溃坝后果严重程度评价模型研究

为了建立溃坝严重程度综合评价的定量模型,依据现行法规,采用建立数学模型的方法,分别分析并提出了决定大坝溃决后果严重性的3个主要影响因子(生命损失、经济损失和社会环境影响)的严重程度系数及其对数非线性和对数线性评价模型,构造了溃坝后果严重程度的综合评价模型.依据该模型,定量分析了5座水库大坝溃坝的严重性,给出了判别严重程度的定量指标.     

冬瓜山铜矿安全生产事故统计分析及改进措施

为查找冬瓜山铜矿安全生产事故规律,提出有效的改进措施,预防和减少事故发生,运用三维预防安全管理体系研究成果,对冬瓜山铜矿2003~2013年的安全生产事故,按照严重程度、伤残等级、事故类型等分别进行统计与分析,查找事故发生规律。统计表明,冬瓜山铜矿死亡事故按类别主要以车辆伤害和中毒窒息死亡为多,其次是片帮冒顶;从原因分析看,排在第一的是事故隐患,其次是管理缺陷和违章。针对事故发生的原因,提出了一系列的改进措施,目前这些改进措施已经逐步在冬瓜铜矿得到落实,取得了明显的效果,2012年以来,事故率明显下降,员工安全意识和安全操作技能一定程度得到提高,矿山安全管理环境得到显著改善。     

核电站核事故核素污染评估模式

利用修正了的高斯模式探讨核电站核素污染事故中的核素扩散。分析了边界条件,气象条件和核素衰变规律对核素扩散污染的影响,并进一步讨论了核素的污染范围,距源距离及核素的沉积量,可作为确定应急安全的理论依据。     

淮河流域河南段退化河流生态系统修复模式

针对淮河流域河南段部分河流出现的水资源过度开发利用、水质恶化和生物多样性下降等生态退化问题,根据淮河流域河南段主要河流退化特点,以改善淮河流域河南段退化现状为目标,在河流生态修复技术研究和工程实践的基础上,按照修复技术的功能,筛选出水量调整、水质净化和生物多样性提高等3类共13种适合淮河流域河南段河流生态系统修复技术。为使流域水生态修复更具针对性,根据流域内主要河流断流频率和洪/枯比计算,将流域内的河流分为常年有水型和间歇性断流型,然后以修复模式为修复范式研究的基本单元,分别针对流域内常年有水型河流生态系统的退化特点构建了3种修复模式,针对流域内间歇性断流型河流生态系统的退化特点构建了5种修复模式,以期为流域内退化河流生态系统的修复提供指导。     

净水除磷基质的综合筛选与长效建模预测

为筛选效果好且长效的除磷基质,选取电气石陶粒、赤泥陶粒、凹凸棒石、硅胶、海绵铁、活性炭、火山岩、沸石8种基质进行磷静态吸附试验,优选3种基质进行单基质柱状除磷渗流试验和微观结构表征,获取基质的除磷特征,并依据渗流试验数据建模预测长效除磷效果。结果表明：8种基质对磷的吸附能力差异较大,其中电气石陶粒、赤泥陶粒、沸石吸附能力较好;渗流试验中,电气石陶粒在111 d后对磷的去除率仍维持在67%,赤泥陶粒则降至37%;不同基质出水总磷浓度随时间呈现不同变化规律,其统计模型预测性较好,经建模预测得出,电气石陶粒在第160天左右对磷的去除率降至50%,第300天左右达到吸附饱和,赤泥陶粒则在第150天左右达到吸附饱和。基质微观结构（比表面积、表面特征）及化学元素组成等对除磷效果有明显影响。     

粤北某离子吸附型稀土矿山土壤和地表水重金属分布及风险评价

对粤北某离子吸附型稀土矿24个土壤样品中的As、Cd、Cr、Cu、Mn、Ni、Pb、Zn和Hg,以及15个地表水样品中的As、Cd、Cu、Hg、Mn、Pb和Zn进行了检测,并以多元统计分析与土壤潜在生态风险指数法、地表水健康风险评价模型相结合的方式,研究了重金属的分布特征及风险水平。结果表明:在土壤中,Mn、Zn、Cd和Pb的平均含量均超过了背景值;Mn、Cr、Ni、Cu、Cd和Zn在采区有较明显集聚,As、Pb和Hg的高含量分布相对均匀;Cr、Ni、Cu和Pb含量主要受区域背景影响,Zn、As、Cd和Hg含量与矿区人类活动关系密切,Mn含量受自然和人为因素共同控制;重金属造成的土壤潜在生态风险整体处于轻微水平,Ⅱ采区和Ⅶ采区生态风险较高;Cd和Hg是造成土壤生态危害的主要重金属元素。在地表水中,Mn的平均浓度超过了《地表水环境质量标准》(GB 3838-2002)规定的限值,其余重金属的含量均满足该标准中的Ⅲ类水质要求;重金属浓度在靠近采区及位于河流中下游的位置偏高;Mn、Cd、Pb、Zn和Cu浓度受稀土开采影响较大,As、Hg浓度主要受自然因素影响;重金属产生的健康总风险(9.39×10^-7~1.01×10^-6 a^-1)低于国际辐射防护委员会推荐的参考标准(5×10^-5 a^-1),但儿童通过饮水途径受到的健康风险(1.01×10^-6 a^-1)略超过部分机构的推荐限值;Cd和As是地表水中产生健康风险的主要重金属元素。综上,研究区重金属污染风险管理的主要对象是Cd和Mn。     

The importance of tangible and intangible factors in human–carnivore coexistence

Conflict with humans is one of the major threats facing the world's remaining large carnivore populations, and understanding human attitudes is key to improving coexistence. We surveyed people living near Hwange National Park about their attitudes toward coexisting with lions. We used ordinal regression models with the results of the survey to investigate the importance of a range of tangible and intangible factors on attitudes. The variables investigated included the costs and benefits of wildlife presence, emotion, culture, religion, vulnerability, risk perception, notions of responsibility, and personal value orientations. This was for the purpose of effectively tailoring conservation efforts but also for ethical policy making. Intangible factors (e.g., fear and ecocentric values) were as important as, if not more important than, tangible factors (such as livestock losses) for understanding attitudes, based on the effect sizes of these variables. The degree to which participants’ fear of lions interfered with their daily activities was the most influential variable. The degree to which benefits accrue to households from the nearby protected area was also highly influential, as was number of livestock lost, number of dependents, ecocentric value orientation, and participation in conflict mitigation programs. Contrary to what is often assumed, metrics of livestock loss did not dominate attitudes to coexistence with lions. Furthermore, we found that socioeconomic variables may appear important when studied in isolation, but their effect may disappear when controlling for variables related to beliefs, perceptions, and past experiences. This raises questions about the widespread reliance on socioeconomic variables in the field of human–wildlife conflict and coexistence. To facilitate coexistence with large carnivores, we recommend measures that reduce fear (through education and through protective measures that reduce the need to be fearful), reduction of livestock losses, and ensuring local communities benefit from conservation. Ecocentric values also emerged as influential, highlighting the need to develop conservation initiatives tailored to local values.     

A guide to representing variability and uncertainty in biodiversity indicators

Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers.     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

Estimation of spatiotemporal trends in bat abundance from mortality data collected at wind turbines

Renewable energy sources, such as wind energy, are essential tools for reducing the causes of climate change, but wind turbines can pose a collision risk for bats. To date, the population-level effects of wind-related mortality have been estimated for only 1 bat species. To estimate temporal trends in bat abundance, we considered wind turbines as opportunistic sampling tools for flying bats (analogous to fishing nets), where catch per unit effort (carcass abundance per monitored turbine) is a proxy for aerial abundance of bats, after accounting for seasonal variation in activity. We used a large, standardized data set of records of bat carcasses from 594 turbines in southern Ontario, Canada, and corrected these data to account for surveyor efficiency and scavenger removal. We used Bayesian hierarchical models to estimate temporal trends in aerial abundance of bats and to explore the effect of spatial factors, including landscape features associated with bat habitat (e.g., wetlands, croplands, and forested lands), on the number of mortalities for each species. The models showed a rapid decline in the abundance of 4 species in our study area; declines in capture of carcasses over 7 years ranged from 65% (big brown bat [Eptesicus fuscus]) to 91% (silver-haired bat [Lasionycteris noctivagans]). Estimated declines were independent of the effects of mitigation (increasing wind speed at which turbines begin to generate electricity from 3.5 to 5.5 m/s), which significantly reduced but did not eliminate bat mortality. Late-summer mortality of hoary (Lasiurus cinereus), eastern red (Lasiurus borealis), and silver-haired bats was predicted by woodlot cover, and mortality of big brown bats decreased with increasing elevation. These landscape predictors of bat mortality can inform the siting of future wind energy operations. Our most important result is the apparent decline in abundance of four common species of bat in the airspace, which requires further investigation.