Integration of ecological–biological thresholds in conservation decision making

In the Anthropocene, coupled human and natural systems dominate and only a few natural systems remain relatively unaffected by human influence. On the one hand, conservation criteria based on areas of minimal human impact are not relevant to much of the biosphere. On the other hand, conservation criteria based on economic factors are problematic with respect to their ability to arrive at operational indicators of well‐being that can be applied in practice over multiple generations. Coupled human and natural systems are subject to economic development which, under current management structures, tends to affect natural systems and cross planetary boundaries. Hence, designing and applying conservation criteria applicable in real‐world systems where human and natural systems need to interact and sustainably coexist is essential. By recognizing the criticality of satisfying basic needs as well as the great uncertainty over the needs and preferences of future generations, we sought to incorporate conservation criteria based on minimal human impact into economic evaluation. These criteria require the conservation of environmental conditions such that the opportunity for intergenerational welfare optimization is maintained. Toward this end, we propose the integration of ecological–biological thresholds into decision making and use as an example the planetary‐boundaries approach. Both conservation scientists and economists must be involved in defining operational ecological–biological thresholds that can be incorporated into economic thinking and reflect the objectives of conservation, sustainability, and intergenerational welfare optimization.     

Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

The Role of Disaggregation of Asset Values in Flood Loss Estimation: A Comparison of Different Modeling Approaches at the Mulde River,Germany

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.     

挥发性有机污染物排放控制标准制订中的关键技术问题研究

研究分析了挥发性有机污染物排放标准制订中4个关键技术问题:污染因子选择、控制指标确定、限值的确定以及技术管理措施.结果表明,应根据毒性和光化学反应性选择污染因子;确定控制指标应既考虑有组织控制指标,又考虑无组织的控制指标;确定排放限值应明确是基于控制技术还是基于健康风险;同时制定技术管理措施以通过源头和过程控制挥发性有机污染物排放.     

饮用水水源地严格管理水质的对策分析——对实行严格水资源管理问题上的看法和建议

2012年1月国务院发布了《关于实行最严格水资源管理制度的意见》,这是众所关注的要事,实行了最严格水资源管理的原则精神以及确定县级以上政府负总责的规定.但对如何严格管理的技术路线和对策措施上,特针对现实中存在的主要问题提出以下3点看法和建议:①达到地面水环境质量Ⅱ类水质的饮用水水源地不一定未受污染;②保护区内企业车间排水口符合第一类污染物最高允许排放浓度规定是满足饮用水水源地要求的必要条件;③《地面水环境质量标准》(GB 3838-2002)对第一类污染物规定的Ⅱ类水质,是否体现了水环境背景值有较大存疑.     

CEMS比对监测数据偏差研究

简述CEMS比对监测技术背景,总结实际监测工作中导致数据偏差的各方面因素,分析问题产生原因并提出解决措施和建议。     

基于遥感监测的黄海绿潮漂移路径及分布面积特征分析

本文利用2008～2012年的多源卫星遥感序列图像,动态监测各年绿潮发生的漂移路径、影响海域面积,得到各年绿潮漂移趋势、分布面积和最大分布面积出现时间。根据监测结果,划分出影响绿潮漂移路径的关键区域,并分析了2008、2009和2010年关键区域内各年绿潮漂移方向和面积变化情况。     

基于DEA的上海市非中心城区工业生态效率研究

运用数据包络分析（DataEnvelopmentanalysis,DEA）的方法,从环境污染和资源消耗两个方面,建立了工业生态效率评价的指标体系,评价了2008年上海市非中心城区各区县的工业生态效率。结果表明,松江区处于上海市工业生态效率的前沿面,9个区县中有2／3区县的工业生态效率优于上海市平均水平。其中,崇明、嘉定和青浦处于规模报酬递增阶段,奉贤、闵行、浦东、金山、宝山则处于规模报酬递减阶段。最后,基于C2R模型对各区县的投入产出情况进行了优化调整,认为削减SO2的排放量和减少能源的消耗应是各区县提高工业生态效率的关键。     

烟气排放连续监测系统的数据准确性研究

随着全国环境自动监控建设任务的完成,“十二五”期间要把探索环境监控数据的应用摆在突出位置,使环境监控数据在环境质量监管、排污费征收、污染物总量减排核查核算、环境执法和环境决策等方面发挥最大效能,最重要的是保证环境监控数据的真实性和准确性。CEMS作为废气自动监控系统,结合CEMS数据应用到环境管理的需求,探讨确保CEMS数据真实性和准确性应注意的事项,通过运用这些注意事项分析实时在线监控数据,能发现CEMS数据中存在的问题,提高CEMS数据的准确性和真实性。