健康风险暴露评价研究进展

综述了国内外健康风险暴露评价的最新研究进展,重点讨论了对人体进行直接监测的生物监测技术和对环境中污染因子进行间接监测并利用数学模型进行暴露剂量计算的间接方法。生物监测方法通过测定人体生理介质(如血液、尿液)中的污染物质及其代谢产物含量确定人体对环境污染物的暴露情况,监测结果反映了风险因子通过所有暴露途径进入人体的总暴露剂量。为了利用生物监测结果评价人体暴露安全性,近几年建立了生物监测等效值的概念,推导确定化学物质的生物监测等效值发展迅速。环境监测和数学模型间接方法通过对不同暴露媒介中风险因子的浓度监测和特定暴露途径的量化研究,同时利用精确的暴露计算模型(如空气分散模型、地下水扩散模型)计算人体对污染物的暴露剂量。生物监测和环境监测技术及数学模型的发展使健康风险评价和管理的暴露参数更加精确,降低了风险评价的不确定性。还介绍了利用数学模拟和剂量重建等方法插补历史空白暴露数据的方法。     

大型排架施工安全监控机制研究

以大型排架为研究对象,研究排架从设计开始、搭设过程、排架使用过程、排架拆除等环节,从安全组织、技术措施、监督检查、安全许可等方面严格把关,对工程建设参建各方应该履行的安全生产管理职责、权限、工作内容及工作流程进行梳理和细化,设置控制节点,强化中间环节的安全检查,实现排架全生命过程的安全控制,研究成果以程序文件表现。大型排架施工过程监控程序文件中有工作流程8个,对应安全检查表10个,工作流程环环相扣,安全检查表间相互关联。通过实施大型排架施工过程监控程序文件,严格过程监督管理,保证高排架在搭设与使用中不发生安全生产事故,有效杜绝了类似于高排架垮塌伤人等群体性伤害安全事故。     

高含硫气田试气与地面工程建设交叉作业安全管理方法初探

普光气田作为高含硫气田在国内大规模开发,尚属首例,在气田开发过程中没有可借鉴的经验.高含硫气井试气作业中易发生井喷、硫化氢泄漏等事故.普光气田地面工程建设施工难度大、地形复杂,按照工程进度要求,试气作业和地面集输工程施工两项作业交叉时必须实现“安全作业、安全施工”,这是普光气田开发的基本目标.通过制订安全管理制度、明确责任主体、服从业主统一管理等有效的安全管理方法,保证了目标的实现,获得安全管理经验,对今后高含硫气田大规模开发具有指导意义.     

西安地铁车站深基坑变形规律FLAC模拟研究

开展西安地铁深基坑变形规律理论与监测的研究对指导西北地区深基坑信息化施工具有重要价值。本文以西安地铁2号线某车站深基坑工程为背景,完成了车站深基坑施工监测方案设计,对深基坑施工过程进行了FLAC计算模拟,重点研究了桩体变形、钢支撑轴力、基坑周边地表变形规律。结果表明,复杂环境下城市地铁车站站深基坑明挖施工时,现场监测是信息化安全施工的保证,采用钻孔灌注桩和钢支撑的复合围护方案作为车站深基坑的围护结构是合理的,土方分层开挖方式和钢支撑预应力施加是减少空间效应保证安全施工的重要措施。桩身水平位移特别是桩顶水平位移是围护结构变形特性的直接反映,围护桩变形最大的地方为基坑中部到三分之二基坑深度处。基坑围护结构附近的地面隆起量明显小于基坑中部的隆起量,随着开挖深度增大,隆起量逐渐由基坑中部最大转变为两边大中间小的型式。     

某市区重点行业职业病危害因素现状调查研究

对某市区重点行业15家企业职业病危害因素进行检测与评价,对普查结果进行综合分析。结果表明:15家企业工作场所化学有害因素职业卫生现状情况良好,但噪声危害程度较大;在6个重点行业中,电子制造行业有害因素合格率最低,为84%,其次是纺织行业,有害因素合格率为85%,其他化学原料及化学制品、化工、木制家具、石材加工四个行业合格率均较高;市直以上企业职业卫生现状情况优于区属企业,300-2000人之间较大规模的企业职业卫生现状情况优于300人以下的企业。同时,对企业提出了职业危害现状的改进措施与建议。     

普光高含硫气田企地三级应急联动模式研究和运用

普光气田属于高含硫气田,一旦发生事故对周边危害极大,该域地形复杂,山高路险,道路狭窄,人口密集,不利于疏散,普光应急管理模式面临挑战.为了解决这一难题,普光气田采用企地三级应急联动模式,旨在紧密联系企业和地方的应急疏散管理,明确应急疏散领导小组及职责,规定居民疏散信号、路线和安全集合点.通过培训与演练,本方案能够提高企地协同作战的应急疏散能力,最大限度地减少事件造成的人员伤亡、财产损失和社会危害.     

高含硫化氢井场公众疏散能力分析方法研究

我国含硫气田存在含硫量高、周边人口稠密、地形复杂等危险因素,如何保证含硫气田周边居民安全疏散成为一个重要的急需解决的问题.针对高含硫气田开发过程面临的公众疏散能力问题,采用毒性负荷判别法作为疏散能力评估的准则,提出一套分析流程,并给出相应的改进措施,形成一整套的复杂地形下高含硫化氢井场公众安全疏散能力分析方法;通过对本方法在现场实际中的应用,在理论和实例分析基础上,发现对高含硫气田井场进行疏散能力评估是可行且非常必要的.     

Numerical simulation of far-field blast loads arising from large TNT equivalent explosives

Large TNT equivalent explosions usually arise from accidents occurring during the transportation, storage, and manufacturing of chemicals relevant to process industries. The blast wave generated by the explosion will spread and interact with the surrounding factories and storehouses, damaging the building structures within several kilometers and causing significant casualties and property losses. This study aims to develop an efficient numerical simulation method to predict blast loads to estimate the consequences of accidents involving far-field free air bursts or surface burst explosions. Before its interaction with the interested target, a blast wave is generated in the numerical model by specifying the initial and boundary conditions of the disturbed air. Based on empirical data of incident overpressure, an explicit formula to calculate the air particle velocity is derived from the governing equations of a perfect inviscid gas. A simplified path line method is proposed to calculate the air density. The proposed method is applied to the LS-DYNA CESE solver to simulate the blast loads on building structures in the far field. Validations against empirical data and experiments indicate that the proposed method is sufficiently accurate for engineering applications and, through a case study, presents a more efficient performance than the LOAD_BLAST_ENHANCED (LBE) and mapping methods.     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.