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。     

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。     

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.     

How can process safety and a risk management approach guide pandemic risk management?

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.     

A human-centered framework for innovation in conservation incentive programs

The promise of environmental conservation incentive programs that provide direct payments in exchange for conservation outcomes is that they enhance the value of engaging in stewardship behaviors. An insidious but important concern is that a narrow focus on optimizing payment levels can ultimately suppress program participation and subvert participants’ internal motivation to engage in long-term conservation behaviors. Increasing participation and engendering stewardship can be achieved by recognizing that participation is not simply a function of the payment; it is a function of the overall structure and administration of the program. Key to creating innovative and more sustainable programs is fitting them within the existing needs and values of target participants. By focusing on empathy for participants, co-designing program approaches, and learning from the rapid prototyping of program concepts, a human-centered approach to conservation incentive program design enhances the propensity for discovery of novel and innovative solutions to pressing conservation issues.     

非对称开采条件下工作面“多向应力”变化特征研究

在非对称开采条件下,工作面受上覆岩层自重应力、超前支承应力、采空区侧向支承应力和回风巷煤柱应力等“多向应力”叠加影响,使得工作面应力呈“非对称”性。为研究非对称开采条件下工作面“多向应力”变化特征,基于微震监测、应力在线监测和理论计算,对母杜柴登煤矿30202工作面回采过程中所形成的非对称开采条件下的应力变化进行分析;并基于工作面所受应力条件和围岩体结构条件,分析了“多向应力”叠加显现机理。结果表明:30202工作面回采期间,煤柱支承应力沿走向分为应力升高区、应力明显降低区、应力缓慢降低区和应力稳定区,其应力峰值主要集中在工作面前方40 m左右,应力集中系数平均为1.61;在非对称开采阶段,在走向方向工作面超前支承应力影响范围较回采初期增加了100 m左右;在不考虑垂直应力影响的情况下,相邻工作面采空区的侧向应力对30202工作面倾向方向的影响范围为44 m,应力最大值为56.1 MPa。在“多向应力”耦合作用下工作面在回采过程中产生能量集聚,并在采动扰动下发生能量释放,满足了大能量事件发生的基本应力条件;同时在扰动条件下采空区发生高位顶板错动,以及围岩支护薄弱为大能量事件的发生提供了围岩结构条件。研究结果可为工作面非对称开采条件下采场矿压显现规律研究、顶板控制和巷道支护设计提供指导。     

农牧交错区土地荒漠化与生物多样性研究--以宁夏盐池县为例

采用α-多样性指数即Shannon-Wiener指数、Simpson指数、群落均匀度指数Jsw和Js和β-多样性指数(相似性系数Cs)研究宁夏盐池县草原群落在不同荒漠化治理措施下(人工封育、退耕还林、撂荒)生物多样性变化.2002和2003年固定样地观测数据表明,到2003年,各样地的α-多样性指数均有不同程度的提高,以撂荒地和退耕还林地α-多样性变化较大.Simpson指数、Js指数变化最为明显,分别平均增加了140%、109%,Shannon-Wiener指数、Jsw指数次之,分别增加67%、49%.人工封育不同处理区α-多样性指数也有较大变化,由核心＞边缘＞外围,变为边缘＞核心＞外围排列次序.β-多样性的研究结果同样表明,各样地的种类组成发生了一定的变化.文章还进一步分析了引起生物多样性变化的主要原因.