应用铅锶同位素示踪研究泉州某林地垂直剖面土壤中重金属污染及来源解析

分别用原子吸收光谱仪(AAS)和电感耦合等离子体质谱仪(ICP-MS)分析了泉州市某林地垂直剖面土壤中8种重金属元素(Sr、Ni、Fe、Cr、Cu、Mn、Pb、Zn)的含量及垂直剖面土壤的铅锶同位素组成,并采用BCR四步提取法对重金属形态进行了分析.重金属总量及形态分析结果表明,泉州市林地土壤重金属污染较轻,主要污染因子为Sr.Pb的非残渣态含量最高,活性最大.内梅罗综合污染指数的评价结果表明,Sr在0~60 cm深度处达到重度污染.次生相与原生相比值法评价结果表明,Pb活性最强,对土壤的潜在危害最大;富集因子计算结果表明,Pb、Sr、Mn、Zn受到外源的影响;因子分析结果表明,重金属主要受到交通源、自然源和农业生产的影响.根据垂直剖面土壤样品与泉州市潜在污染源在~(206)Pb/~(207)Pb-~(207)Pb/~(204)Pb图中的分布特征,Pb主要来源于汽车尾气尘和土壤母质层,利用铅同位素二元混合模型计算出土壤母质层对垂直剖面土壤中铅的贡献率为85.14%(62.53%~98.36%),汽车尾气尘的贡献率平均值为14.86%(1.640%~37.47%).锶同位素示踪结果表明,锶主要来源于汽车尾气尘和土壤母质.铅锶同位素联合示踪结果与前述研究一致.     

基于Monte Carlo模拟法对水源水体中微囊藻毒素的健康风险评估

调查水源水体中微囊藻毒素MCs(MC-RR、MC-LR和MC-YR)的污染情况,结合调查情况应用蒙特卡洛法(Monte Carlo)模拟量化人群通过饮水途径摄入微囊藻毒素的风险.在珠江西航道沿线设置5个采样点,在2016年1~6月期间共采集90份水样,根据国标(GB/T 20466-2006)推荐的HPLC方法检测水体中的微囊藻毒素,运用专业风险评估软件@Risk7.0,构建非参数概率评估模型,对通过饮水途径摄入微囊藻毒素(暴露)风险进行概率评估.对随机采集90份水源水体中微囊藻毒素MCs质量浓度检测值进行分布拟合,并运用Chi-Squared、Anderson-Darling、Kolmogorov-Smirnov这3种统计方法进行拟合度检验,根据3种评估拟合结果,确定最佳拟合分布模型.结果表明,在检测的90个水样品中,MC-RR的检出率最高,达到51.11%,质量浓度范围为0.001 7~0.386 3μg·L~(-1);其次为MC-LR和MC-YR,检出率分别是47.78%和21.11%,质量浓度范围分别是0.028 5~0.279 6μg·L~(-1)和0.003 0~0.136 2μg·L~(-1),水源水体中3种微囊藻毒素以MC-RR为主,最大检出质量浓度为0.386 3μg·L~(-1),MC-YR的含量最低.采用软件@Risk7.0分布拟合结果显示,MC-LR质量浓度最适的拟合分布为Ext Value Min模型(0.113 91,0.098 462),MC-RR质量浓度最适的拟合分布为Logistic(0.058 064,0.053 044).健康风险评估表明,MC-LR对人体健康危害的风险高于MC-RR的风险,儿童比成人更易于受到MCs污染的威胁.MC-LR对儿童健康危害的致癌年风险数值大于美国环保署(USEPA)推荐的最大可接受风险水平1×10-4;MC-LR对成人的致癌暴露年风险数值大于国际辐射防护委员会(ICRP)推荐的最大可接受风险水平5×10-5,表明水源水体中的MCs对人体健康存在潜在的危害,有必要加强饮用水源水体的保护与监控,为有效控制水源地水质污染和更好地保障人民健康奠定基础.     

某储罐区火灾多米诺效应场景推演

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...     

基于蒙特卡洛模拟的多米诺事故风险量化管理*

为降低危化品相关的化工事故造成的人员伤亡和财产损失,以化工多米诺事故为研究对象,探讨由初始事故引发1个或多个次生事故的连锁反应机理与风险评估方法。提出应用蒙特卡洛模拟对多米诺事故风险进行动态量化的方法,梳理化工多米诺事故风险的识别、分析、评定、处理全周期管理流程,并以1个天然气压气站为案例,验证基于蒙卡模拟的化工多米诺事故风险量化方法的有效性。结果表明:该方法可以更准确地对化工多米诺事故风险进行定量评估。多米诺事故风险全周期管理流程的梳理能够有效指导化工企业开展安全管理、事故预防等工作。     

Dams in the Amazon: Belo Monte and Brazil’s Hydroelectric Development of the Xingu River Basin

Hydroelectric dams represent major investments and major sources of environmental and social impacts. Powerful forces surround the decision-making process on public investments in the various options for the generation and conservation of electricity. Brazil’s proposed Belo Monte Dam (formerly Kararaô) and its upstream counterpart, the Altamira Dam (better known by its former name of Babaquara) are at the center of controversies on the decision-making process for major infrastructure projects in Amazonia. The Belo Monte Dam by itself would have a small reservoir area (440 km²) and large installed capacity (11, 181.3 MW), but the Altamira/Babaquara Dam that would regulate the flow of the Xingu River (thereby increasing power generation at Belo Monte) would flood a vast area (6140 km²). The great impact of dams provides a powerful reason for Brazil to reassess its current policies that allocate large amounts of energy in the country’s national grid to subsidized aluminum smelting for export. The case of Belo Monte and the five additional dams planned upstream (including the Altamira/Babaquara Dam) indicate the need for Brazil to reform its environmental assessment and licensing system to include the impacts of multiple interdependent projects.     

青藏铁路用空气弹簧橡胶胶料耐紫外线老化试验研究

我国正在建设的青藏铁路是世界上海拔最高、线路最长的高原铁路.其气候特点对橡胶制品提出了新的技术要求,为适应在该气候条件下的运用需要,进行了针对空气弹簧胶囊用橡胶的耐紫外线老化试验研究工作,试验表明随着紫外线老化时间的增加,其硬度和300%定伸强度均提高,但拉伸强度和扯断伸长率随着紫外线老化时间的增加而降低;通过与胶料的耐臭氧试验和大气老化试验比较,基本可以认为引起空气弹簧胶囊表面龟裂的主要原因是受力状况、臭氧和大气老化的作用,而紫外线对橡胶的表面龟裂影响作用不大,因此,为提高空气弹簧胶囊的耐老化性能,主要考虑降低橡胶的受力,提高耐臭氧和耐大气老化性能.     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

Monte Carlo analysis of field water flow comparing uni- and bimodal effective hydraulic parameters for structured soil

Soil structure critically affects the hydrological behaviour of soils. In this paper, we examined the impact of areal heterogeneity of hydraulic properties of a structured soil on soil ensemble behaviour for various soil water flow processes with different top boundary conditions (redistribution and drainage plus evaporation and infiltration). Using a numerical solution of the Richards' equation in a stochastic framework, the ensemble characteristics and flow dynamics were studied for drying and wetting processes observed during a time interval of ten days when a series of relatively intense rainfall events occurred. The effects of using unimodal and bimodal interpretative models of hydraulic properties on the ensemble hydrological behaviour of the soil were illustrated by comparing predictions to mean water contents measured over time in several sites at field scale. Although the differences between unimodal and bimodal fitting are not significant in terms of goodness of fit, the differences in process predictions are considerable with the bimodal soil simulating water content measurements much better than unimodal soil. We also investigated the relative contribution of the soil variability of each parameter on the variance of the water contents obtained as the main output of the stochastic simulations. The variability of the structural parameter, weighting the two pore space fractions in the bimodal interpretative model, has the largest contribution to water content variance. The contribution of each parameter depends only partly on the coefficient of variation, much more on the sensitivity of the model to the parameters and on the flow process being observed. We observed that the contribution of the retention parameters to uncertainty increases during drainage processes; the opposite occurs with the hydraulic conductivity parameters.     

Spatial modelling of the probability of obtaining a detectable concentration of ammonia in Moreton Bay, Australia

Ammonia is an important water quality variable, which in excess, can be detrimental to waterways and their ecosystems. In the Ecosystem Health Monitoring Program in South-east Queensland ammonia is monitored monthly, however, often more than 50% of the ammonia observations in Moreton Bay are below detection limit, making it difficult to draw useful inferences. In this paper a clipped Gaussian random field is used to spatially model and map the probability of detectable concentration of ammonia. The methodology is applied to the Moreton Bay samples collected in February 2005. The results suggest that for this month the oceanic impacted areas have higher probability of detectable ammonia concentration than the areas closer to the main sources of anthropogenic inputs.     

建筑物消防系统可靠性分析

为评估建筑物消防系统的可靠性,首先将系统分为探测报警系统和灭火系统等部分,再用事件树法分析事故发生时消防系统不同部分的反应情况.通过贝叶斯理论及历史统计数据得到系统失效率的不确定性概率分布,建立消防系统可靠性随时间变化的数学模型; 用蒙特卡罗方法模拟求得系统可靠性的时间函数并对模型的不确定性参数进行敏感性分析.该方法将统计数据与经验公式、理论方法相结合,并利用蒙特卡罗方法处理模型中的不确定性,不仅能够有效估计消防系统的可靠性,还可对其他类似系统的可靠性进行分析,并通过敏感性分析为进一步减少估计的不确定性提出合理建议.