基于Monte Carlo与Markov法参数不确定性条件下SIL评估

为了避免由参数不确定性因素导致较大的Markov法SIL评估偏差,且减少计算工作量和复杂性,采用Monte Carlo（MC）仿真方法处理含不确定性参数的Markov模型,并借助Matlab GUI编程开发MC仿真处理参数不确定性条件下Markov法SIL评估可视化仿真计算软件。在理论研究的基础上,为说明该研究方法与计算软件的可行性,以石油天然气工业高完整性压力保护系统（HIPPS）为算例进行SIL评估。结果表明:MC仿真方法可以有效处理Markov法SIL评估中参数不确定性问题;基于Matlab GUI编程设计出的仿真计算软件在一定程度上可以提高计算效率。     

多因素耦合作用下建筑火灾后加固施工过程风险性评估

为降低火灾后建筑结构加固施工过程中事故的发生率,保障加固施工作业过程的 安全,针对我国建筑火灾后加固施工项目的特点,构建了多因素耦合作用下的基于熵权 -未确知测度理论的建筑火灾后加固施工过程风险性评估模型,并将模型应用于五个工 程实例中,取得了良好的效果。研究结果表明,该模型是熵权理论、未确知测度理论的 的有机结合,为定量研究建筑火灾后加固施工过程安全风险,并有效的控制安全风险, 确保施工安全提供了新的思路。     

大气可降水量卫星遥感反演不确定性对太阳总辐射模拟的影响

辐射传输方程模拟法作为一种较为准确有效的太阳总辐射间接获得方法,其模拟精度受到代入方程的大气参数精度的影响。论文通过将香港2005—2013年卫星遥感反演的大气可降水量产品与地面站数据对比,以68.3%的置信水平分析了卫星遥感反演的大气可降水量的不确定性;并通过收集2005—2013年香港其他影响大气上界太阳辐射到达地表的大气参数,如气溶胶光学厚度、气溶胶单次散射反照率、臭氧含量综合模拟大气环境,确定了大气可降水量的不确定性在夏季和冬季对太阳总辐射模拟（250~2 800 nm）造成的相对误差。研究结果表明,受大气可降水量不确定性影响,使用辐射传输方程法模拟获得的太阳总辐射有1%~3%的相对误差。使用该方法冬季受大气可降水量不确定性影响程度比夏季大。决定相对误差大小的主要因素是波长,大气水汽吸收作用强的波长上,相对误差也大;其次因素是太阳天顶角,太阳天顶角越大,该方法模拟所得太阳总辐射相对误差越大。研究为大气可降水量参数不确定性影响下太阳总辐射模拟的误差评估提供了依据。     

东莞市某地区地表水中重金属健康风险不确定性评价

为了解东莞市西南地区(麻涌镇、沙田镇、虎门镇和长安镇)地表水中重金属污染现状与健康风险水平,对研究区域内29个地表水样中As、Cd、Cr、Cu、Hg、Ni、Pb和Zn等重金属元素进行测定,并运用不确定性健康风险评价模型对研究区地表水中的重金属进行健康风险评估。结果表明:该研究区域地表水中重金属引起的致癌风险较高,人均年健康风险都在Ⅴ级风险及以上。化学致癌性重金属引起的人均年健康风险中,以Cr最大,As次之,然后是Cd。非化学致癌性重金属引起的人均年健康风险中,Pb的风险最大,Ni次之,风险均远小于化学致癌性重金属引起的人均年健康风险。因此,该研究区域地表水中重金属健康风险管理应以化学致癌风险为主,重金属污染物的优先控制顺序为CrAsCd。     

Climate change implications for disaster risk management in Japan: A case study on perceptions of risk management personnel and communities in Saijo city

This paper reviews climate change impacts and the existing disaster risk management system in Japan and offers the results of a structured questionnaire survey of the community leaders and disaster risk management personnel of Saijo city of Japan that assesses their perceptions about dealing with the extreme disasters by the existing disaster risk management systems. This study was inspired by the record number of typhoon landfall that has surprised the local government and communities in 2004. While unearthing the hidden vulnerabilities in cities like Saijo, this event has loosened the confidence of local communities on the disaster risk management systems. From the study, we conclude that the existing disaster risk management systems need further fillip and that the proactive community involvement in disaster risk management is still in nascent stages. Associating with the scientific community, involving the local communities (including the elderly), enhancing the redundancy in disaster risk management systems, inculcating strategic thinking and micro-level planning, conducting vulnerability assessments by considering the special circumstances including resource constraints of small cities and better policy coordination across the administrative hierarchy are some important considerations for dealing with the uncertainty brought by the extreme events.     

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.     

Modeling the impact of uncertainty in emissions trading markets with bankable permits

The various forms of uncertainty that firms may face in bankable emission permit trading markets will affect firms’ decision making as well as their market performance. This research explores the effect of increased uncertainty over future input costs and output prices on the temporal distribution of emission. In a dynamic programming setting, the permit price is a convex function of stochastic prices of coal and electricity. Increased uncertainty about future market conditions increases the expected permit price and causes a risk neutral firm to reduce ex ante emissions in order to smooth out marginal abatement costs over time. Finally, safety valves, both low-side and high-side, are suggested to reduce the impact of uncertainty in bankable emission trading markets.     

A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources

We demonstrate a density projection approximation method for solving resource management problems with imperfect state information. The method expands the set of partially-observed Markov decision process (POMDP) problems that can be solved with standard dynamic programming tools by addressing dimensionality problems in the decision maker's belief state. Density projection is suitable for uncertainty over both physical states (e.g. resource stock) and process structure (e.g. biophysical parameters). We apply the method to an adaptive management problem under structural uncertainty in which a fishery manager's harvest policy affects both the stock of fish and the belief state about the process governing reproduction. We solve for the optimal endogenous learning policy—the active adaptive management approach—and compare it to passive learning and non-learning strategies. We demonstrate how learning improves efficiency but typically follows a period of costly short-run investment.     

污染场地健康风险评价中多介质模型的优选研究

综合考虑了参数不确定性、模型不确定性对风险评价结果可信度的影响差异和国内实际污染场地健康风险评价中对已有模型的不当选择可能带来的负面效应,提出相对于参数不确定性的控制,针对目标情景的适当模型选择应成为提高风险评价结果可信度的更重要环节.本研究选取了模型理论架构上符合案例情景的美国环境保护署研发的3MRA、MMSOILS和美国劳伦斯伯克力国家实验室研发的CalTOX 3个被广泛使用的污染场地健康风险评价多介质模型,并与蒙特卡洛模拟相结合,载入污染厂址案例,并对特定受体进行了风险评价,由此定量分析了参数不确定性和模型选择不确定对评价结果可信度的影响程度,并通过对比3个模型的内在架构分析了不确定性的可能来源,最后总结提出了“6步综合评价模式”,该模式能有效提高污染场地健康风险评价的可信度及经济可行性.     

北京地区二噁英类的环境多介质迁移和归趋模拟

以二噁英类为研究对象,利用LevelⅢ多介质逸度模型模拟计算了稳态假设下二噁英类在北京地区空气相、水相、土壤相和沉积物相中的浓度分布及各相间的迁移通量.结果表明:二噁英类在空气和土壤相中的模拟输出浓度(以I-TEQ计,全文同)范围分别为0.32～0.34 pg/m3和0.33～1.14 ng/kg,而相应环境相中的实测值分别为0.33 pg/m3和1.48 ng/kg,二者吻合较好,验证了该模型在北京地区的适用性;北京地区二噁英类的主要迁移过程为空气相—土相沉降和空气相—水相沉降.土壤和大气降解是二噁英类的主要消亡途径,二者占总降解量的99.1%.土壤相是二噁英类的最大储库,其占总量的97.8%.