Five-wheel framework for system-based monitoring of heavy metal pollution in rivers

Sectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data-rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems-based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific- and policy-based tools would help sustainably manage the health of rivers, wildlife, and humans.     

Deliberative democracy meets democratised science: a deliberative systems approach to global environmental governance

The main achievements of the debates on deliberative democracy and democratised science are investigated in order to analyse the reasons, meanings and prospects for a democratisation of global environmental policy. A deliberative systems approach, which emphasises the need to explore how processes in societal spheres interact to shape the deliberative qualities of the system as a whole, is adopted. Although science plays a key role in this, its potential to enhance deliberative capacity has hardly been addressed in deliberative theories. The democratisation of science has potential to contribute to the democratisation of global environmental policy, in that it also shapes the potential of deliberative arrangements in the policy sphere. Deliberative arrangements within the policy sphere may stimulate the democratisation of science to different degrees.     

基于排队论的城市垃圾箱最佳间距研究

针对城市垃圾箱最佳间距的研究不够精细的现状,本文通过建立排队论模型得到了可供不同城市参考的城市垃圾箱最佳间距参数表。通过对部分城市街道的垃圾箱摆放间距情况进行实地调查,运用层次分析法将影响垃圾箱间距分布的多重因素转化为单因素;建立了基于指数分布的教学模型,利用排队论分析单因素状态下的垃圾箱间距分布,得到了求解城市垃圾箱最佳间距的一般方法。     

基于最优分割理论的酸雨监控点位的分类研究

Fisher最优分割法是用离差平方和来表示同类样本之间的差异程度,通过简便的计算步骤和作图,确定最优分类数,使同类样本间的差异最小,各类别样本间的差异最大,并用F检验法检验最优分类数的合理性。文章根据Fisher最优分割法的理论内涵,研究其应用于酸雨环境监测优化布点的可行性。以漳州市沿海区域监测点为研究实例,用Fisher最优分割法确定最优的分类情况和最优的优化布点方案。结果表明,原布设的9个监测点用Fisher最优分割法优化为3个典型点位,可客观地反映漳州市酸雨控制区的整体污染状况和污染趋势,该方法理论内涵直观、计算简便、重复性好,对环境质量监测有重要的意义。     

矿产资源规划环境影响评估方法及实例研究

矿产资源规划环境影响评价有利于矿产资源的可持续利用和矿区的环境保护,随着全国性矿产资源规划、地区性矿产资源规划的制定和实施,探讨适合于矿产资源规划环境影响评价的方法体系显得相当重要.本文从矿产资源的特点出发,结合实例研究,构建了由可持续目标和环境目标设立法、专家评判法、矩阵法、强度分析法和GIS技术方法等组成的矿产资源规划环境影响评价方法体系,定量地评价了矿区环境的现状和规划引起的环境变化,为我国矿产资源规划环境影响评估提供借鉴.     

Performance evaluation of process industries resilience: Risk-based with a network approach

Risk management can be defined as coordinated activities to conduct and control an organization with consideration of risk. Recently, risk management strategies have been developed to change the approach to hazards and risks. Resilience as a safety management theory considers the technical and social aspects of systems simultaneously. Resilience in process industries, as a socio-technical system, has four aspects of early detection, error-tolerant design, flexibility, and recoverability. Meanwhile, process industries' resilience has three phases: avoidance, survival, and recovery, determining the transition between normal state, process upset event, and catastrophic event. There may be various technical and social failures such as regulatory and human or organizational items that can lead to upset or catastrophic events. In the avoidance phase, the upset event is predicted, and thus, the system remains in a normal state. For the survival phase, the system state is assumed to be an upset process event, and the system tries to survive through the unhealthy process conditions or remains in the same state, probably with low performance. In the recovery phase, the system is supposed to be catastrophic, and the emergency barriers are prioritized to show the severity of the consequences and response time, leading to a resumption of a normal state. Therefore, a resilience-based network can be designed for process industries to show its inherent dynamic transition in nature. In this study, network data envelopment analysis (DEA), as a mathematical model, is used to evaluate the relative efficiency of the process industries regarding a network transition approach based on the system's internal structure. First, a resilience-based network is designed to consist of three states of normal, upset, and catastrophic events. Then, the efficiency of each industrial department, which is defined as decision-making units (DMUs), is evaluated using network DEA. As a case study, a refinery that is considered a critical process industry is assessed. Using the proposed model shows the efficient and inefficient DMUs in each of three states of normal, upset, and catastrophic events of the process and the projection onto efficient frontiers. Besides calculating the network efficiency, the performance of each state is extracted to precisely differentiate between DMUs. The results of this study, which is one of the fewest cases in the area of performance evaluation of process industries with a network approach, indicated a robust viewpoint for monitoring and assessment of risks.     

垃圾降解过程中脲酶测定条件优化

在土壤脲酶测定方法的基础上,以垃圾填埋场中的填埋垃圾为研究对象,通过单因素试验和正交试验对垃圾降解过程中脲酶活性测定方法进行优化。结果表明,垃圾降解过程中脲酶活性测定最优条件为:垃圾样品风干温度30℃,甲苯0.5mL,10%尿素1mL,0.1mol/LpH5.5柠檬酸钠缓冲液3mL,培养温度及培养时间分别为30℃和24h。优化后的的测定条件RSD小于0.82%,且测定值较传统方法提高30.48%～147.28%,表明该方法精密度高,可行性强。     

耐淋雨试验系统的研制

对自行研制的耐淋雨试验系统进行了介绍。设备研制中对试验系统的试验台升降及旋转系统、降雨强度调节系统和控制系统进行了优化设计,同时根据环境试验要求,指出了该试验设备研制过程中所遇到的淋雨滴雨合一、受试面转换和温度调控等技术难点,并给出了解决途径。该试验系统研制成功,为考核装备的环境适应性和产品可靠性提供了科学的试验手段。     

对复配菌群降解焦化废水优化条件的探讨

焦化废水属于含苯环的芳香类化合物和杂环化舍物的难降解有机废水。在正常条件下,难以取得很好效果,在复配茵群降解焦化废水生物处理工程中,只有在优良的环境条件下,复配菌群才能更好地生长、繁殖,从而达到最佳的处理效果。     

海洋硫酸盐还原菌群处理烟气脱硫废水

针对高盐硫酸根废水的特性,从海底沉积物中富集得到1个硫酸盐还原菌菌群SRB-2,并研究了盐度、温度、pH值、碳源、硫酸根浓度和铁形态对其活性的影响.结果表明,SRB-2为嗜盐中温硫酸盐还原菌群, 可以利用乙醇及乳酸为单一碳源,最佳生长温度为30～40℃,最佳生长pH值为7.4～8.3,SRB-2菌群能够在硫酸根浓度为5 200 mg/L或盐度为60　g/L的条件下正常生长,还原铁粉对该菌群还原硫酸根的能力具有加强作用,而二价铁离子则抑制细菌活性.扫描电子显微镜及光学显微镜对菌体形态研究发现,在反应器填料表面,该菌群中有大量短竿状及螺旋状细菌黏附,而在底部,主要为表面覆盖大量黑色粘稠物质,由短竿状细菌组成,推测其可能是菌种SRB-2-64（GenBank 序列号：EU167911）的堆积.