典型河谷城市儿童土壤与灰尘铅暴露风险

为定量评价河谷型城市土壤与灰尘铅对城市儿童健康的影响与风险,结合野外调查的基础上,以陕西渭河谷地典型工业城市宝鸡、西安、渭南和铜川为研究区域,以定量分析河谷型城市儿童环境铅暴露为核心,收集4个城市土壤与灰尘分析样品总计243个。利用X射线荧光光谱法测定了4个城市土壤与灰尘铅的含量;采用BCR连续形态分级法探究了城市土壤与灰尘重金属铅地球化学形态分布与迁移特征;根据US EPA污染物暴露与健康风险评价模型对城市儿童铅暴露进行了评价。研究结果表明渭河谷地典型城市宝鸡、铜川、西安和渭南城市土壤与灰尘铅浓度(X±SD)分别为(409.2±52.54)和(624.70±66.15)mg·kg-1、(357.47±41.37)和(592.60±36.78)mg·kg-1、(61.4±13.31)和(78.42±14.89)mg·kg-1、(46.71±12.11)和(64.7±13.76)mg·kg-1,均高出陕西省土壤铅背景值;污染水平依次为宝鸡铜川西安渭南。4个河谷型城市土壤与灰尘重金属铅发生整体迁移的趋势为:宝鸡城市灰尘(90.71%)西安城市灰尘(84.74%)≥宝鸡城市土壤(83.12%)渭南城市灰尘(74.89%)≥西安城市土壤(74.50%)铜川城市灰尘(72.49%)铜川城市土壤(57.50%)渭南城市土壤(53.79%)。可见,铅在均在城市灰尘中的迁移趋势远大于相应的城市土壤。4个城市土壤与灰尘均表现出较大的儿童暴露致癌风险,宝鸡和铜川城市儿童土壤与灰尘铅暴露也分别存在非致癌风险,风险程度依次为宝鸡铜川西安渭南。儿童铅暴露致癌与非致癌风险程度与其城市土壤和灰尘中铅可氧化态分布呈相似的规律,表明碱性的城市土壤与城市灰尘(p H7)中可氧化态铅可能是导致河谷型城市儿童铅暴露风险和儿童血铅污染的主要形态和因素。由此,城市燃煤排放与含铅制品的加工等活动可能是渭河谷地河谷型城市儿童血铅与铅暴露最主要的贡献源,必须采取长期有效的监测与控制措施。     

进出口工业化学品风险管理与风险评估进展

我国是世界化学品生产和贸易的大国。大量化学品的生产和进出口贸易给我国化学品风险管理和风险评估带来了巨大挑战。在借鉴发达国家和经济组织对化学品风险管理经验和评估技术的基础上,我国在近年来也逐步开展了化学品的管理和控制,以应对当前日益突出的化学品环境安全和健康安全事故。本文从我国化学品管理与国外化学品风险管理之间的对比出发,在梳理化学品管理政策发展的过程中,重点就我国环境保护部门对有毒和进口化学品、新化学物质和废弃危险化学品以及质检部门对进出口工业化学品的管理和控制措施,以及化学品的评估技术进行了综述。通过与发达国家就化学品风险管理与评估技术的对比,指出我国目前化学品管理和评估的不足与欠缺,并提出一些可行的应对措施与办法,为完善我国化学品风险管理和评估提供一定的参考。     

中国环境基准研究重点方向探讨

环境基准是环境标准的科学依据,在国家环境质量评价和风险管理体系中处于基础地位。它主要是依据特定对象在环境介质中的暴露数据,以及与环境要素的剂量效应关系数据,通过科学判断得出的,涉及环境化学、毒理学、生态学、流行病学、生物学和风险评估等前沿学科领域。国家环境基准研究是一个长期的系统工程,本文基于环境基准研究的学科特点和国际前沿,结合国家科技需求和相关领域的研究现状,综合分析并提出了未来中国环境基准研究的重点研究方向:1)环境基准的理论与方法学;2)环境基准基础数据库;3)基准目标污染物的筛选甄别和优先排序技术;4)水体营养物基准;5)生物测试与毒性评价技术;6)人体暴露评价理论与相关技术;7)环境基准的审核和校对;8)环境基准与标准转化理论及其对环境管理支撑技术。本文从环境基准学科发展的角度,阐述了与环境基准研究紧密相关的8个重点研究方面的国内外研究进展、关键科学问题以及未来重点研究内容。同时指出,这些重要的研究方向是环境基准研究的根本,未来环境基准的长期战略发展必将是建立在各个重要方向长足发展的基础之上,环境基准研究也必带动这些方向的共同蓬勃发展,为环境地球化学、毒理学、生态学等学科领域发展注入活力。     

利用N-乙酰-β-D-氨基葡萄糖苷酶含量评价农药对花翅摇蚊种群发育的影响

为了进一步探索种群水平的生态风险评估方法,本文利用β-N-Acetyl-D-glucosaminidase(NAGase)的变化量来监测农药对摇蚊种群发育的影响。从花翅摇蚊Chironomus kiiensis体内分离纯化得到电泳纯的NAGase,并通过免疫大白兔制得NAGase的多克隆抗体。运用间接非竞争ELISA法检测抗体特异性,结果表明其与共同存在于水体的一些生物的NAGase的交叉反应率为隆线溞4.41%、老年低额溞3.12%、多刺裸腹溞3.40%、中华薄壳介4.17%、日本沼虾3.23%、白纹伊蚊7.50%、小球藻0.5%。运用抗体测得毒死蜱、氰戊菊酯和阿维菌素3种杀虫剂对于摇蚊NAGase释放量的12 d-EC50分别为1.2012、0.0043和0.6281μg·L-1,以NAGase活力作为测试终点,测得相应的12 d-EC50:1.4765、0.0051和0.6756μg·L-1,两者差异不显著,但均显著低于以死亡作为测试终点的12 d-LC50:4.8171、0.0954和2.1340μg·L-1,且毒力大小均为氰戊菊酯阿维菌素毒死蜱。上述结果表明,利用NAGase多克隆抗体可以特异性地检测农药对花翅摇蚊种群发育的影响。     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

Dynamic-risk-informed safety barrier management: An application to cost-effective barrier optimization based on data from multiple sources

An integrated approach for performance assessment and management of safety barriers in a systemic manner is needed concerning the prevention and mitigation of major accidents in chemical process industries. Particularly, the effects of safety barriers on system risk reduction should be assessed in a dynamic manner to support the decision-making on safety barrier establishments and improvements. A simulation approach, named Simulink-based Safety Barrier Modeling (SSBM), is proposed in this paper to conduct dynamic risk assessment of chemical facilities with the consideration of the degradation of safety barriers. The main functional features of the SSBM include i) the basic model structures of SSBM can be determined based on bow-tie diagrams, ii) multiple data (periodic proof test data, continuous condition-monitoring data, and accident precursor data) may be combined to update barrier failure probabilities and initiating event probabilities, iii) SSBM is able to handle uncertainty propagation in probabilistic risk assessment by using Monte Carlo simulations, and iv) cost-effectiveness analysis (CEA) and optimization algorithms are integrated to support the decision-making on safety barrier establishments and improvements. An illustrative case study is demonstrated to show the procedures of applying the SSBM on dynamic risk-informed safety barrier management and validate the feasibility of implementing the SSBM for cost-effective safety barrier optimization.     

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.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

Methodology for Natech coupling risk assessment using correlative multi-criteria decision-making method

When a natural disaster occurs, it may damage multiple industrial facilities in a certain area at the same time, and the resulting Natech events may have an impact on the surrounding industrial facilities, generating coupling risk. In this study, the assessment of Natech events coupling risk is conducted using the method of correlated multi-criteria decision-making, and the knowledge of fuzzy measures is introduced to solve the uncertainty problem in Natech coupling risk. Natech Coupling Risk Index is constructed to involve physical and functional facilities. The concept of equivalent population is proposed to compare the risks generated by physical facilities and functional facilities. And economic indicators are added to calculate the comprehensive risk value. The purpose of this contribution is to enable local government managers to use their expertise and resources and the existing risk assessment of the plants themselves and rely on the scoring of experts limitedly to quickly and easily identify potential high Natech risk areas. In the calculation process of coupling risk, the government can also take the lead to promote information communication between different plants and other industrial subjects. The proposed method was applied in a realistic chemical industry area in Guangzhou, China and in a hypothetical town. The result shows that the physical risk may be transferred to the population and economy through the coupling between industrial facilities and the functional link between functional facilities and population and economy.     

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.