Distribution and health risk assessment of organochlorine pesticides （OCPs） in industrial site soils： A case study of urban renewal in Beijing, China

A field survey was conducted in a contaminated industrial site of southern Beijing,China to investigate the contents and distribution of the organochlorine pesticides(α-,β-,γ-,δ-HCH,p,p-DDT,p,p-DDE,p,p-DDD and o,p-DDT) in the profiles of soil,and a health risk assessment was carried out with CalTOX multimedia exposure model.Results showed that mean concentrations of total hexachlorocyclohexane isomers(HCHs) and total dichlorodiphenyltrichloroethane isomers(DDXs) in soils were in the range of 13.20-148.71 mg...     

基于替代模型的地下水溶质运移不确定性分析

为分析参数的不确定性对地下水溶质运移数值模型的影响,采用蒙特卡罗(Monte Carlo)模拟对一算例进行分析,并从风险评估的角度对不确定性分析的结果进行了阐释.为减小计算负荷,利用Sobol'法对模型参数进行了灵敏度分析,筛选出较为敏感的参数作为随机变量,建立了模拟模型的克里格(Kriging)替代模型,进而实现Monte Carlo模拟.结果表明:置信度为80%时,井1,2,3浓度值的置信区间分别为23.46~42.06,47.99~66.73,69.54~82.94mg/L;结合风险评估,计算出地下水受污染的风险为0.54,可为地下水污染物防控与修复提供科学依据.     

Social network analysis and valid Markov chain Monte Carlo tests of null models

Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.

Benchmark calculations from summarized data: an example

Benchmark calculations often are made from data extracted from publications. Such data may not be in a form most appropriate for benchmark analysis, and, as a result, suboptimal and/or non-standard benchmark analyses are often applied. This problem can be mitigated in some cases using Monte Carlo computational methods that allow the likelihood of the published data to be calculated while still using an appropriate benchmark dose (BMD) definition. Such an approach is illustrated herein using data from a study of workers exposed to styrene, in which a hybrid BMD calculation is implemented from dose response data reported only as means and standard deviations of ratios of scores on neuropsychological tests from exposed subjects to corresponding scores from matched controls. The likelihood of the data is computed using a combination of analytic and Monte Carlo integration methods.

基于蒙特卡罗模拟的阿特拉津健康风险评价

为评估阿特拉津(ATR)对人体的健康风险,通过文献检索及追溯方式,收集了93篇文献中关于我国环境介质中ATR的检测数据,基于美国环保署健康风险评价方法,并运用蒙特卡罗模拟方法,评价了我国成年男性和女性ATR的健康风险,分析了各参数的敏感性和相关性。结果显示,我国成年男性和女性的非致癌健康风险熵值分别为4.53×10~(-2)和4.30×10~(-2),分别有89.8%的成年男性和89.9%的成年女性风险熵值低于0.10;饮用水中ATR的浓度对其健康风险的贡献(即敏感性)分别为男性88.0%和女性83.3%,与健康风险的关联性(R)分别为男性0.907和女性0.895。我国ATR的非致癌健康风险处于可接受水平,饮用水中ATR对其健康风险的贡献最大。该方法可为有毒有害物质的健康风险预警和精准控制提供方法学参考。     

重金属污染场地土壤风险筛选值关键影响因子研究——以砷为例

合理确定重金属土壤筛选值是污染场地风险识别和调查评估的基础,过松或过严的标准都会增加风险评估的不确定性,甚至可能会导致风险管控措施失效或修复资金浪费,确定重金属土壤筛选值的关键影响因子是合理确定重金属土壤筛选值的前提。选择工业污染场地中检出频率高、毒性大的砷作为研究对象,通过假设工商业用地下的暴露情景,根据《污染场地风险评估技术导则》(HJ25.3—2014)得到砷筛选值计算公式,利用基于蒙特卡罗模拟的Crystal Ball模型计算公式中各参数对结果的敏感性和贡献率来确定关键影响因子,通过调研国内外砷筛选值现状,从计算模型、毒性评估及关键影响因子等方面对引起各国砷筛选值差异的原因进行了分析探讨。结果表明:工商业用地下土壤砷筛选值取值为0.84～175.7 mg·kg~(-1),均值为21.4 mg·kg~(-1),95%的置信上限为24.19 mg·kg~(-1);风险可接受水平、每日土壤摄入量(IR)、暴露频率(EF)和暴露周期(ED)对砷筛选值的贡献率依次为41.3%、-27.3%、-16.3%和-12.7%,其余因子的贡献率均小于1%;关键影响因子按贡献率绝对值从大到小分别为风险可接受水平、每日土壤摄入量(IR)、暴露频率(EF)、暴露周期(ED)。计算模型和毒性评估存在差异是国内外土壤砷筛选值差异的基础原因,当计算模型和毒性评估差异不大时,关键影响因子才是决定性因素,其影响程度与模拟计算的结果一致。建议我国完善筛选值计算模型,对砷的毒性效应和符合我国暴露人群特征的关键影响因子展开深入研究。     

Simulating wildfire patterns using a small-world network model

This paper presents the development and validation results of a weighted small-world network model designed to simulate fire patterns in real heterogeneous landscapes. Fire spread is simulated on a gridded landscape, a mosaic in which each cell represents an area of the land surface. In this model, the interaction between burning and non-burning cells (here, due to flame radiation) may extend well beyond nearest neighbors, and depends on local conditions of wind, topography, and vegetation. An approach based on the coupling of the solid flame model with the Monte Carlo method is used to predict the radiative heat flux from the flame generated by the burning of each combustible cell to its neighbors. The weighting procedure takes into account latency (a combustible cell will only ignite when it has accumulated enough energy along time) and flaming persistence of burning cells. The model is applied to very different fire scenarios: a historical Mediterranean fire that occurred in southeastern France in 2005 and experimental fires conducted in arid savanna fuels in South Africa in 1992. Model results are found to be in agreement with real fire patterns, in terms both of rate of spread, and of the area and shape of the burn. This work also shows that the fractal properties of fire patterns predicted by the model are similar to those observed from satellite images of three other Mediterranean fire scars.     

接管高应变区缺陷的安全评定概率方法研究

随着计算机技术飞速发展,Ｍｏｎｔｅ Ｃａｒｌｏ 法在压力容器可靠性分析中得到广泛应用。但对于小概率情形,利用直接抽样的Ｍｏｎｔｅ Ｃａｒｌｏ 法进行失效概率计算,费用较高。为此,笔者提出了几种新的失效概率近似计算方法,并以压力容器接管为例,对其断裂失效概率进行了计算,同时还和直接抽样的Ｍｏｎｔｅ Ｃａｒｌｏ 法的计算数值进行了比较。结果表明,新的失效概率近似计算方法具有精度高、节省机时的优点。     

CDM项目温室气体减排成本的不确定性分析

以清洁发展机制(CDM)为背景,针对计算温室气体排放量和减排成本中存在的不确定性进行了MonteCarlo模拟研究,分析了不确定性变量是如何影响决策变量的.结论是:各类碳排放因子、碳排放量、碳减排成本、减排收益是相互关联而存在连锁影响的,其中的发电碳排放因子和煤炭开采释放的甲烷排放系数是主要的.这为进一步计算或估计存在的风险,从而为参与CDM谈判决策提供重要的决策参考.     

Spatially explicit sensitivity and uncertainty analysis for multicriteria-based vulnerability assessment

This research analyses the application of spatially explicit sensitivity and uncertainty analysis for GIS (Geographic Information System) multicriteria decision analysis (MCDA) within a multi-dimensional vulnerability assessment regarding flooding in the Salzach river catchment in Austria. The research methodology is based on a spatially explicit sensitivity and uncertainty analysis of GIS-CDA for an assessment of the social, economic, and environmental dimensions of vulnerability. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimise the associated uncertainty within each dimension of the vulnerability assessment. The methodology proposed for achieving this objective is composed of four main steps. The first step is computing criteria weights using the analytic hierarchy process (AHP). In the second step, Monte Carlo simulation is applied to calculate the uncertainties associated with AHP weights. In the third step, the global sensitivity analysis (GSA) is employed in the form of a model-independent method of output variance decomposition, in which the variability of the different vulnerability assessments is apportioned to every criterion weight, generating one first-order (S) and one total effect (ST) sensitivity index map per criterion weight. Finally, in the fourth step, an ordered weighted averaging method is applied to model the final vulnerability maps. The results of this research demonstrate the robustness of spatially explicit GSA for minimising the uncertainty associated with GIS-MCDA models. Based on these results, we conclude that applying the variance-based GSA enables assessment of the importance of each input factor for the results of the GIS-MCDA method, both spatially and statistically, thus allowing us to introduce and recommend GIS-based GSA as a useful methodology for minimising the uncertainty of GIS-MCDA.