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51.
以剑麻纤维为原料,通过简易的一步炭化活化法制备了一系列多孔炭材料,分别探究了3种温和的金属盐活化剂、活化剂与剑麻纤维的质量比和活化温度对炭材料的氯苯吸附量的影响,并通过BET、SEM、XRD、Raman、FT-IR、元素分析等手段表征其物理化学性质.结果表明,当采用CuCl2为活化剂、CuCl2与剑麻纤维的质量比为10∶1及活化温度为800℃时,制备得到的剑麻纤维基多孔炭(PCC)吸附性能最佳,其在氯苯浓度为1560 mg·m-3时,吸附量达到856 mg·g-1,而未经CuCl2活化的炭材料(PC)的氯苯吸附量仅为15 mg·g-1.氯苯吸附性能的提升主要归因于比表面积、孔容、无序性和表面含氧官能团的增加.此外,采用巨正则系综蒙特卡洛(GCMC)方法模拟氯苯分子在制备的多孔炭材料中的吸附行为,结果表明,该材料中孔径为0.5 nm的孔对氯苯分子的吸附能力最强,且对氯苯吸附起主导作用的为苯环中心和Cl原子与炭材料上连接含氧官能团的H原子之间的静电作用力. 相似文献
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53.
土壤理化性质对污染场地环境风险不确定性的影响 总被引:3,自引:1,他引:2
以某化工污染场地中挥发性有机物苯的环境风险评价为例,研究了土壤有机质含量、土壤含水率、土壤容重等理化性质对风险评价结果的影响.在土壤污染物苯浓度不变的前提下,土壤有机质含量、土壤含水率和土壤容重在场地条件下随机取值的变异范围分别为0.31%~2.31%、0.12~0.25和1.25~1.75 g/cm3.10 000次蒙特卡罗模拟结果表明,在95%置信水平下,苯的总致癌风险(概率值)在1.45×10-5~2.74×10-5之间.在该场地条件下,土壤有机质含量是影响苯风险不确定性的最主要因素,其对风险评价结果不确定性的贡献率高达90.2%;土壤含水率和土壤容重的贡献率分别为5.6%和4.2%.因此,在土壤污染健康风险评价过程中,应对理化参数进行敏感性分析,对评价结果影响较大的关键参数取值需慎重. 相似文献
54.
Jiping Jiang Feng Han Yi Zheng Nannan Wang Yixing Yuan 《Frontiers of Environmental Science & Engineering》2018,12(5):6
Identifying source information after river chemical spill occurrences is critical for emergency responses. However, the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated. To fill this gap, stochastic analysis approaches, including a regional sensitivity analysis method, identifiability plot and perturbation methods, were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework. Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules. For example, the release load can be most easily inverted, and the source location is responsible for the largest uncertainty among the source parameters. The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty. The differences among the different objective functions are smaller for instantaneous release than for continuous release cases. Small monitoring errors affect the inversion results only slightly, which can be ignored in practice. Interestingly, the estimated values of the release location and time negatively deviate from the real values, and the extent is positively correlated with the relative size of the mixing zone to the objective river reach. These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.
相似文献
55.
This paper proposes an Integrated Monte Carlo Methodology (IMCM) to solve the parameter estimation problem in water quality models. The methodology is based on Bayesian approach and Markov Chain Monte Carlo techniques and it operates by means of four modules: Markov Chain Monte Carlo (MCMC), Moving Feasible Ranges (MFR), Statistical Analysis of the Joint Posterior Distribution (SAD) and Uncertainty Propagation Analysis (UPA). The main innovation of the new proposal lies in the combination of MCMC and MFR modules which provides the joint posterior distribution of the calibrated parameters following the classical Bayesian approach. While MCMC module, based on Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm, is specially designed to sample complex joint posterior shapes within certain parameter ranges, the MFR readjusts these ranges until the coverage of the feasible parameter space is guaranteed. Once the joint posterior distribution is properly defined, the SAD provides the parameter statistics and the UPA performs an analysis of the uncertainty propagation through the model. The possibilities of the new proposal have been tested on the basis of a simple model featuring different activated sludge batch experiments. IMCM has been implemented in Matlab and it is prepared to be easily connected to any software package. 相似文献
56.
Complex marine ecosystems contain multiple feedback cycles that can cause unexpected responses to perturbations. To better predict these responses, complicated models are increasingly being developed to enable the study of feedback cycles. However, the sparseness of ecological data often limits the direct empirical parameterization of all model parameters. Here we use a Bayesian inverse analysis approach to synthesize empirical data and ecological theory derived from published studies of a coral atoll's enclosed pelagic ecosystem (Takapoto Atoll, French Polynesia). We then use the estimates of flux magnitudes to parameterize probabilistic compartment models with two forms of heterotrophic consumption: (1) “bottom-up” donor-controlled heterotrophic consumption and (2) “top-down” mass-action heterotrophic consumption. We explore how the flux magnitudes affect the ecosystem's stability properties of resilience, reactivity, and resistance under both assumptions for heterotrophic consumption. The models suggest that the microbial uptake of dissolved organic carbon (DOC) regulates the long term rate of return to steady state following a temporary or pulse perturbation (resilience), and the cycling of carbon between abiotic pools and heterotrophic compartments regulates the short-term response (reactivity). In the bottom-up process model, the sensitivity of steady state masses following a sustained or press perturbation (resistance) is highest for the DOC pool following a sustained change to the microbial uptake rate of DOC. Further, a change in the microbial uptake of DOC propagates through the ecosystem and affects the steady state values of zooplankton. The analysis suggests that the food web is highly dependent on the recycling between the abiotic and biotic carbon pools, particularly as mediated by the microbial consumption of DOC, and this recycling determines how the ecosystem responds to perturbations. 相似文献
57.
Individual-based models (IBMs) have been improved in quality and reliability in recent years with an approach called pattern-oriented modelling (POM). POM proposes guidelines to develop models reproducing multiple patterns observed on the field and to test systematically how well the IBMs reproduce them. POM studies used generally traditional methods of goodness of fit such as the sum of squares evaluation or ad hoc comparisons of fitting errors and variations. Model selection, however, can be a rigorous statistical approach based on information theory and information criteria such as the Akaike's information criterion (AIC) or the deviance information criterion (DIC). So far, it has not been tried to link POM to these rigorous techniques. The main problems to achieve that are: (a) the difficulty to have likelihood functions for IBMs’ parameters and (b) the possibility to obtain posterior distributions of IBMs’ parameters given the patterns to reproduce. In a first part, this paper answers problem (a) by proposing and explaining how to calculate a deviance measure (POMDEV) for models developed in a context of POM. And while answering the second problem, a second part of the paper proposes an information criterion for model selection in a POM context (the pattern-oriented modelling information criterion: POMIC). This criterion does not yet have the same theoretical foundation as, e.g., AIC, but uses formal analogies to the DIC. In a third part POMIC is tested with a modelling exercise. This exercise shows the potential of POMIC to use multiple patterns for selecting among multiple potential submodels and eventually select the most parsimonious and well fitting model version. We conclude that POMIC, although being a heuristically derived approach, can greatly improve the POM framework. 相似文献
58.
Andrew O. Finley Sudipto Banerjee Ronald E. McRoberts 《Environmental and Ecological Statistics》2008,15(2):241-258
In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply,
there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small
area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based
approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability
of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s
posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The
paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot
data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries
of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest
and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification
accuracy, 88%, for the trial site.
相似文献
Ronald E. McRobertsEmail: |
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化工园区中危险源众多,一旦发生事故很容易在整个园区内蔓延和发展.针对化工园区内储罐密集,容易引发连锁反应导致事故扩大的特点,利用FDS软件对储罐火灾场景进行数值模拟,根据储罐所受热辐射确定化工园区内储罐火灾最可能的事故发生序列,并引入基于设备失效前时间的机械设备故障概率模型对罐区内单个储罐的火灾风险进行研究,得到储罐区... 相似文献