小风条件下大气扩散模式发展回顾研究

由于小风对污染事件的潜在影响,以及与中等风速和大风条件相比边界层结构、湍流和扩散的特殊性,常规的稳态高斯烟流模式在这种条件下具有局限性,建立适用于小风条件的大气扩散模式具有重要的理论和实际意义。本文回顾了近年来小风条件下大气扩散模式的建立和发展,从较简单的解析模式到复杂的数值模式,并对不同模式的参数化方案、与示踪物实验的比较结果、适用性和不足之处进行了总结。综合考虑不同模式的优劣,在应用方面有利于根据实际情况选择合适的模式,在模式开发方面,有助于建立更加完备的大气扩散模式。     

Evaluating the Impacts of Environmental Flow Alternatives on Reservoir and Recreational Operations Using System Dynamics Modeling

Providing environmental flows is increasingly a management obligation in many water resource systems. Evaluating the impacts of environmental flow alternatives on other water uses in a basin can be a challenge, especially when collaborating with stakeholders. We demonstrate the use of system dynamics (SD) modeling to assess the impacts of four environmental flow alternatives in the Rio Chama, New Mexico. The model was developed to examine impacts of each alternative on reservoir storage and releases, hydropower production and revenue, and whitewater boating access. We simulated each alternative within a stochastic framework in order to explicitly incorporate hydrologic uncertainty into the analyses. The environmental flow alternatives were developed at a collaborative workshop of geomorphology, hydrology, and ecology experts. Results from the model indicate that the proposed flow recommendations on the Rio Chama will generally decrease annual reservoir storage, increase median flows, and have minimal impacts on hydropower production and whitewater rafting on the system. The Rio Chama case study is a promising example of how SD modeling can be used in the early stages of environmental flow studies and why it is compatible with collaborative modeling.     

People,protected areas and ecosystem services: a qualitative and quantitative analysis of local people's perception and preferences in Côte d'Ivoire

The long‐term integrity of protected areas (PAs), and hence the maintenance of related ecosystem services (ES), are dependent on the support of local people. In the present study, local people's perceptions of ecosystem services from PAs and factors that govern local preferences for PAs are assessed. Fourteen study villages were randomly selected from three different protected forest areas and one control site along the southern coast of Côte d'Ivoire. Data was collected through a mixed‐method approach, including qualitative semi‐structured interviews and a household survey based on hypothetical choice scenarios. Local people's perceptions of ecosystem service provision was decrypted through qualitative content analysis, while the relation between people's preferences and potential factors that affect preferences were analyzed through multinomial models. This study shows that rural villagers do perceive a number of different ecosystem services as benefits from PAs in Côte d'Ivoire. The results based on quantitative data also suggest that local preferences for PAs and related ecosystem services are driven by PAs' management rules, age, and people's dependence on natural resources.     

基于YOLOv3的电网作业人员安全帽佩戴检测

为了有效监测电网作业人员不规范佩戴安全帽行为,提出1种基于YOLOv3的电网作业现场安全帽佩戴检测方法.针对安全帽佩戴规范性问题,构建正确佩戴、不正确佩戴和未佩戴安全帽3种情况下的图像样本库;并利用该数据库对YOLOv3模型进行训练与测试,结合模型参数、样本比例及算法对比分析,开展电网作业人员安全帽佩戴检测算例.结果表...     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

Accident in a French dynamite factory: An example of an organisational investigation

The 27th of March 2003, an explosion caused the death of four employees in a Nitrochimie pyrotechnic plant, at Billy Berclau, in the north of France. Following the accident, the ministry of Ecology and Sustainable Development appointed INERIS to perform an investigation. According to the terms of reference, the investigation would cover technical (origins of the explosion, extent of damages) as well as organisational issues, as defined by SEVESO II safety management system requirements. This paper has a threefold purpose. It intends first to illustrate with an empirical case the current trend in safety auditing and accident investigation, targeting organisational factors, alongside human factors. There are not so many published cases of accidents analysed with an organisational perspective. Secondly, it shows that it is possible to investigate organisational dimensions (through articulation of safety engineering, safety management and human and social sciences) within reasonable time frames and a reasonable amount of resources. By focusing on key actors and asking appropriate questions related to key dimensions, investigating organisational accidents might not necessarily imply spending much more resources than other steps such as damage assessment, chronological construction or identification of technical scenarios, although there are also some prerequisite conditions needed to achieve this. Finally this paper should be seen as a technical communication beyond the pyrotechnic industry.     

Effects of seismic intensity and socioeconomic status on injury and displacement after the 2007 Peru earthquake

Earthquakes are a major cause of displacement, particularly in developing countries. Models of injury and displacement can be applied to assist governments and aid organisations in effectively targeting preparedness and relief efforts. A stratified cluster survey was conducted in January 2008 to evaluate risk factors for injury and displacement following the 15 August 2007 earthquake in southern Peru. In statistical modelling, seismic intensity, distance to rupture, living conditions, and educational attainment collectively explained 54.9 per cent of the variability in displacement rates across clusters. Living conditions was a particularly significant predictor of injury and displacement, indicating a strong relationship between risk and socioeconomic status. Contrary to expectations, urban, periurban, and rural clusters did not exhibit significantly different injury and displacement rates. Proxies of socioeconomic status, particularly the living conditions index score, proved relevant in explaining displacement, likely due to unmeasured aspects of housing construction practices and building materials.     

一种基于宏观经济指标的地震灾害快速评估方法及实现

利用Matlab软件及Office组件函数编写了基于宏观经济指标进行地震灾害快速评估并自动生成灾害简报的程序。该程序可以充分利用天津地区高密度强震台网资源,也可以计算理论烈度下的灾害评估结果。程序利用的基础数据资料主要是乡镇级别的人口及区县级别的GDP统计数据,数据更新维护经济可靠。通过与天津市"十五"地震应急评估系统触发的342个地震灾害评估结果对比,表明二者之间相关性非常强,而该程序计算速度则快一个量级以上。程序涉及的数据处理及计算方法适合于大城市及城市群的地震灾害快速评估。     

北京市水资源数据管理系统

介绍了《北京市水资源数据管理系统》的概况,以水资源、环境与社会经济活动信息为基础,采用计算机技术建立包括社会经济活动、水资源开发、城市排水、水污染环境质量等各类信息的具有空间特征的水资源宏观数据管理体系,并为政府有关部门提供可靠的决策依据。     

流动注射化学发光测定痕量亚硝酸根的研究

基于NO_2~-对I_2-EDTA光化学反应的抑制作用和I_2与鲁米诺的化学发光反应,建立了痕量亚硝酸根的流动注射化学发光分析方法.试验并选择了最佳条件,测定了天然水中亚硝酸根的含量,回收率在93—103.5％,方法线性范围为1.0×10~(-7)—4.0×10~(-6)mol.L~(-1),检测限为1.1×10~(-6)mol.L~(-1).对2.0×10~(-7)mol·L~(-1)NO_2~-进行测定,相对标准偏差为2.2％.