化工火灾事故调查方法研究

为克服化工火灾调查中人为因素的影响,增强事故调查的科学性和准确性,在总结化工火灾现场特点的基础上,提出化工火灾事故调查时应遵循的假设推理法,通过证据收集、分析资料、建立假设、检验假设和选择假设,构建化工火灾事故调查的证据系统,包括实物证据,位置证据、文件证据和人证,从火灾动力学角度分析了火灾痕迹的影响因素。结果表明:假设推理法重视证据收集,综合使用归纳推理和演绎推理,有助于避免主观判断的影响;采用系统方法收集证据,扩大证据收集的范围,列举证据清单和优先考虑敏感性证据的方法有助于提高调查效率;火灾行为、材料的热响应和灭火行动对火灾痕迹的形成有直接影响,分析时应充分考虑,以免作出错误的判断。     

建筑安全事故应急措施推理研究

为了加强对建筑安全事故历史案例经验的利用,提高建筑安全事故应急管理的信息化水平,对如何利用智能化技术制定事故应急措施进行研究。将规则推理引入案例推理中,建立了建筑安全事故应急措施推理模型。通过计算综合相似度,在案例库中检索出相似案例的基础上,根据关键属性运用规则推理对相似案例的应急措施进行适用性修改。计算特征属性权重时,利用灰色关联分析法对传统粗糙集方法进行改进,对冗余属性提出了灰色重要度的概念,避免了冗余属性权重为0的情况。选取基坑事故案例对模型进行实例分析,结果表明:建筑安全事故应急措施推理模型具有普遍适用性,可应用于事故应急管理。     

Aliphatic and polycyclic aromatic hydrocarbons in the bottom sediments from Klaipėda Harbour,Lithuania (Baltic Sea)

A total of 22 bottom surface sediment samples were collected from Klaip?da Harbour, Lithuania (Baltic Sea). Sediment parameters, such as grain size and concentrations of total organic carbon, aliphatic hydrocarbons (ALHs) and polycyclic aromatic hydrocarbons (PAHs), were determined. Hydrocarbon contamination levels in the sediments were estimated based on classifications provided in the literature, and potential biological effects were assessed based on the effects range low–effects range median values of the Sediment Quality Guidelines and on the total toxic benzo(a)pyrene equivalent. The results demonstrated that concentrations of PAHs and total aliphatic hydrocarbons in the sediments varied between 1.6 and 5456?ng?g^?1 d.w. and 6.9 and 727?μg?g^?1 d.w., respectively. In most cases, the concentrations of ALHs and PAHs in the sediments indicated low to moderate levels of pollution and were not observed to have any significant adverse effects on living biota. To determine possible sources of hydrocarbons, hydrocarbon compositions were analysed, and ALHs’ and PAHs’ specific diagnostic ratios were calculated. According to the results, the Klaip?da Harbour bottom sediments were dominated by PAHs of pyrogenic origin, whereas the origin of ALHs was mixed, that is, anthropogenic with a biogenic (aquatic and terrestrial) input.     

Diagnosis of famine: a discursive contribution

This paper recommends the incorporation of an additional discursive dimension in famine diagnosis that draws on the number of reports referring to famine in the United Nations Office for the Coordination of Humanitarian Affairs (OCHA)'s ReliefWeb database. Present‐day diagnostic tools already apply the principle of triangulation with multiple indicators; the addition of a discursive diagnostic dimension would enable even more refined analysis, allowing more forcefully for the incorporation of the aspect of change. The newly devised discursive famine indices are used both to identify famines—in Ethiopia (2000), Malawi (2002), and Somalia (2011)—and to analyse key socioeconomic determinants of famine. The study finds that income (or poverty) together with state fragility appear to be the major determinants of cross‐country variations in famine reporting, while political regimes do not appear to have any independent effect. The indices appear largely robust with regard to concerns about cross‐country, semantic, and temporal biases.     

地铁施工风险应急管理研究

针对近年来地铁施工事故频发,难以快速、有针对性地进行应急处置的问题,通过对近年来地铁施工事故案例的统计,分析导致地铁施工事故的相关因素,用Protege软件对以往地铁施工事故应急案例建立知识本体模型。通过目标案例与本体库中的源案例进行相似度计算与匹配,检索出案例库中相似度超过阈值的源案例,然后再对比筛选后的源案例与目标案例之间的关键属性相似度,从而决定进行规则推理或直接参考该案例的应急措施,以此提高应急处置的速度与针对性。     

南京冬季市区和郊区气溶胶中PAHs浓度的昼夜特征及粒径分布

为了研究南京市区与郊区气溶胶中多环芳烃(PAHs)污染状况和分布特征,利用气-质联用仪(GC-MS)分析了2010年1月1～10日日间和夜间分别在南京大学和南京信息工程大学采集的气溶胶样品,得到南京市区与郊区17种PAHs浓度,总浓度分别为41.36～220.35 ng.m-3和45.10～200.86 ng.m-3,其中约66%～67%分布于细粒子(Dp≤2.1μm)中.研究发现,南京市区和郊区气溶胶中PAH总浓度均处于较高的水平;但两者昼夜变化趋势不同,即市区PAH总浓度日间高于夜间,郊区PAH总浓度日间低于夜间.主导风向的改变和高压天气系统对PAH浓度变化影响较大;在市区其影响主要表现在细粒子部分,而郊区主要表现在粗粒子部分.市区和郊区不同环数的PAHs粒径分布不同;2～3环PAHs,郊区含量高于市区;而4～6环PAHs,市区含量高于郊区.高环数(4～6环)PAHs在粗模态出现较大浓度峰可能是由于南京地区粗模态气溶胶中碳含量较高.市区和郊区相似的特征比值说明两者的PAHs具有相同污染来源,主要为生物质及煤的燃烧和汽车尾气,表明南京市区PAHs受到郊区工业源排放影响较大.     

典型化工集中区环境空气SVOCs污染特征及来源解析

于冬春两季在华东3个典型石化化工集中区设置环境空气观测点,利用PUF大气被动采样技术(PUF-PAS)采集大气中半挥发性有机化合物(SVOCs),使用气相色谱-质谱联用仪(GC-MS)进行分析.获得59种SVOCs的浓度,包括25种多环芳烃(PAHs)、24种正构烷烃及10种藿烷,并结合主成分分析和特征比值法解析PAHs来源.结果表明:①各观测点正构烷烃贡献率最高,其次是PAHs,分别超过60％和30％;②根据各化合物冬春季浓度变化并结合风向进行分析,推测正构烷烃C18、C29 αβ-藿烷和C30αβ-藿烷与石油化工排放有关;③PAHs单体以菲(Phe)、荧蒽(Fla)、萘(Nap)、芴(Flu)和芘(Pyr)为主,合计占比高达90.0％;④主成分分析显示观测点PAHs主要来自化石燃料燃烧、机动车尾气和石化工艺排放等,3类来源对PAHs的贡献率分别为56.0％、19.2％和8.6％,基于特征比值法的PAHs来源解析予以了验证.     

自然风化条件下原油中金刚烷的风化规律

以渤海2个原油作为研究对象进行自然风化实验,探讨了原油中金刚烷化合物的分布情况及其风化规律.结果表明,经过100 d的室外自然风化,原油中金刚烷的分布已经发生了较大的改变：风化损失程度与金刚烷化合物的沸点有关,风化初期单金刚烷损失严重,风化45 d,单金刚烷完全消失;双金刚烷浓度变化较小,有较好的抗风化稳定性;通过t检验分析,所选取诊断比值中,单金刚烷指标A6、A8、A12和双金刚烷指标A16、A17、A18较为稳定,能很好地指示自然风化20 d内油样的来源;双金刚烷指标A17、A18风化100 d非常稳定,可用于长期自然风化油样来源鉴别,金刚烷化合物诊断比值对于溢油鉴别具有重要的意义.     

天津城区夏冬季典型污染过程中BTEX变化特征及其健康风险评估

利用GC955在线气相色谱仪分别于2019年7月和2020年1月在天津市区开展苯系物(BTEX,包括苯、甲苯、乙苯、间/对-二甲苯和邻-二甲苯)实时在线观测,对典型污染过程中BTEX的浓度水平、组成及演化机制进行了研究,并运用特征物种比值法对BTEX的来源进行了定性分析,最后运用US EPA的人体暴露分析评价方法对BTEX健康风险进行评估.结果表明,臭氧和霾污染过程中BTEX体积分数平均值分别为1.32×10^-9和4.83×10^-9,其中苯的体积分数占比最大,其次是甲苯、乙苯和二甲苯占比最小.2020年1月BTEX体积分数很大程度上受到西南方向短距离传输的影响,而在2019年7月BTEX浓度受到本地排放的影响.BTEX浓度水平在2019年7月受到温度和相对湿度的共同影响,而在2020年1月当温度较低时BTEX浓度对相对湿度的变化更敏感.天津市区BTEX在霾污染过程中受生物质燃烧/化石燃料燃烧/燃煤排放的影响较大,而在臭氧污染过程中除了受到燃烧排放源影响,交通源排放在很大程度上也有影响.臭氧污染和霾污染过程中BTEX的HI分别为0.072和0....     

Characterization of aliphatic hydrocarbons in deep subsurface soils near the outskirts of Beijing, China

Thirty-nine deep subsurface soils( 150—180 cm depth) near the outskirts of Beijing were investagated. The concentrations including n-alkanes from C13 to C36, pristane and phytane were in the range of 0.60 to 170.10μg/g, with a median value of 4.26. Carbon preference index values for n-alkanes ranged from 1.08 to 2.98, with a median value of 1.48. The percentage contribution of “wax” nalkanes was in the range of 6.03%--46.22%. A predominance of odd/even carbon n-alkanes and unresolved complex mixtures with different shapes and ranges were frequently observed. Factor analysis reduced the data set into three principal components and confirming contributions from low ( 19.58% ), medium ( 20.49% ) molecular weight species and long-chain n-alkanes (43.41% ), respectively.Molecular biomarkers such as pristane, phytane, hopanes and steranes were detected. Based on the principal component analysis, the concentration profiles and molecular markers, it was found that the aliphatic hydrocarbons were from both biogenic and anthropogenic sources.