枣庄市二氧化硫污染现状及对策

枣庄市一次能源消费以煤为主，空气污染为煤烟型污染。因而，必需采取调整产业结构，发展集中供热和联片采暖，开展清洁能源替代等综合措施控制二氧化硫污染。     

核事故中放射性核素扩散浓度的理论预测

在高斯烟羽模型的基础上,对核事故中放射性云团在大气中的扩散规律进行了研究。利用倾斜烟团模式,考虑实际过程中核素粒子的重力沉降、雨洗作用以及放射性衰变等因素的影响,提出一种迅速估算放射性核素扩散浓度的方法。该方法可计算核事故中连续点源和瞬时点源在不同气象、地形条件下的浓度分布,并可获得地面的干沉积率和湿沉积率。放射性核素浓度的确定是放射性事故抢险救援和辐射防护等工作的基础和前提,是放射性事故应急救援的重要组成部分。该结果在核事故的应急救援过程中,对救援人员划定警戒区和确定周围居民的疏散范围具有重要意义,并可为制定救援方案和应急决策提供科学依据。     

我国物流产业基本经济活动空间格局分析

基于经济基础理论,对1991～2001年间我国各省区物流产业基本经济活动部分进行了实证分析,从省际差异、东中西部差异两个方面分析了我国区域物流产业基本经济活动的省区差异:在时间维度上区域差异呈逐步扩大趋势,在空间结构上表现出较明显的沿海指向性和交通指向性,并以此分析归纳出我国物流产业基本活动的"四区(物流集聚区)一带(物流集聚带)"空间格局特征,进而得出其基本经济活动强度与区域经济发展水平、物流需求程度、科技水平、区位条件、交通等基础设施、政策及历史发展作用紧密相关的结论.     

广东省的地质灾害与防治对策

广东省地质灾害类型多,分布广,频率高,危害大.地震、滑坡、崩塌、地裂缝、地面塌陷、地面沉降和软土地基变形等地质灾害是影响当地经济发展和城乡建设的消极因素.地质灾害的时间分布具有波动性,过去35年来存在两个明显的高发期,分别是1985-1992年和1997年至今;其空间分布具有区域耦合性,地质构造复杂、自然环境恶劣的粤西、粤北低山丘陵区和人类活动剧烈、经济发达的沿海平原地区都是重灾区.地质灾害与自然条件、人类活动关系密切.它们的防治应以防为主,防治结合,将生态与工程措施相结合,并调整人类的经济行为,保持生态环境的平衡.     

能源安全工程研究体系探讨

随着矿物能源开采消耗量的急剧增加,煤炭开采安全、能源开采和消耗引起的生态环境改变及破坏、世界石油市场油价波动对经济增长的不利影响、水电等可再生能源所占比重较低等,构成了制约可持续发展的能源安全问题。能源安全工程研究体系由区域能源开发消费安全、合理能源结构和消费布局、能源安全战略及相关法律法规等研究内容组成。通过分层次研究和循环促进,开展能源安全战略、能源安全技术、能源生态关系、能源安全经济的系统研究,为科学解决能源安全问题、实现国家或区域社会经济的可持续发展,提供了理论支持和科技保障。     

洪涝灾害评价的威布尔模型

首次将威布尔分布用于淮河流域水灾成灾面积研究,揭示了淮河流域洪涝灾害成灾面积形成的 内在规律,进而利用河南、安徽、江苏和山东4省的各自灾度对淮河流域的灾害风险建立了线性回归模型.实证分析表明,本方法切实可行,特别适用于大样本计算.     

北京市地热资源开发的时空变化

简要介绍了北京地热资源特点,分析了北京地热资源开发的时空演变规律.在搜集资料和实地调查的基础上,绘制出了北京城区地热井分布示意图,并针对其开发利用中可能出现的问题提出了对策.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado     

Applying network theory to prioritize multispecies habitat networks that are robust to climate and land‐use change

Designing connected landscapes is among the most widespread strategies for achieving biodiversity conservation targets. The challenge lies in simultaneously satisfying the connectivity needs of multiple species at multiple spatial scales under uncertain climate and land‐use change. To evaluate the contribution of remnant habitat fragments to the connectivity of regional habitat networks, we developed a method to integrate uncertainty in climate and land‐use change projections with the latest developments in network‐connectivity research and spatial, multipurpose conservation prioritization. We used land‐use change simulations to explore robustness of species’ habitat networks to alternative development scenarios. We applied our method to 14 vertebrate focal species of periurban Montreal, Canada. Accounting for connectivity in spatial prioritization strongly modified conservation priorities and the modified priorities were robust to uncertain climate change. Setting conservation priorities based on habitat quality and connectivity maintained a large proportion of the region's connectivity, despite anticipated habitat loss due to climate and land‐use change. The application of connectivity criteria alongside habitat‐quality criteria for protected‐area design was efficient with respect to the amount of area that needs protection and did not necessarily amplify trade‐offs among conservation criteria. Our approach and results are being applied in and around Montreal and are well suited to the design of ecological networks and green infrastructure for the conservation of biodiversity and ecosystem services in other regions, in particular regions around large cities, where connectivity is critically low.     

The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.