黔西南喀斯特地区红色风化壳的物源及元素迁移特征

南方喀斯特地区广泛分布着大量的红色风化壳,其成因及形成机制目前还存在争议。本文以贵州黔西南布依族苗族自治州泥凼石林的两处红色风化壳剖面(ND和YK)为研究对象,来探讨风化剖面上覆红土的物源问题,并计算其主、微量元素及稀土元素的迁移规律,认为本研究两处风化壳上覆红土为下伏基岩原位风化形成的,而且两剖面土壤均继承各自基岩的地球化学特征,并由于pH、风化强度等因素的影响表现出了元素迁移特征上的差异性。     

南昌市冬季大气PM2.5中重金属元素来源分析及健康风险评价

采集2015年南昌市冬季大气PM_(2.5)样品,利用电感耦合等离子体质谱仪(ICP-MS)测定样品中重金属(V、Mn、Cr、Co、Ni、Cu、Zn、Cd、Ba和Pb)的含量,分析重金属的分布特征和来源,并对重金属健康风险进行评价。结果表明:采样期间PM2.5浓度总平均值为(29.74±16.82)μg/m~3,其中省外办最高,武术学校最低;各重金属元素总体平均浓度从高到低次序为:ZnPbCuMnBaNiVCrCdCo。因子分析结果表明:PM_(2.5)中重金属元素的来源包括道路交通尘和冶金化工排放、机动车尾气以及混合源。健康风险评价结果显示:PM_(2.5)中Mn对人体健康存在非致癌风险,其他元素(Cr、Ba、Co、Pb、Cd、Cu、V、Zn、Ni)基本没有非致癌风险;Cr对人体有较明显的致癌风险,Cd、Ni和Co对部分年龄段的人群(尤其是成年人)存在一定的致癌风险。     

西安市文教区表层土壤中PAHs来源及风险分析

文教区土壤环境质量直接影响学生以及职工的身体健康。本研究应用高效液相色谱仪对采集的西安市文教区表层土壤样品中的16种优控多环芳烃(PAHs)进行含量检测,分析其组分特征、来源及健康风险。结果表明,西安市文教区表层土壤中∑PAHs含量为0.290~4.147μg/g,平均值为1.515μg/g,7种致癌多环芳烃的含量为0.079~2.093μg/g,均值为0.593μg/g,土壤PAHs污染较为严重。其中4环的高环PAHs为土壤PAHs污染的主要物质,平均占∑PAHs含量的40.72%。源解析结果表明西安市文教区表层土壤中PAHs主要来源于石油燃烧、煤及生物质等的不完全燃烧。终生癌症风险评价表明西安市文教区表层土壤中PAHs污染对其生活在周围的人群产生的终生致癌风险性较小,但71.4%的样点达到严重污染水平,产生的间接影响应引起足够重视。     

有机硅改性环氧树脂的光固化动力学与性能研究

目的研究紫外光固化有机硅改性环氧树脂的固化行为和性能。方法通过介电分析(DEA)研究光引发剂、热引发剂及有机硅含量对紫外光固化脂环族环氧树脂反应过程的影响,利用热重(TG)、差示扫描热(DSC)和显微硬度仪对有机硅树脂改性环氧树脂性能进行分析。结果发现光引发剂与热引发剂对固化效率可起到协同互补的作用,增加光引发剂和热引发剂的浓度,可缩短引发时间,加快固化速率,提高固化效率。与纯环氧树脂相比,有机硅改性环氧树脂的固化效率和初始分解温度都有所下降,但高温阶段降解速率明显降低,500℃的残炭率也得到提高。当加入质量分数为10%的有机硅时,固化物表现出较好的耐热性能,树脂维氏硬度可达31.75HV。结论紫外光可以很好地固化有机硅改性环氧树脂。     

盐酸萘乙二胺比色法测定大气中硝基苯、苯胺浓度的改进

本文探讨了盐酸萘乙二胺比色方法测定大气中硝基苯、苯胺浓度的最佳反应条件,如酸度、试剂加入量、反应温度、反应时间等。简化了过滤、洗涤操作,精密度高、显色稳定。     

Analysing the sensitivity behaviour of two hydrology models

Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects) and the extent these parameters interact with each other (second-order effects). This method requires the specification of two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock.     

环境样品中 PCBs 的测定

通过碱解、酸洗、萃取、净化、浓缩、脱硫等步骤处理实际样品,利用毛细管柱GC/ECD进行PCBs的定性和定量.方法定性准确,定量良好,具有较高的回收率.各同系物的方法检出限,水样为0.01～0.08ng/L;土壤样为0.003～0.03μg/kg,可以应用于土壤、沉积物、水样、油样等环境样品中PCBs的测定     

大气环境质量综合评价加权灰色关联模型的建立与应用

利用灰色理论建立大气环境质量综合评价加权灰色关联模型 ,运用污染贡献率确定权重系数。与其它评价模型相比 ,加权灰色关联模型还具有排序功能 ,实例表明该模型是简便有效的     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

在线自动监测仪与实验室国标方法测定地表水中总氮的比对分析

随着监测技术的发展,地表水中越来越多的污染物可采用自动监测仪器实现实时监测。采用自动监测与实验室国标方法测定地表水中总氮的结果是否可比以及造成差异的原因,文章根据实际样品比对实验对此进行了探讨。