乌鲁木齐市民用住宅室内氡浓度水平调查研究

对乌鲁木齐市具有代表性的民用住宅室内氡浓度进行了连续一年的监测调查,调查结果显示室内氡浓度平均值55.4 Bq／m3,低于国标的“200 Bq／m3”,并据此估算出其所致居民的年均有效剂量为1.400 mSv,以及所致居民支气管上皮组织、肺、性腺、骨髓及骨表面细胞等关键组织的年吸收剂量分别为1886．49,378.96,2.80,3.20,3.20μGy／a,大于UNSCEAR2000年报告的世界平均值1.008 mSv,结合室内氡的主要来源,提出了对建筑材料进行放射性监测、控制装修、工程竣工后开展室内环境监测、加强室内通风以及开展室内氡污染治理等措施控制室内氡浓度水平。     

海南省废弃物处理温室气体排放估算研究

废弃物处理所释放的甲烷、二氧化碳和氧化亚氮是温室气体排放估算的组成部分之一.通过对海南省废弃物处理固体废弃物填埋、焚烧、生活污水以及工业废水过程中温室气体排放的估算,得出废弃物处理温室气体排放清单.2010年海南省城市废弃物处理温室气体排放以CO2当量计算总量为65.79万吨,同时进行了不确定性分析,不确定性约为30％,并提出拟采取的降低不确定性措施,为今后不断完善废弃物处理温室气体排放清单编制提供依据.     

固相膜萃取-气相色谱法测定地表水中有机氯农药研究

研究了固相膜萃取对地表水中有机氯农药(OCPs)物质的提取效果,并与液液提取法(LLE)进行比较.建立了固相膜萃取/气相色谱法检测地表水中20种有机氯农药的方法.结果表明:当萃取膜为HLB膜,洗脱液溶剂为丙酮和正己烷,萃取效果最理想,20种OCPs的回收率稳定在74.1%~94.3%之间,固相膜萃取法与传统液液提取法相比,既提高了萃取效率同时又减少了有机萃取溶剂的用量.该法检测实际样品时,同时加入2种内标指示剂对方法的性能进行了验证,2种内标示踪剂的回收率分别为71.6%~94.8%和69.9%~109.5%,样品中20种OCPs均未检出.     

恶臭污染评价方法及来源识别研究

随着经济的发展,人们的环境意识不断增强,恶臭作为一种环境污染已越来越受到人们的重视.文章概述了恶臭评价的主要影响因素,以及目前常用的评价方法如臭气强度评价法、臭气浓度(或臭气指数)评价法、臭气扩散模型预测评价法,同时从谱图恶臭污染源识别和源解析模型识别技术两方面介绍了恶臭污染物源解析技术,为恶臭污染事故诊断分析、定性恶臭污染物来源提供了一种新的途径和方法.最后通过分析目前尚未解决的科学问题,指出了今后的研究方向.     

基于两种样品前处理方法对比研究小冬克玛底冰川雪坑中痕量元素

通过对唐古拉山小冬克玛底冰川雪坑中非季风季节沉积的雪样分别进行酸化处理和消解处理后,利用高分辨扇形磁场等离子体质谱仪(ICP-SFMS)测试了样品中19种痕量元素(Ba、U、Sr、Rb、Tl、Mo、Cs、Pb、Sb、V、Cr、Mn、Fe、Co、Al、Cu、Ti、Li、As)的酸化浓度和总浓度.研究结果表明,痕量元素浓度的变化范围较大,元素Al的最大/最小浓度比为326(酸化浓度)和465(总浓度),元素Pb相应比值为27和48.雪冰中痕量元素的总浓度一般大于该元素的酸化浓度,其中,元素Pb、Fe、Sb、Ba、Al、Ti的酸化浓度占总浓度的平均比值分别为91%、76%、60%、52%、33%和21%.一般地,样品中不溶微粒含量越大,酸化浓度占总浓度的比值越小;不溶微粒含量越小,则相反.对痕量元素的富集系数(EF)分析表明,各元素总浓度EF均值小于酸化浓度EF均值,揭示了用酸化浓度计算EF存在对痕量元素人为来源影响的高估.人类排放是小冬克玛底冰川中痕量元素的来源之一,对于元素Mo和Sb,人类排放估计是主要来源.利用后向轨迹模型模拟出小冬克玛底冰川雪冰中痕量元素在非季风季节主要来源于青藏高原西部及中亚中东地区.     

HNO_3在气相及SiO_2表面的紫外光解反应

利用紫外及红外吸收光谱等分析手段对365 nm光照下HNO3在气相与SiO2表面的光解反应进行了研究.考察了HNO3浓度、光照时间、相对湿度等条件对反应的影响.结果表明:随着HNO3浓度及光照时间的增加,光解产生的NO2和NO浓度均呈指数增加;无水汽情况下,400 Pa的HNO3光解45 min后,产生NO2及NO浓度比气相光解产生的分别高约3及1.7倍.HNO3光解产生的HONO的浓度随相对湿度的增加而呈线性增加,在SiO2颗粒物表面光解产生的NO2浓度随着相对湿度的增加而减少,而NO浓度则随之增大.400 Pa的HNO3光照45 min后,SiO2表面光解产生的HONO浓度是气相光解的3倍、SiO2表面暗反应的约30倍.     

基于放射性碳分析的多环芳烃源解析:技术进展和准确性评估

化石燃料燃烧和生物质燃烧是污染物多环芳烃(polycyclic aromatic hydrocarbon,PAHs)的两大来源.放射性碳(14C)分析近年用于评估这两类源对环境中PAHs的相对贡献.此方法基于化石燃料和生物质的14C含量差异,即化石燃料不含14C,而生物质的14C浓度有一个较稳定值.14C的自然丰度极低(约10-12),因此检测PAHs这样的痕量污染物的14C含量一度极具挑战.1990年代中期,加速器质谱的技术突破使得对环境样品PAHs的14C分析具有实用价值.要准确测出PAHs的14C含量,须先从化学成分复杂的环境样品中分离出高纯度的PAHs.制备气相色谱因其出色的分离能力而成为目前环境样品PAHs14C分析必备的工具.本文意在简介基于14C分析的PAHs源解析的基本原理、技术进展,以及评估该方法获得的PAHs源解析结果的准确性.     

CFD-based assessment and visualization of the failure consequences of LPG tankers

This paper presents a simulation analysis of the explosions following an LPG leak and visualizes the consequences of the accident to reduce the consequences of the LPG leak explosion. Firstly, this paper proposes a CFD numerical simulation-based method for visualizing the consequences of LPG tanker failure. The method combines satellite maps and CFD numerical simulation data to visualize the consequences of LPG leaks and explosions, taking into account the influence of obstacles on the danger range of leaks and explosions; Secondly, this paper applies the method to a liquefied petroleum gas accident that occurred in the Wenling section of the Shenhai Expressway and performs CFD numerical simulation on the accident process and visualizes the consequences of the accident. Therefore, this method can provide a theoretical reference for the prior prevention of LPG accidents and the analysis of the consequences of accidents, as well as certain practical guidance instructive.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.