大气污染物扩散模式的应用研究综述

应用大气污染物扩散模式可以模拟不同尺度、气象、地形条件下工业污染物在大气中的输送与扩散特征,为大气监测、城市环境规划和空气质量预报等工作提供科学依据.归纳了目前广泛应用于模拟工业污染物扩散的模式,着重介绍了近年来国内外对这些模式的主要应用研究进展,比较了各模式在应用上的优缺点,并对大气污染物扩散模式的应用研究前景进行了讨论.     

OSPM - A Parameterised Street Pollution Model

For many practical applications, as e.g. in support of air pollution management, numerical models based on solution of the basic flow and dispersion equations are still too complex. Alternative are models that are basically parameterised semi-empirical models making use of a priori assumptions about the flow and dispersion conditions. However, these models must, be thoroughly tested and their performance and limitations carefully documented. The Danish Operational Street Pollution Model (OSPM) belongs to this category of parameterised models. In the OSPM, concentrations of exhaust gases are calculated using a combination of a plume model for the direct contribution and a box model for the recirculating part of the pollutants in the street. Parameterisation of flow and dispersion conditions in street canyons was deduced from extensive analysis of experimental data and model tests. Results of these tests were used to further improve the model performance, especially with regard to different street configurations and a variety of meteorological conditions.     

A Simple Model for Urban Background Pollution

A simple urban background pollution model is presented. Contributions from the individual area sources, subdivided into a grid net of a resolution of 2km × 2km, are integrated along the wind direction path assuming linear dispersion with the distance to the receptor point. Horizontal dispersion is accounted for by averaging the calculated concentrations over a certain, wind speed dependent wind direction sector, centred on the average wind direction. Formation of the nitrogen dioxide due to oxidation of nitrogen monoxide by ozone is calculated using a simple chemical model based on assumption of a photochemical equilibrium on the time scale of the pollution transport across the city area. The rate of entrainment of fresh rural ozone is governed by this time scale. The model is suitable for calculations of urban background when the dominating source is the road traffic. For this source the emissions take place at ground level, and a good approximation is to treat the emissions as area sources, but with an initial vertical dispersion determined by the height of the buildings.     

Exploring the use of computer models in participatory integrated assessment – experiences and recommendations for further steps

Integrated assessment (IA) can be defined as a structured process of dealing with complex issues, using knowledge from various scientific disciplines and/or stakeholders, such that integrated insights are made available to decision makers (J. Rotmans, Enviromental Modelling and Assessment 3 (1998) 155). There is a growing recognition that the participation of stakeholders is a vital element of IA. However, only little is known about methodological requirements for such participatory IA and possible insights to be gained from these approaches. This paper summarizes some of the experiences gathered in the ULYSSES project, which aims at developing procedures that are able to bridge the gap between environmental science and democratic policy making for the issue of climate change. The discussion is based on a total of 52 IA focus groups with citizens, run in six European and one US city. In these groups, different computer models were used, ranging from complex and dynamic global models to simple accounting tools. The analysis in this paper focuses on the role of the computer models. The findings suggest that the computer models were successful at conveying to participants the temporal and spatial scale of climate change, the complexity of the system and the uncertainties in our understanding of it. However, most participants felt that the computer models were less instrumental for the exploration of policy options. Furthermore, both research teams and participants agreed that despite considerable efforts, most models were not sufficiently user-friendly and transparent for being accessed in an IA focus group. With that background, some methodological conclusions are drawn about the inclusion of the computer models in the deliberation process. Furthermore, some suggestions are made about how given models should be adapted and new ones developed in order to be helpful for participatory IA. This revised version was published online in July 2006 with corrections to the Cover Date.     

生命线网络系统抗震拓扑优化的Benchmark模型

生命线系统拓扑优化问题的Benchmark模型是评测新型算法正确性和适用性的重要手段。基于此,首先以生命线网络系统抗震拓扑优化分析模型为背景,建立了该优化问题的三个Benchmark模型,并在Visual Compaq Fortran开发环境下,通过穷举法统计出解空间的所有网络,进而甄选出不同节点可靠度约束下的最优网络和若干次优网络,最后利用上述Benchmark模型对生命线网络系统抗震拓扑优化中的蚁群算法进行测试。结果表明,当网络规模较小时,蚁群算法能精确地搜索到最优解;当网络规模增大后,蚁群算法也能以较大概率搜索到最优解或次优解。     

Using Expert and Non-expert Models of Climate Change to Enhance Communication

Climate change is a significant global risk that is predicted to be particularly devastating to coastal communities. Climate change adaptation and mitigation have been hindered by many factors, including psychological barriers, ineffective outreach and communication, and knowledge gaps. This qualitative study compares an expert model of climate change risks to county administrators' “mental” models of climate change and related coastal environmental hazards in Crystal River, Florida, USA. There were 24 common nodes in the expert and the combined non-expert models, mainly related to hurricanes, property damage, and economic concerns. Seven nodes mentioned by non-experts fit within, but were not a part of, the expert model, primarily related to ecological concerns about water quality. The findings suggest that effective climate outreach and communication could focus on compatible parts of the models and incorporate local concerns to find less controversial ways to discuss climate-related hazards.     

Societal individualism–collectivism and uncertainty avoidance as cultural moderators of relationships between job resources and strain

The job demands–resources model is a dominant theoretical framework that describes the influence of job demands and job resources on employee strain. Recent research has highlighted that the effects of job demands on strain vary across cultures, but similar work has not explored whether this is true for job resources. Given that societal characteristics can influence individuals' cognitive structures and, to a lesser extent, values in a culture, we address this gap in the literature and argue that individuals' strain in reaction to job resources may differ across cultures. Specifically, we theorize that the societal cultural dimensions of individualism–collectivism and uncertainty avoidance shape individual‐level job resource–strain relationships, as they dictate which types of resources (i.e., individual vs. group preference‐oriented and uncertainty‐reducing vs. not) are more likely to be valued, used, or effective in combating strain within a culture. Results revealed that societal individualism–collectivism and uncertainty avoidance independently moderated the relationships between certain job resources (i.e., job control, participation in decision making, and clear goals and performance feedback) and strain (i.e., job satisfaction and turnover intentions). This study expands our understanding of the cross‐cultural specificity versus generalizability of the job demands–resources model.     

Considering built environment and spatial correlation in modeling pedestrian injury severity

Objective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.

Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.

Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.

Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.     

The role of respondents’ comfort for variance in stated choice surveys: evidence from a SCUBA diving case

Preference elicitation among outdoor recreational users is subject to measurement errors that depend, in part, on survey planning. This study uses data from a choice experiment survey on recreational SCUBA diving to investigate whether self-reported information on respondents’ comfort when they complete surveys correlates with the error variance in stated choice models of their responses. Comfort-related variables are included in the scale functions of the scaled multinomial logit models. The hypothesis was that higher comfort reduces error variance in answers, as revealed by a higher scale parameter and vice versa. Information on, e.g., sleep and time since eating (higher comfort) correlated with scale heterogeneity, and produced lower error variance when controlled for in the model. That respondents’ comfort may influence choice behavior suggests that knowledge of the respondents’ activity patterns could be used to plan the timing of interviews to decrease error variance in choices and, hence, generate better information.