杭州市区机动车污染物排放特征及分担率

选取杭州市区绕城高速、快速路、主干道和民用支路4种典型道路进行工况测试,建立了2010年机动车CO、HC、NOx和PM10排放清单,获得了分车型、燃料类型、排放标准以及道路类型的机动车污染物排放分担率.结果表明,杭州市机动车的污染物排放分担率差别显著,乘用车、出租车和公交车是CO和HC排放的主要来源,重型货车和公交车是NOx和PM10排放的主要来源,且乘用车的NOx排放分担率也较大;柴油车的NOx和PM10的排放分担率远大于其保有量的贡献率,是其排放的主要来源,汽油车是CO和HC排放的主要来源;占保有量30%的国0和国I车辆,对CO、HC、NOx和PM10排放分担率分别为67%、69%、58%和82%;主干道是机动车CO、HC和NOx排放的主要来源,其排放分担率分别为66%、65%和64%,民用支路是PM10排放的主要来源,分担率为55%.     

武器装备验证环境因素确定模型及其应用研究

从系统的角度,分析了与武器装备环境紧密相联的4个因素,从武器装备的产品分解结构、环境分解结构、功能分解结构、状态集构建了武器装备系统、分系统、部件的试验验证环境因素确定模型,分析了模型的应用,给出了模型的工作流程。描述了基于上述模型的信息管理系统的构建思路与功能。     

废物水泥窑共处置产品中重金属释放量研究

通过模拟煅烧实验制取水泥熟料,并采用国标GB/T 17671-1999制取混凝土样品.选用EA NEN7371和EA NEN7375浸出方法,利用基于菲克扩散第二定律的一维扩散模型对废物水泥窑共处置产品（混凝土）中重金属长期累积释放量进行了研究.结果表明,混凝土中重金属的最大释放量低于总量;各种重金属在混凝土中的扩散系数不同,且Cr>As>Ni>Cd; Cr、As、Ni和Cd 30a的累积释放量分别为4.43、 0.46、 1.50和0.02 mg/kg,释放率（累积释放量/最大释放量）分别为27.0%、 18.0%、 3.0%和0.2%;扩散系数是影响重金属累积释放量的重要因素,且两者表现出较好的相关性;Cr和As的扩散系数较大（分别为1.15E-15 m²/s和6.42E-16 m²/s）,应重点控制其进入水泥窑处置过程的总量.     

基于损伤检测的腐蚀疲劳寿命预测概率模型

建立了飞机结构腐蚀疲劳寿命预测的4阶段概率模型,结合腐蚀疲劳损伤检测结果,通过检测的灵敏度和准确度2个随机变量来描述检测技术的可靠性;建立了检测后的修正腐蚀疲劳寿命预测概率模型,通过对比分析修正前后的腐蚀疲劳寿命分布,得出腐蚀损伤尺寸的检测结果。对腐蚀疲劳寿命的评估影响很大,并且检测技术越可靠,寿命评估越准确。     

A transportation network assessment tool for hazardous material cargo routing: Weighing exposure health risks,proximity to vulnerable areas,delay costs and trucking expenses

The inherent risks associated with accidental releases of hazardous materials during transport have drawn attention and concerns in the recent decades. The aim of this study is to propose a tool for evaluation and comparison of the transportation networks which can be used to assess the routing options between origins and destinations of the cargos for their suitability for transporting hazardous material cargos by tanker trucks and to identify routes which provide lower accidental release risks, lower public exposure risks, and offer economical benefits. Each route segment of transportation networks were evaluated using specific criteria which included health risk and cost of delay in case of an accidental release of materials, trucking cost and proximity to vulnerable areas. Since, the health impact of hazardous materials differ depending on the characteristics of the material being transported as well as release quantities and atmospheric conditions; this paper aimed in providing a tool that can be used to estimate the impact radius (for health risks) after accidental release of hazardous materials by taking into account different atmospheric conditions based on the meteorological data and solar elevation angle. The Gaussian air dispersion model paired with ArcGIS using Python programming were employed to estimate the health risk impact zones by considering the meteorological data, and accordingly to analyze road segments for cost impacts (delay and trucking costs), and the proximity to vulnerable areas. The route assessment tool was demonstrated with a case study. The results of this study can efficiently aid decision makers for transportation of hazardous materials.     

Developing Metapopulation Connectivity Criteria from Genetic and Habitat Data to Recover the Endangered Mexican Wolf

Restoring connectivity between fragmented populations is an important tool for alleviating genetic threats to endangered species. Yet recovery plans typically lack quantitative criteria for ensuring such population connectivity. We demonstrate how models that integrate habitat, genetic, and demographic data can be used to develop connectivity criteria for the endangered Mexican wolf (Canis lupus baileyi), which is currently being restored to the wild from a captive population descended from 7 founders. We used population viability analysis that incorporated pedigree data to evaluate the relation between connectivity and persistence for a restored Mexican wolf metapopulation of 3 populations of equal size. Decreasing dispersal rates greatly increased extinction risk for small populations (<150–200), especially as dispersal rates dropped below 0.5 genetically effective migrants per generation. We compared observed migration rates in the Northern Rocky Mountains (NRM) wolf metapopulation to 2 habitat‐based effective distance metrics, least‐cost and resistance distance. We then used effective distance between potential primary core populations in a restored Mexican wolf metapopulation to evaluate potential dispersal rates. Although potential connectivity was lower in the Mexican wolf versus the NRM wolf metapopulation, a connectivity rate of >0.5 genetically effective migrants per generation may be achievable via natural dispersal under current landscape conditions. When sufficient data are available, these methods allow planners to move beyond general aspirational connectivity goals or rules of thumb to develop objective and measurable connectivity criteria that more effectively support species recovery. The shift from simple connectivity rules of thumb to species‐specific analyses parallels the previous shift from general minimum‐viable‐population thresholds to detailed viability modeling in endangered species recovery planning. Desarrollo de Criterios de Conectividad Metapoblacional a Partir de Datos Genéticos y de Hábitat para Recuperar al Lobo Mexicano en Peligro de Extinción     

Using empirical models of species colonization under multiple threatening processes to identify complementary threat‐mitigation strategies

Approaches to prioritize conservation actions are gaining popularity. However, limited empirical evidence exists on which species might benefit most from threat mitigation and on what combination of threats, if mitigated simultaneously, would result in the best outcomes for biodiversity. We devised a way to prioritize threat mitigation at a regional scale with empirical evidence based on predicted changes to population dynamics—information that is lacking in most threat‐management prioritization frameworks that rely on expert elicitation. We used dynamic occupancy models to investigate the effects of multiple threats (tree cover, grazing, and presence of an hyperaggressive competitor, the Noisy Miner (Manorina melanocephala) on bird‐population dynamics in an endangered woodland community in southeastern Australia. The 3 threatening processes had different effects on different species. We used predicted patch‐colonization probabilities to estimate the benefit to each species of removing one or more threats. We then determined the complementary set of threat‐mitigation strategies that maximized colonization of all species while ensuring that redundant actions with little benefit were avoided. The single action that resulted in the highest colonization was increasing tree cover, which increased patch colonization by 5% and 11% on average across all species and for declining species, respectively. Combining Noisy Miner control with increasing tree cover increased species colonization by 10% and 19% on average for all species and for declining species respectively, and was a higher priority than changing grazing regimes. Guidance for prioritizing threat mitigation is critical in the face of cumulative threatening processes. By incorporating population dynamics in prioritization of threat management, our approach helps ensure funding is not wasted on ineffective management programs that target the wrong threats or species.     

Environmental cost of using poor decision metrics to prioritize environmental projects

Conservation decision makers commonly use project‐scoring metrics that are inconsistent with theory on optimal ranking of projects. As a result, there may often be a loss of environmental benefits. We estimated the magnitudes of these losses for various metrics that deviate from theory in ways that are common in practice. These metrics included cases where relevant variables were omitted from the benefits metric, project costs were omitted, and benefits were calculated using a faulty functional form. We estimated distributions of parameters from 129 environmental projects from Australia, New Zealand, and Italy for which detailed analyses had been completed previously. The cost of using poor prioritization metrics (in terms of lost environmental values) was often high—up to 80% in the scenarios we examined. The cost in percentage terms was greater when the budget was smaller. The most costly errors were omitting information about environmental values (up to 31% loss of environmental values), omitting project costs (up to 35% loss), omitting the effectiveness of management actions (up to 9% loss), and using a weighted‐additive decision metric for variables that should be multiplied (up to 23% loss). The latter 3 are errors that occur commonly in real‐world decision metrics, in combination often reducing potential benefits from conservation investments by 30–50%. Uncertainty about parameter values also reduced the benefits from investments in conservation projects but often not by as much as faulty prioritization metrics.     

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.     

兰州市废旧手机产生量预测分析研究

手机普及率日益提高,随之产生的废弃量也日益增多,分别采用时间梯度模型、市场供给A模型、斯坦福(stanford)模型对兰州市2013年-2022年废旧手机的产生量进行了预测.结果显示,不同模型预测结果之间存在较大差异,市场供给A模型和斯坦福(Stanford)模型的预测结果比较接近,均大于时间梯度模型的预测结果,三种模型预测的兰州市2022年废旧手机的产生量分别为40.40万、46.67万和21.04万部.总体上看,兰州市废旧手机的产生量增长趋势较快.