孔石莼和重金属（铜与镉）对海水无机碳体系影响的模拟研究

通过室内实验模拟并应用海洋生态系统动力学模型,研究了孔石莼和重金属(铜与镉)共同作用下,海水二氧化碳体系及海洋碳源汇强度的变化过程.结果表明,二氧化碳体系各组分含量随时间的变化幅度(Δ)和趋势与重金属种类和添加浓度有关.与对照相比,低浓度(0.1μmol·L^-1和1μmol·L^-1)的铜、镉添加组中,DIC、HCO₃^-和pCO₂均随时间的延长呈下降趋势(p=0.01).而当铜、镉浓度大于“转折浓度”后,t=7时DIC、HCO₃^-和p_CO2均要大于初始值,其增幅随着重金属浓度的增加而增大.对于Δ由负值过渡到正值所对应的“转折浓度”,铜添加组要明显低于镉添加组.这与孔石莼对不同种类重金属的耐受程度不同有关.此外,当重金属浓度不同时,箱中的碳源汇格局亦随时间做不同的变化.当重金属浓度较低时,箱内水体表现为CO₂的汇,并且随着时间推移,可能过渡成为大气CO₂的源;高浓度重金属的影响下,箱内水体表现为CO₂的源,且随重金属浓度的增大,其向大气释放CO2的量也增加.     

Speciation and quantification of vapor phases in soy biodiesel and waste cooking oil biodiesel

This study characterizes the compositions of two biodiesel vapors, soy biodiesel and waste cooking oil biodiesel, to provide a comprehensive understanding of biodiesels. Vapor phases were sampled by purging oil vapors through thermal desorption tubes which were then analyzed by the thermal desorption/GC/MS system. The results show that the compounds of biodiesel vapors can be divided into four groups. They include methyl esters (the main biodiesel components), oxygenated chemicals, alkanes and alkenes, and aromatics. The first two chemical groups are only found in biodiesel vapors, not in the diesel vapor emissions. The percentages of mean concentrations for methyl esters, oxygenated chemicals, alkanes and alkenes, and aromatics are 66.1%, 22.8%, 4.8% and 6.4%, respectively for soy biodiesel, and 35.8%, 35.9%, 27.9% and 0.3%, respectively for waste cooking oil biodiesel at a temperature of 25 ± 2 °C. These results show that biodiesels have fewer chemicals and lower concentrations in vapor phase than petroleum diesel, and the total emission rates are between one-sixteenth and one-sixth of that of diesel emission, corresponding to fuel evaporative emissions of loading losses of between 106 μg l⁻¹ and 283 μg l⁻¹. Although diesels generate more vapor phase emissions, biodiesels still generate considerable amount of vapor emissions, particularly the emissions from methyl esters and oxygenated chemicals. These two chemical groups are more reactive than alkanes and aromatics. Therefore, speciation and quantification of biodiesel vapor phases are important.     

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.     

南京市不同功能区冬季大气PM2.5分布特征及其来源解析

2016年12月-2017年1月,在南京市4类典型功能区（农业区、住宅区、交通干道区、工业区）各选两点,共采集了大气PM_2.5样品32套,测定并分析了其质量浓度、9种水溶性离子（WSIs）、有机碳（OC）以及元素碳（EC）的含量.观测期间,南京市冬季PM_2.5的平均浓度为104.5 μg·m^-3,分布特征为：工业区（116.6 μg·m^-3）>农业区（104.3 μg·m^-3）>住宅区（100.1 μg·m^-3）>交通干道区（96.9 μg·m^-3）;WSIs、OC和EC的平均浓度（/PM_2.5）分别为：53.4 μg·m^-3（51.1%）、11.8 μg·m^-3（11.3%）、8.2 μg·m^-3（7.8%）.农业区和住宅区受WSIs污染较严重且NOR、SOR较高,而工业区和交通干道区的OC、EC污染较严重且SOC/OC较高.进一步运用PMF模型解析,南京市冬季PM_2.5来源为：二次源（37.3%）、工业源（31.2%）、交通源（16.4%）、建筑尘（7.9%）和燃煤源（7.2%）.最后,本文收集了自2000年起南京市冬季大气PM_2.5浓度及其污染来源研究,总体而言,近年来南京冬季大气PM_2.5浓度呈下降趋势,其主要污染源比重也发生了较大变化,燃煤贡献有所下降,而工业和交通排放逐渐上升,且二次污染贡献逐渐突出.今后,控制二次污染源将成为南京市大气PM_2.5治理的重中之重.