The Role of Fictional Film Exposure and Narrative Engagement for Personal Norms,Guilt and Intentions to Protect The Climate

ABSTRACT

Using an integration of Norm Activation Theory, Value-Belief-Norm Theory and Narrative Persuasion, this study investigates the ability of an eco-dystopian science fiction film set in a world of excessive solar radiation to support intentions for pro-environmental behavior. Specifically, the influence of narrative engagement and explicit references to human responsibility for climate change are tested. A 2 (high vs. low narrative engagement) x?3 (human responsibility frame vs. temperature frame vs. no frame) plus control group (no film) experiment was conducted (n?=?257). Results show that compared to the control condition, the film had an indirect effect on behavioral intentions by raising the personal norm (a sense of personal obligation to act). The temperature change and human responsibility frames did not show any influence, while narrative engagement had an indirect effect on intentions by increasing guilt. The results are discussed with regard to their theoretical and practical implications.     

自然灾害等级划分及灾情比较模型探讨

作者参照灰色关联分析方法，提出了一种自然灾害等级划分和灾情比较的模型，应用该模型，在计算出关联度后，很容易得到灾害分级和灾情比较的结果。此外，该模型对分级 指标项数没有限制，故有很强的灵活和实用性。     

西藏古乡沟泥石流模型试验研究

通过模型试验，研究不同类型（粘性、稀性、水石流）和不同规模（Ｐ＝２％、４％）的泥石流在古乡沟堆积扇上的运动和冲淤规律，同时进行防治工程模型试验研究，结合野外调查和资料对比，选择合理的防治工程方案，为工程设计提供可靠的依据和数据。本文结论对川藏公路与古乡沟类似灾害的整治有一定的参考价值     

Modeling the effects of concentration history on the slow desorption of trichloroethene from a soil at 100% relative humidity

To determine the effects of concentration history on slow sorption processes, desorption kinetic profiles for trichloroethene (TCE) were measured for a soil at 100% relative humidity subject to different exposure concentrations and exposure times. Exposure concentrations ranged from 1% to 80% of the saturation vapor pressure (Ps) for TCE, and exposure times (i.e., time allowed for sorption before desorption begins) ranged from 1 to 96 days. A spherical diffusion model based on a gamma distribution of sorption rates and a gamma distribution of desorption rates was developed and applied to the data. At 80% P/Ps, the entire gamma distributions of sorption and desorption rates were available for TCE. In accordance with a micropore filling mechanism, the fraction of these distributions available for TCE sorption decreased with decreasing P/Ps. Experimental results are consistent with a micropore-filling mechanism, where the amount of slow desorbing mass decreased with decreasing exposure time, and the fraction of slow desorbing sites filled decreased with decreasing exposure concentration. Simulation results suggest that diffusion limits the rates that micropores fill, and that rates of sorption and desorption for soil contaminated at smaller values of P/Ps are, on average, less than those at larger values of P/Ps (i.e., slow desorption rates are a function of exposure concentration). Simulation results also suggest that the model adequately describes the effects of exposure concentration and exposure time on the rates of sorption and desorption, but not on the capacity of the slow sites for TCE. This work is important because contaminant concentrations in the subsurface vary in space and time, and the proposed model represents a new and mechanistically based approach to capture the effects of this heterogeneity on slow desorption.     

几种大气预测模式预测结果比较分析

估算模式、AERMOD模式系统、ADMS模式系统均是HJ2.2-2008《环境影响评价技术导则大气环境》中推荐的大气预测模式,为探求此3种大气预测模式预测结果的大小关系规律,选用估算模式、AERMOD模式系统、ADMS模式系统,在简单地形和复杂地形两种条件下,结合一般工业类环评项目中常见的点源、面源案例,对不同预测模式的大气预测结果进行比较分析,得出相应的规律,对环评工作中进一步预测模式的选用具有一定的参考借鉴意义。     

Traditional occupancy–abundance models are inadequate for zero-inflated ecological count data

Traditional occupancy–abundance and abundance–variance–occupancy models do not take into account zero-inflation, which occurs when sampling rare species or in correlated counts arising from repeated measures. In this paper we propose a novel approach extending occupancy–abundance relationships to zero-inflated count data. This approach involves three steps: (1) selecting distributional assumptions and parsimonious models for the count data, (2) estimating abundance, occupancy and variance parameters as functions of site- and/or time-specific covariates, and (3) modelling the occupancy–abundance relationship using the parameters estimated in step 2. Five count datasets were used for comparing standard Poisson and negative binomial distribution (NBD) occupancy–abundance models. Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) occupancy–abundance models were introduced for the first time, and these were compared with the Poisson, NBD, He and Gaston's and Wilson and Room's abundance–variance–occupancy models. The percentage of zero counts ranged from 45 to 80% in the datasets analysed. For most of the datasets, the ZINB occupancy–abundance model performed better than the traditional Poisson, NBD and Wilson and Room's model. He and Gaston's model performed better than the ZINB in two out of the five datasets. However, the occupancy predicted by all models increased faster than the observed as density increased resulting in significant mismatch at the highest densities. Limitations of the various models are discussed, and the need for careful choice of count distributions and predictors in estimating abundance and occupancy parameter are indicated.     

Prevalence-adjusted optimisation of fuzzy models for species distribution

Most performance criteria which have been applied to train ecological models focus on the accuracy of the model predictions. However, these criteria depend on the prevalence of the training set and often do not take into account ecological issues such as the distinction between omission and commission errors. Moreover, a previous study indicated that model training based on different performance criteria results in different optimised models. Therefore, model developers should train models based on different performance criteria and select the most appropriate model depending on the modelling objective. This paper presents a new approach to train fuzzy models based on an adjustable performance criterion, called the adjusted average deviation (aAD). This criterion was applied to develop a species distribution model for spawning grayling in the Aare River near Thun, Switzerland. To analyse the strengths and weaknesses of this approach, it was compared to model training based on other performance criteria. The results suggest that model training based on accuracy-based performance criteria may produce unrealistic models at extreme prevalences of the training set, whereas the aAD allows for the identification of more accurate and more reliable models. Moreover, the adjustable parameter in this criterion enables modellers to situate the optimised models in the search space and thus provides an indication of the ecological model relevance. Consequently, it may support modellers and river managers in the decision making process by improving model reliability and insight into the modelling process. Due to the universality and the flexibility of the approach, it could be applied to any other ecosystem or species, and may therefore be valuable to ecological modelling and ecosystem management in general.     

漳卫河流域水文循环过程对气候变化的响应

气候变化对我国各地区水资源影响的时空格局变化,是气候变化影响评估的重要内容。论文以漳卫河为研究流域,采用线性回归法、Mann-Kendall非参数检验等方法,分析了1957-2001年的水文气象要素变化特征;基于数字高程模型、土地利用和土壤类型等资料,建立了SWAT分布式水文模型,验证了SWAT模型在该流域的适用性;根据IPCC第四次评估报告多模式结果,分析了IPCC SRES-A2、A1B、B1情景下21世纪降水、气温、径流、蒸发的响应过程。结果表明漳卫河流域未来2011-2099年降水量变化较基准期呈现出增加趋势,年平均气温较基准期也呈现出显著的上升趋势,各年代径流量较基准期将出现先减少后增大的态势。     

Emergy algebra: Improving matrix methods for calculating transformities

Transformity is one of the core concepts in Energy Systems Theory and it is fundamental to the calculation of emergy. Accurate evaluation of transformities and other emergy per unit values is essential for the broad acceptance, application and further development of emergy methods. Since the rules for the calculation of emergy are different from those for energy, particular calculation methods and models have been developed for use in the emergy analysis of networks, but double counting errors still occur because of errors in applying these rules when estimating the emergies of feedbacks and co-products. In this paper, configurations of network energy flows were classified into seven types based on commonly occurring combinations of feedbacks, splits, and co-products. A method of structuring the network equations for each type using the rules of emergy algebra, which we called “preconditioning” prior to calculating transformities, was developed to avoid double counting errors in determining the emergy basis for energy flows in the network. The results obtained from previous approaches, the Track Summing Method, the Minimum Eigenvalue Model and the Linear Optimization Model, were reviewed in detail by evaluating a hypothetical system, which included several types of interactions and two inputs. A Matrix Model was introduced to simplify the calculation of transformities and it was also tested using the same hypothetical system. In addition, the Matrix Model was applied to two real case studies, which previously had been analyzed using the existing method and models. Comparison of the three case studies showed that if the preconditioning step to structure the equations was missing, double counting would lead to large errors in the transformity estimates, up to 275 percent for complex flows with feedback and co-product interactions. After preconditioning, the same results were obtained from all methods and models. The Matrix Model reduces the complexity of the Track Summing Method for the analysis of complex systems, and offers a more direct and understandable link between the network diagram and the matrix algebra, compared with the Minimum Eigenvalue Model or the Linear Optimization Model.