A fuzzy Bayesian network approach to improve the quantification of organizational influences in HRA frameworks

Organizational factors are the major root causes of human errors, while there have been no formal causal model of human behavior to model the effects of organizational factors on human reliability. The purpose of this paper is to develop a fuzzy Bayesian network (BN) approach to improve the quantification of organizational influences in HRA (human reliability analysis) frameworks. Firstly, a conceptual causal framework is built to analyze the causal relationships between organizational factors and human reliability or human error. Then, the probability inference model for HRA is built by combining the conceptual causal framework with BN to implement causal and diagnostic inference. Finally, a case example is presented to demonstrate the specific application of the proposed methodology. The results show that the proposed methodology of combining the conceptual causal model with BN approach can not only qualitatively model the causal relationships between organizational factors and human reliability but also can quantitatively measure human operational reliability, and identify the most likely root causes or the prioritization of root causes causing human error.     

Air pollution and body burden of persistent organic pollutants at an electronic waste recycling area of China

Environmental Geochemistry and Health - This paper reviews the concentrations of persistent organic pollutants (POPs) in atmosphere of an electronic waste (e-waste) recycling town, Guiyu, in...     

Effect of composition and microstructure of humic acid on 3,3′,4,4′-tetrachlorobiphenyl sorption

Environmental Science and Pollution Research - The fate and transport of polychlorinated biphenyls (PCBs), a class of persistent organic compounds, in soils was markedly affected by their...     

基于GM（1,1）模型的秦皇岛市大气污染物SO2预测

根据2004—2009年大气中SO2污染物的监测数据,通过灰色GM(1,1)模型预测了未来6年秦皇岛市大气中SO2的变化趋势。结果显示,灰色系统GM(1,1)模型合理,精度较高,相对误差为-1.875%～1.228%,与环保部门公布的数据吻合程度较好。     

Estimating monthly total nitrogen concentration in streams by using artificial neural network

Artificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system. Here, the feed-forward ANN model was used to investigate the relationship among the land use, fertilizer, and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC). It was shown by the sensitivity analysis, that precipitation, temperature, river discharge, forest area and urban area have high relationships with TNC. The ANN structure having eight inputs and one hidden layer with seven nodes gives the best estimate of TNC. The 1:1 scatter plots of predicted versus measured TNC were closely aligned and provided coefficients of errors of 0.98 and 0.93 for ANNs calibration and validation, respectively. From the results obtained, the ANN model gave satisfactory predictions of stream TNC and appears to be a useful tool for prediction of TNC in Japanese streams. It indicates that the ANN model was able to provide accurate estimates of nitrogen concentration in streams. Its application to such environmental data will encourage further studies on prediction of stream TNC in ungauged rivers and provide a useful tool for water resource and environment managers to obtain a quick preliminary assessment of TNC variations.     

Explosions and deflagration-to-detonation transitions in epoxy propane/air mixtures

The explosion and deflagration-to-detonation transition (DDT) in epoxy propane (E.P.) vapor/air mixture clouds under weak ignition conditions has been studied in an experimental tube of diameter 199 mm and length 29.6 m. E.P. vapor clouds were formed by injecting liquid E.P. into the experimental tube and evaporating of the fine E.P. droplets. The dimension and the evaporating process of the E.P. droplet were measured and analyzed. The E.P. vapor/air mixture clouds were ignited by an electric spark with an ignition energy of 40 J. The characteristics and the stages of the DDT process in the E.P. vapor/air mixtures have been studied and analyzed. A self-sustained detonation wave formed, as was evident from the existence of a transverse wave and a cellular structure. Moreover, a retonation wave formed during the DDT process in the E.P. vapor/air mixture. The influence of the E.P. vapor concentration on the DDT process has been studied. The minimum E.P. vapor concentration for the occurrence of the DDT in the E.P. vapor/air mixture has been evaluated and the variation of DDT distance with E.P. vapor concentration has been analyzed.     

Cu、Cd污染对土壤脲酶活性的影响研究

随着重金属污染的越来越重,重金属对土壤环境的危害引起人们广泛的关注,土壤酶作为土壤生态系统中生化反应的催化剂,对各种土壤代谢过程起驱动作用.为研究单因素重金属Cu、Cd对于土壤脲酶活性的影响,以至于对环境保护和环境评价工作提供意见.本文采用靛酚蓝比色法测定脲酶活性,以氨氮浓度表征土壤脲酶活性.实验结果表明,随着Cu2＋、Cd2＋浓度的增加,土壤脲酶活性显著降低,即重金属对土壤脲酶具有抑制作用.     

贵州省境内乌江干流水质现状分析

根据贵州省环境监测部门资料显示乌江已成为贵州省水污染程度最为严重的流域,突出的问题是总磷、氟化物超标。本文用最小二乘法模型和t检验方法对贵州省境内乌江干流2006—2013年水质状况进行分析,结果表明乌江干流总磷、氨氮、氟化物,高锰盐指数浓度变化趋式为"无趋势",乌江干流水质稳定,并提出流域污染防治的对策和建议。     

东三旗台Sacks体应变干扰分析与震例研究

本文对北京市昌平区东三旗台Sacks体应变观测值与固体潮、气压、水位、降雨关系进行了初步分析。通过干扰因素分析,为提高地震预报的准确性提供了参考依据。同时,进行了有关震例研究,希望通过总结震例,为今后利用Sacks体应变观测探索地震预报实践建立科学的分析处理方法。