Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis

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.

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Bayesian Networks and Adaptive Management of Wildlife Habitat

Abstract: Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision‐support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive‐management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief‐updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions.     

植被覆盖度与沙尘暴形成条件分析

以归一化差值植被指数（Normalized Difference Vegetation Index,NDVI）为指标分析了我国1981年到1998年期间植被覆盖变化情况,给出了植被急剧减少产生新沙源的实例。通过对1998年4月强沙尘暴的分析,指出影响沙尘暴产生和运移的因素除了地形、风速、风向、大气稳定度外,当地植被覆盖和土壤因素也是关键因素之一。并通过对植被覆盖度（NDVI）与浮尘指数（Aerosol Index）进行定量分析,发现二者之间存在明显的负相关关系,表明植被的存在可以从一定程度上抑制浮尘现象的产生。因此,在沙尘运移路径上,采取封山封荒,植树种草,增加植被覆盖度以固结当地土壤和泥沙的措施,可以在一定程度上抑制沙尘暴灾害的发生。     

A Fuzzy Model for Assessing Risk of Occupational Safety in the Processing Industry

Abstract

Managing occupational safety in any kind of industry, especially in processing, is very important and complex. This paper develops a new method for occupational risk assessment in the presence of uncertainties. Uncertain values of hazardous factors and consequence frequencies are described with linguistic expressions defined by a safety management team. They are modeled with fuzzy sets. Consequence severities depend on current hazardous factors, and their values are calculated with the proposed procedure. The proposed model is tested with real-life data from fruit processing firms in Central Serbia.     

原子荧光法测定污泥中总砷含量的不确定度评定

对采用原子荧光法测定污泥中总砷过程中引入的不确定度进行分析,找出了影响测定结果的因素,并对各不确定度分量及合量进行了评定。测量不确定度主要来源于试样称量、标准溶液配制、工作曲线拟合、污泥含水率、各种玻璃量器的使用及测量重复性。其中样品预处理、样品消解液中总砷的质量浓度及污泥含水率所产生的不确定度较大。这对进一步优化试验方法,提高检测结果的准确性有重要意义。     

适应性环境管理模式在大型赛事筹备中 的应用（下） ——应用实例

将适应性环境管理的模式应用于深圳第26届世界大学生夏季运动会生态环境保障工作,对生态环境保障总目标层层分解落实,由第三方评估机构对任务逐项跟踪管理,定期评估各部门绩效,根据外部条件和内部因素的变化对工作方案和阶段性目标进行动态调整,最大限度地降低了环境管理工作的不确定性。经过六个月的实践应用,大运赛事期间深圳市生态环境保障工作取得了十分显著的成效。     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

基于ARM处理器的CO气体在线监测系统的设计

以三星公司的ARM7处理器S3C44B0X、嵌入式实时操作系统uC/OS -Ⅱ为基础,介绍了一种CO气体在线监测仪器电路部分的实现,分析其基本原理,给出了系统整体框图,完成了其硬件系统和软件系统的设计.实践证明,该方法能够满足系统控制和数据处理要求,具有较好的实用性.     

自然灾害风险分析的基本原理

介绍了风险研究的基本内容,探讨了自然灾害风险分析的基本原理。从风险的本质和自然灾害的具体表现可以看出,自然灾害风险的分析,依赖于一整套贯穿于致灾因子到灾害影响的、能前后相联的模型。本文对各个环节的模型提出了一些原则性的建议。     

Sensitivity Analyses of Spatial Population Viability Analysis Models for Species at Risk and Habitat Conservation Planning

Abstract:  Population viability analysis (PVA) is an effective framework for modeling species- and habitat-recovery efforts, but uncertainty in parameter estimates and model structure can lead to unreliable predictions. Integrating complex and often uncertain information into spatial PVA models requires that comprehensive sensitivity analyses be applied to explore the influence of spatial and nonspatial parameters on model predictions. We reviewed 87 analyses of spatial demographic PVA models of plants and animals to identify common approaches to sensitivity analysis in recent publications. In contrast to best practices recommended in the broader modeling community, sensitivity analyses of spatial PVAs were typically ad hoc, inconsistent, and difficult to compare. Most studies applied local approaches to sensitivity analyses, but few varied multiple parameters simultaneously. A lack of standards for sensitivity analysis and reporting in spatial PVAs has the potential to compromise the ability to learn collectively from PVA results, accurately interpret results in cases where model relationships include nonlinearities and interactions, prioritize monitoring and management actions, and ensure conservation-planning decisions are robust to uncertainties in spatial and nonspatial parameters. Our review underscores the need to develop tools for global sensitivity analysis and apply these to spatial PVA.