A framework for allocating conservation resources among multiple threats and actions

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource allocation to address multiple threats when multiple management options are available, including mutually exclusive options. Formulating the decision as a combinatorial optimization problem, our framework takes as inputs the expected impact and cost of each threat for each action (including do nothing) and for each overall budget identifies the optimal action to take for each threat. We compared the optimal solution to an easy to calculate greedy algorithm approximation and a variety of plausible ranking schemes. We applied the framework to management of multiple introduced plant species in Australian alpine areas. We developed a model of invasion to predict the expected impact in 50 years for each species-action combination that accounted for each species’ current invasion state (absent, localized, widespread); arrival probability; spread rate; impact, if present, of each species; and management effectiveness of each species-action combination. We found that the recommended action for a threat changed with budget; there was no single optimal management action for each species; and considering more than one candidate action can substantially increase the management plan's overall efficiency. The approximate solution (solution ranked by marginal cost-effectiveness) performed well when the budget matched the cost of the prioritized actions, indicating that this approach would be effective if the budget was set as part of the prioritization process. The ranking schemes varied in performance, and achieving a close to optimal solution was not guaranteed. Global sensitivity analysis revealed a threat's expected impact and, to a lesser extent, management effectiveness were the most influential parameters, emphasizing the need to focus research and monitoring efforts on their quantification.     

Study of lignocellulosic biomass ignition properties estimation from thermogravimetric analysis

In the last decade, the use of renewable resources has increased significantly in order to reduce the energetic dependence on fossil fuels, as they have an important contribution to the global warning and greenhouse gasses effect. Because of that, research on biofuels has been increased in the last years as its characteristics of use match those of the conventional fuel's: solid biomass can be used instead of coals, and biodiesel could replace diesel. Research on solid biomass ignition properties has been considerably developed because of the amount of industrial accidents related to the treatment and use of solid biomass (self-ignition, dust explosions, etc.). On the other hand, thermogravimetric analysis (TGA) is becoming and important characterization technique as it can be used to determine a wide spectrum of properties, such as kinetics, composition, proximate analysis, etc. This research aims to combine thermal analysis and ignition properties, by using the TGA to obtain the elemental composition of lignocellulosic biomass and compare those results to Minimum Ignition Energy (MIE) values test output, so a relation between composition and MIE can be found.To achieve this aim, biomass samples from different origins have been used: oil palm wastes (empty fruit bunches, mesocarp fiber and palm kernel shell), agricultural wastes (straw chops) and forestry wastes (wood chips and wood powder). Also, raw materials and torrefied biomass were compared. The hemicellulose/cellulose ratio was calculated and compared to different flammability properties, finding out that the greater the ratio and the lower the onset temperature (temperature at which the pyrolysis reaction accelerates), the lower was the minimum ignition energy. From this basis it was possible to define “tendency areas” that grouped the samples whose MIE values were similar. Three tendency areas were found: high minimum ignition energy, medium minimum ignition energy, and low ignition energy.     

Quantifying the impact of vegetation-based metrics on species persistence when choosing offsets for habitat destruction

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area × habitat suitability; and condition × habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss.     

A proactive process risk assessment approach based on job hazard analysis and resilient engineering

Complex systems often experience a long period of incubation before accidents occur. Therefore, a proactive risk assessment is essential for process safety. The conventional job hazard analysis (JHA) method has been an effective tool to conduct a process risk assessment in the high-risk industrial field. However, the conventional JHA is inadequate for the proactive risk assessment since it is usually conducted during and before one specific operation process. Operations such as startup and maintenance are performed repeatedly on the lifecycle of a plant. Therefore, the risk reduction measures for the industrial process should include not only preventive actions obtained from the conventional JHA but also recovery ones. Resilience engineering (RE) has proven to be helpful for the recovery analysis of a complex system. The objective of this paper is to propose a proactive and comprehensive process risk assessment approach based on JHA and RE. The mechanism of applying RE to address operation process risk is illustrated. The integrated approach can provide guidelines to establish proactive risk reduction measures as well as maintain a low-risk level. Finally, a gas transmission startup process risk assessment case is presented to demonstrate its applicability.     

两种有机锌络合物处理棉花的霉变及热解行为研究

棉花是纺织业的重要原料,是人民群众生活不可或缺的必需品,同时也是我国进出口重要的商品。研究如何安全有效地进行棉花的储备具有十分重要的现实意义。棉纤维本身含有脂肪、蜡质和果胶等适合微生物生长繁殖的营养物质。在棉花储备中,高的回潮率会加速微生物的繁殖,进而产生热量。热量的累积会引起温度升高以及棉花霉变,不利于棉花的安全有效储存。因此,通过静电吸附法将安全无毒的有机锌络合物附着在棉纤维表面,研究表明,相同条件下,处理棉的霉变状况明显得到抑制。加速发霉条件下,未处理棉的相对于白纸的平均色差值为28.10,而双乙酸锌以及苯甲酸锌防霉处理棉的色差值分别为5.16和5.86,下降了81.6%和79.1%。自然发霉条件下,双乙酸锌以及苯甲酸锌防霉处理棉的色差值分别下降了53.8%和50.7%。同时研究了纯棉以及处理棉氮气下的热分解动力学,相比于未处理棉,双乙酸锌防霉处理后活化能下降了15.8%,而苯甲酸锌防霉处理后活化能下降了10.9%。此外,利用实时红外和热重红外联用技术得到了样品在热解过程中固相以及气相的裂解产物的红外谱图,发现防霉处理能一定程度上抑制棉花热解。     

Abundance and removal of antibiotic resistance genes (ARGs) in the rearing environments of intensive shrimp aquaculture in South China

Although research regarding antibiotic resistance genes (ARGs) in aquaculture environments has gained increasing scientific interest, further studies are required to understand the abundances and removal mechanisms of ARGs during the entire rearing period of shrimp aquaculture. Thus, in this study, abundances, distributions and removal rates of ARGs in different environmental compartments of intensive shrimp farms in South China were investigated during the entire rearing period. The results indicated that sul1 and cmlA were the predominant ARGs in the water and sediment samples. Additionally, the total abundance of ARGs was higher in shrimp pond water than in the source water and farm effluent. Moreover, sediment samples indicated significantly higher ARG abundances than water samples from the shrimp ponds (P?<?0.05). Environmental factors were found to significantly affect the distribution of ARGs in shrimp rearing environments. Furthermore, stable ponds aided the removal of ARGs from shrimp pond water. This study accounted for temporal variations in ARG abundances as well as removal of ARGs in different environmental compartments during the entire shrimp rearing period. However, additional research is required to optimize the water treatment process for removal of ARGs from the aquaculture.     

Key considerations and challenges in the application of social-network research for environmental decision making

Attempts to better understand the social context in which conservation and environmental decisions are made has led to increased interest in human social networks. To improve the use of social-network analysis in conservation, we reviewed recent studies in the literature in which such methods were applied. In our review, we looked for problems in research design and analysis that limit the utility of network analysis. Nineteen of 55 articles published from January 2016 to June 2019 exhibited at least 1 of the following problems: application of analytical methods inadequate or sensitive to incomplete network data; application of statistical approaches that ignore dependency in the network; or lack of connection between the theoretical base, research question, and choice of analytical techniques. By drawing attention to these specific areas of concern and highlighting research frontiers and challenges, including causality, network dynamics, and new approaches, we responded to calls for increasing the rigorous application of social science in conservation.     

官厅水库入库断面水质多指标评价与演变特征分析

水体质量状况及其污染特征是流域水污染防治规划和治理措施制定的前提和基础。采用主成分分析和层次聚类分析等多元统计分析方法,选取溶解氧、高锰酸盐指数、化学需氧量等10个不同类型的监测指标,综合评价了官厅水库入库断面八号桥2006—2017年丰水期和枯水期水质年际变化特征,识别了不同阶段关键污染指标。结果表明:各年份丰水期(9月)大多数水质指标均优于枯水期(5月),特别是粪大肠菌群和氨氮,但总磷和高锰酸盐指数的差异较小。根据水质指标年际变化情况,可将研究期分为污染严重阶段(2006—2007年)、污染改善阶段(2008—2015年)和污染全面好转阶段(2016—2017年)。大部分水质指标呈现逐年好转的趋势,特别是粪大肠菌群、氨氮和五日生化需氧量,但总磷仍是官厅水库入库河流的重要污染指标。     

AHP与GA-SVM耦合模型在滑坡预警中的应用*

为了减少滑坡造成的损失，提高滑坡预测的准确性，通过搭建灾害模拟平台获得滑坡的实验数据，在获得多组模拟实验数据后，分析各变量的特性。首先，通过层次分析(Analytic Hierarchy Process，AHP)算法，对滑坡进行危险度划分；然后，通过支持向量机(Support Vector Machine，SVM)建立模型，遗传算法(Genetic Algorithm,GA)再优化SVM参数，提出1种层次分析法与GA-SVM相耦合的模型。研究结果表明:AHP方法划分后的数据，通过GA与SVM结合建立的模型精度较好，实验预测结果与实际结果较为吻合，与单一SVM相比，精度更高，结果更好，更加适用于多变量的复杂非线性滑坡预警。     

基于模糊推理的FRAM事故定量分析方法研究

为有效克服FRAM事故分析中无法进行定量分析的缺陷，提出结合模糊推理技术的Fuzzy FRAM模型。此改进模型基于FRAM识别系统运行状态；依据功能输出要素的时间/精度属性利用Matlab构建2阶模糊推理系统量化功能输出质量；根据通用性能条件(CPC)及功能输入耦合端口构建功能评价体系，针对评价体系中存在的不确定性信息融合及建模问题，采用模糊证据推理技术，通过模糊信度结构建立、数据处理、信息融合测度后获得功能的风险指数;以既有铁路危险品运输事故为例，验证方法的可行性。结果表明:Fuzzy FRAM模型的评估结果较为精确，是FRAM分析方法的有效补充。