基于LCA的污水处理厂提标改造环境影响负荷研究

研究旨在评价污水处理厂提标改造工程前后的环境影响,并做出科学的管理决策。以呼和浩特市公主府污水处理厂为例,采用生命周期评价(LCA)的方法,定量研究了污水处理厂改造前后的环境影响负荷,识别和判断出环境影响负荷的变化量。结果表明:该污水处理厂经过改造之后的环境负荷大于改造之前,相比改造前增长约63.5%,其中水质和水体富营养化影响潜能明显减小,而不可再生资源消耗、全球变暖影响潜能的增加量较大,环境负荷增加的根本原因在于电耗的增加。最后根据评价结果提出针对污水处理厂提标改造工程的改善建议。     

混沌理论在水质预测中的应用初探

基于混沌理论、相空间重构思想和嵌入理论分析涟水流域溶解氧的时间序列,计算吸引子维数和最大的Lyapunov指数,研究河流水质系统的复杂性特征。结果表明,河流水质时间序列具有混沌特性,其外在表现为貌似随机的无规则特点;但系统本身内在的、固有的规律(表现在最大的Lyapunov指数),表明短期预测河流水质变化是可行的,并应用混沌相空间模线性回归模型进行了短期预测,其预测结果对涟水流域的水质管理和控制具有一定的参考价值。     

水上交通事故分析研究进展

为更好地开展水上交通事故分析研究,提高我国水上交通安全水平,从船舶风险评估与事故预测、事故分析以及事故及通航安全数据的组织与数据库建立3个方面对国内外的相关研究进行论述和分析。提出以建立水上交通事故时空数据平台为基础,结合数据挖掘和安全工程的理论方法研究事故发生机理,评估事故风险,并将研究事故模拟再现技术作为事故分析的重要技术手段。     

基于前景理论的地铁施工灾后重建方案决策研究

为解决地铁施工灾后有限重建方案的多属性决策问题,以技术先进性、工程造价、工期、对周围居民的生活的影响及环境保护措施可靠性作为决策属性指标,采用考虑决策者参照依赖、损失规避和敏感性递减等心理行为特征的多属性决策分析方法—前景理论,以综合前景值的大小作为评价指标,进行重建方案的选择,计算结果与工程实际较吻合。该研究方法可用于灾后重建方案的决策问题,为决策者提供了理论参考和决策依据。     

The feasibility of concentrated rural settlement in a context of post‐disaster reconstruction: a study of China

There is growing appreciation of the use of concentrated rural settlement as an effective means of implementing infrastructure projects and helping to achieve sustainable development in rural areas. This occurs in China through the exchange of rural residential land for urban construction. However, this policy has not been effective under normal circumstances (called development‐driven conditions) as frequently farmers are reluctant to accept such an exchange. By contrast, in a time of disaster, such as after the 2008 earthquake in Sichuan Province, China, rural victims have accepted this policy of rural residential land exchange. Employing game theory, this paper identifies the reasons for the different outcomes and it contends that the implementation of concentrated rural settlement practice under disaster‐induced conditions is more effective than its introduction under development‐driven conditions. The results of the analysis indicate that, in China, concentrated rural settlement is feasible in a context of post‐disaster reconstruction.     

Multi-stakeholder collaboration in community post-disaster reconstruction: case study from the Longmen Shan Fault area in China

While multi-stakeholder collaboration is critical for effective community post-disaster reconstruction (CPDR), it is often very difficult in practice. The Longmen Shan Fault active seismic zone in China has experienced several recent earthquakes with the 2008 Wenchuan earthquake and 2013 Lushan earthquake, both of which caused extensive and widespread damage to many communities, presenting unprecedented challenges for post-disaster reconstruction. This paper develops a multi-cycle field research method that involves three interconnected cycles: internet research, field visits, and extensive surveys: to examine multi-stakeholder collaboration for the CPDR following the Wenchuan earthquake. It was found that there were 12 types of active stakeholders engaged across four main areas: infrastructure rebuilding, psychological recovery, socio-economic rehabilitation, and ecological restoration. Specifically, local community participation and effective collaboration between the community and the external stakeholders were found to be the most crucial elements for successful reconstruction. Multi-stage field research after the Lushan earthquake verified that CPDR was effective and that multi-stakeholder collaboration had improved from the lessons learnt from the Wenchuan earthquake reconstruction experience. Some advantages and limitations of this research are also given.     

煤矿瓦斯涌出时序预测的自组织数据挖掘方法

为分析煤矿瓦斯涌出复杂系统时间序列预测方法,提出自组织数据挖掘(SODM)与相空间重构(PSR)相结合的预测建模方法。首先应用C-C方法计算时间序列的最佳嵌入维数和延迟时间后进行PSR;然后以二元二次方程为传递函数,以嵌入维数变量为自变量,以延迟时间后的时间序列为因变量,通过内准则确定传递函数系数和外准则选择最优传递函数,并以最优传递函数的输出为下层迭代传递函数的输入,最后获得最优复杂度预测模型。算例结果表明:该方法对煤矿瓦斯涌出量预测的相对误差为-5.751 7% ~6.049 3%,平均相对误差2.145 7%,预测结果能满足煤矿安全生产实际工程应用要求。     

Analysis of the global warming dynamics from temperature time series

Global warming is the observed increase of the average temperature of the Earth. The primary cause of this phenomenon is the release of the greenhouse gases by burning of fossil fuels, land cleaning, agriculture, among others, leading to the increase of the so-called greenhouse effect. An approach to deal with this important problem is the time series analysis. In this regard, different techniques can be applied to evaluate the global warming dynamics. This kind of analysis allows one to make better predictions increasing our comprehension of the phenomenon. This article applies nonlinear tools to analyze temperature time series establishing state space reconstruction and prediction. Since noise contamination is unavoidable in data acquisition, it is important to employ robust techniques. The method of delay coordinates is employed for state space reconstruction and delay parameters are evaluated using the method of average mutual information and the method of false nearest neighbors. Afterwards, the simple nonlinear prediction method is employed to estimate temperatures of the future. Temperature time series from different places of the planet are used. Initially, the approach is verified considering known parts of the time series and afterwards, results are extrapolated for future values estimating temperature until 2028. Results show that these techniques are interesting to estimate temperature time history, presenting coherent estimations.     

Workaholism and daily recovery: A day reconstruction study of leisure activities

This study among 85 individuals used a day reconstruction approach to examine whether workaholism moderates the relationship between daily activities during non‐work time and daily well‐being in the evening (evening happiness, momentary vigor before bedtime, and momentary recovery before bedtime). Specifically, it was hypothesized that daily work‐related activities during the evening have a stronger negative relationship with daily well‐being for employees high (versus low) in workaholism and that daily physical and social activities have a stronger positive relationship with well‐being for employees high (versus low) in workaholism. The results of multilevel analyses largely supported the hypotheses for daily physical and work‐related activities but not for social activities during non‐work time. These findings imply that organizations should not encourage their employees and particularly those who score high on workaholism to work during non‐work time and instead promote physical exercise. Copyright © 2012 John Wiley & Sons, Ltd.     

Using Paleo Reconstructions to Improve Streamflow Forecast Lead Time in the Western United States

In water stressed regions, water managers are exploring new horizons that would help in long‐range streamflow forecasts. Oceanic‐atmospheric oscillations have been shown to influence streamflow variability. In this study, long‐lead time streamflow forecasts are made using a multiclass kernel‐based data‐driven support vector machine (SVM) model. The extended streamflow records based on tree ring reconstructions were used to provide a longer time series data. Reconstructed data were used from 1658 to 1952 and the instrumental record was used from 1953 to 2007. Reconstructions for oceanic‐atmospheric oscillations included the El Niño‐Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation. Streamflow forecasts using all four oscillations were made with one‐year to five‐year lead times for 21 gages in the western United States. This is the first study that uses both instrumental and reconstructed data of oscillations in SVM model to improve streamflow forecast lead time. SVM model was able to provide “satisfactory” to “very good” forecasts with one‐ to five‐year lead time for the selected gages. The use of all the oscillation indices helped in achieving better predictability compared to using individual oscillations. The SVM modeling results are better when compared with multiple linear regression model forecasts. The findings are statistical in nature and are expected to be useful for long‐term water resources planning and management.