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.     

石家庄市栾城区农田土壤重金属分布特征及作物风险评价

为探讨栾城区内农田土壤重金属的分布特征,对表层土壤As、Cd、Cr、Cu、Hg、Ni、Pb和Zn这8种重金属污染特征、空间分布和污染风险进行分析,并使用PMF模型进行污染源解析,探讨栾城区土壤管控方案.进一步检测作物中重金属含量,利用概率风险评价方法评价研究区农产品的非致癌健康风险,为栾城区内农田土壤重金属污染治理与防控提供理论基础.结果表明,表层土壤ω（Cd）、ω（Cr）、ω（Cu）、ω（Pb）和ω（Zn）分别为0.06~1.08、22.14~473.47、12.83~150.74、10.75~577.72和62.23~652.78 mg·kg^-1,点位超标率分别为1.83%、1.22%、0.61%、0.61%和1.22%.此外,部分区域土壤中的重金属有向作物中转移并积累的现象,小麦籽粒Cd和Pb含量超标率分别为2.13%和5.32%,玉米籽粒Cd含量超标率为1.20%.健康风险评价结果表明,栾城区玉米的复合非致癌健康风险（TTHQ）小于1,对人体没有明显的负面影响,食用研究区小麦的复合非致癌风险（TTHQ>1）,对人体产生负面影响的可能性较大.土壤中重金属的空间分布受工业区涉污企业分布的影响,8种重金属含量较高的区域均主要分布在工业企业较集中的中部、西部和南部.总体而言,研究区大部分表层耕地土壤未受到明显的重金属污染,存在中等强度（2级）的生态危害.Cd为无-中度污染（1级）,Cd和Hg存在中等强度潜在生态风险（2级）,其余重金属均为无污染,低潜在生态风险（1级）.根据PMF污染源解析结果和实地调查推测,土壤重金属主要来自土壤母质（52.05%）,历史污灌和工业生产的人为污染源（32.98%）和大气沉降（14.97%）.综上所述,研究区北部和东部土壤重金属含量较低,应划分为优先保护类,研究区西部、中部和南部部分点位重金属含量超标,其来源主要为化工、涂料、机械装备等企业,应划分为安全利用类,严控污染物的输入,采用农艺调控等措施,减少重金属向农作物的转移,降低食品安全健康风险.本研究将有助于栾城区土壤的分级治理和污染管控.     

平流层硫酸盐气溶胶辐射效应的模拟研究

平流层中的硫酸盐气溶胶在地球能量循环和全球气候变化中发挥着关键性作用.基于自主开发的矢量辐射传输模型,重点研究对流层气溶胶类型、平流层气溶胶光学厚度(AOD)、太阳天顶角(SZA)和地表反照率等对平流层硫酸盐气溶胶辐射强迫和大气加热速率等辐射效应的影响.结果表明,对流层无气溶胶时,平流层气溶胶在大气顶层(TOA)的辐射强迫为-15.80 W·m^-2,地气系统的冷却效应最大.对流层气溶胶为黑碳时,平流层气溶胶在大气底层(BOT)的辐射强迫最小,为-47.53 W·m^-2,地表冷却最大.同时,平流层硫酸盐的辐射强迫导致对流层降温,平流层升温,在模拟条件下,最大升温可达0.6 K·d^-1.此外,结果还表明,平流层硫酸盐辐射强迫对AOD、SZA和地表反照率均具有很高的敏感性.平流层气溶胶在TOA和BOT的辐射强迫随AOD的增大呈线性减小趋势,但随地表反照率的增大呈线性增大趋势.AOD和SZA的增大会强化辐射强迫的作用效果,但地表反照率的增大可能会改变辐射强迫的正负,导致平流层硫酸盐对地气系统的作用效果从冷却变为加热.     

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.     

基于MonteCarlo模拟的土壤重金属综合风险评价与案例分析

基于不确定性理论,将Monte Carlo模拟计算应用到土壤重金属风险评价中,构建了生态风险和健康风险的随机不确定模型,结合多元统计方法进行实例研究.结果表明,研究区土壤Cd、Co和Cr污染水平较高,主要富集在建矿时间较长的工业园区;综合单因子指数和综合风险因子指数评价显示土壤重金属主要为轻度污染(相应概率分别为53.2%和55.6%);手-口摄入是研究区土壤重金属暴露的主要途径,Cr为主要致癌因子;儿童承担的健康风险较高,其非致癌风险指数分别为成人的5.0倍(手-口摄入途径)和8.2倍(皮肤暴露途径),两种暴露途径下其致癌风险指数均高于USEPA推荐的安全水平.     

Assessment of human exposure to benzene through foods from the Belgian market

Benzene is a volatile organic compound known to be carcinogenic to humans (Group 1) and may be present in food. In the present study, 455 food samples from the Belgian market were analyzed for benzene contents and some possible sources of its occurrence in the foodstuffs were evaluated. Benzene was found above the level of detection in 58% of analyzed samples with the highest contents found in processed foods such as smoked and canned fish, and foods which contained these as ingredients (up to 76.21 μg kg⁻¹). Unprocessed foods such as raw meat, fish, and eggs contained much lower concentrations of benzene. Using the benzene concentrations in food, a quantitative dietary exposure assessment of benzene intake was conducted on a national representative sample of the Belgian population over 15 years of age. The mean benzene intake for all foods was 0.020 μg kg bw d⁻¹ according to a probabilistic analysis. These values are below the minimum risk level for oral chronic exposure to benzene (0.5 μg kg bw d⁻¹).     

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N₂O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N₂O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N₂O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N₂O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N₂O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N₂O emissions from arable soils at regional or continental scales.     

考虑参数随机性的焊点热疲劳失效分析

目的研究电子产品焊点几何参数的随机性对焊点热疲劳寿命的影响。方法基于Coffin-Masson的修正式Engelmaier模型,建立考虑多参数随机性的引线型封装的焊点热疲劳寿命评估模型,并利用蒙特卡罗法对该模型的精确度进行验证。以SMT鸥翼型(gull-wing)TSOP062封装为例,将焊点几何参数的随机性对热疲劳寿命的影响进行量化分析。结果影响焊点寿命的几何参数离散系数越大,焊点疲劳寿命越小,随机变量越多,焊点热疲劳寿命下降的越明显,焊点高度是影响元器件热疲劳寿命的敏感参数。结论该方法在已知小批次产品的参数波动信息的情况下,能预测整批次产品的热疲劳寿命,极大地减少试验时间和成本,提高电子产品及装备的可靠性。     

MODIFIED WATER QUANTITY RECEIVING MODEL FOR FLORIDA CONSERVATION AREAS1

ABSTRACT: The Conservation Areas in South Florida have been considered as one of the major water storage areas to provide a water supply for the Everglades National Park and Lower East Coast (LEC). Due to the increasing water demands of the area, additional backpumping of the surplus runoff from the LEC area into the Conservation Areas has been considered as one of several alternative plans. The Receiving Water Quantity (EPA, 1971) model has been adapted and modified to be applicable in the Conservation Areas to investigate the possible impact of additional inflow under various backpumping cases. The modification of the model included Manning's roughness coefficient, depth of flow, width of hypothetical channels through marsh areas, rainfall input, seepage rate, etc. The use of the Monte Carlo technique for area computations was found to be easy and time saving both in area and weighting rainfall input to each node. Comparison of results generated by this modified model with the recorded values in Conservation Areas 1 and 2A indicated that the model not only can be a very good evaluation tool to simulate the hydraulic regime of the Conservation Areas system but also a proper tool for investigating the impact of additional inflow resulting from the backpumping related to the water use planning and management.     

PROBABILISTIC METHODS IN STREAM QUALITY MANAGEMENT1

ABSTRACT. Recent advances in water quality modelling have pointed out the need for stochastic models to simulate the probabilistic nature of water quality. However, often all that is needed is an estimate of the uncertainty in predicting water quality variables. First order analysis is a simple method of providing an estimate in the uncertainty in a deterministic model due to uncertain parameters. The method is applied to the simplified Streeter-Phelps equations for DO and BOD; a more complete Monte Carlo simulation is used to check the accuracy of the results. The first order analysis is found to give accurate estimates of means and variances of DO and BOD up to travel times exceeding the critical time. Uncertainty in travel time and the BOD decay constant are found to be most important for small travel times; uncertainty in the reaeration coefficient dominates near the critical time. Uncertainty in temperature was found to be a negligible source of uncertainty in DO for all travel times.