenging task given that the length of a latent period varies from hours to months and depends on the type of pathogen, individual susceptibility to the pathogen, dose of exposure, route of transmission, and many other factors. The two main objectives of this communication are to introduce an approach to modeling time‐distributed effect of exposures to infection cases and to demonstrate this approach in an analysis of the association between high ambient temperature and daily incidence of enterically transmitted infections. The study is supplemented with extensive simulations to examine model sensitivity to response magnitude, exposure frequency, and extent of latent period. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
99.
100.
In this paper we examine the use of data augmentation techniques for simplifying iterative simulation in the context of both
Bayesian and classical statistical inference for survival rate estimation. We examine two distinct model families common in
population ecology to illustrate our ideas, ring-recovery models and capture–recapture models, and we present the computational
advantage of this approach. We discuss also the fact that problems associated with identifiability in the classical framework
can be overcome using data augmentation, but highlight the dangers in doing so under both inferential paradigms.
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