不同施工阶段扬尘污染特征研究

以北京城近郊区40多个建筑工地为实验工地,以降尘作为施工扬尘监测指标,对不同施工阶段的扬尘污染规律进行了研究,对不同施工阶段自身降尘浓度数据进行频率分布统计.结果表明,挖槽阶段相比结构和装修的施工扬尘污染更加严重;挖槽、装修和结构阶段自身降尘浓度＞50 t/(km²·30 d)的概率分别为16%、16%和3%,挖槽和装修阶段比结构阶段更容易发生高强度施工扬尘污染;不同施工阶段都遵循春季施工扬尘污染强度明显大于夏、秋、冬季,以及冬季略大于夏、秋季的规律;不同施工阶段的扬尘污染强弱关系非常明显,扬尘污染强度比值为,挖槽∶结构∶装修等于100∶67∶87.     

中国区域地表和大气人为影响敏感区域分布

通过1981-2000年NOAA-AVHRR 8km*8km卫星数据,从季节和年尺度分析了人为活动对地表植被覆盖具有显著影响区域分布.在季节尺度上,人为的多季农业耕作活动,造成部分地区植被覆盖出现季节不连续性,在植被轮作季节地表物理减性和几何特征发生明显变化;在年尺度上,人类活动对区域气候的影响主要体现在造成区域地表和...     

制定湖泊营养物基准的技术方法研究进展

湖泊营养物基准是指用可信的科学数据表示水中营养物的允许浓度和生态指标的阈值,是制定富营养化标准、评价湖泊状态依据。文章分析了国外制定湖泊营养物基准的方法:参照状态法、输入-响应模型法和机理推断法,并概述了目前湖泊富营养化预测模型。通过分析对比,提出了适合我国湖泊营养物基准制定的技术方法及思路。     

基于云模型和熵权法的巢湖流域防洪减灾能力评估

防洪减灾能力评估是洪涝灾害管理急需解决的重大问题之一。从防洪除涝能力、监测预警能力、抢险救灾能力及灾害管理能力4个方面来构建指标体系,建立基于云模型和熵权法的巢湖流域防洪减灾能力评估模型,对整个流域以及流域内各县市的防洪减灾能力进行评估,并与通过组合赋权法及最优分割法得到的结果进行验证对比。以期为防洪减灾能力评估提供新的思路,对巢湖流域的防洪减灾能力建设提供科学依据。研究结果表明:巢湖流域整体防洪减灾能力属于中等偏上水平,但流域内部差异明显;其中合肥市辖区、和县的防洪减灾能力属于高水平;巢湖市、含山县处于中等水平;肥西县和庐江县位于较低水平;肥东县、无为县、舒城县的防洪减灾能力则最低。     

Barrier management in the offshore oil and gas industry

Accident investigations indicate that inadequate barrier management has been a main cause of many accidents in the process industry. In 2013, the Norwegian Petroleum Safety Authority issued a barrier management framework for the offshore oil and gas industry. The framework describes principles related to barrier management and may be a valuable guide for the entire process industry. However, the offshore industry faces several challenges when implementing the framework. This paper discusses these challenges and clarifies the central concepts and steps of barrier management. A key message is the need for clarity and integration in a systematic approach to risk and barrier management.     

基于新能源视角的南京市雾霾形成与治理路径研究

伴随着南京市工业化进程的快速推进,雾霾天气出现的频率越来越高,严重影响到交通的顺。运行,威胁着居民健康,制约了当地经济环境的可持续发展,因此人们对雾霾现象的关注度日益提高。从能源结构角度,具体分析南京市雾霾天气的形成原因,提出针对性的治理雾霾的现实性建议与路径,指出必须调整以煤炭为中心的能源消费结构,加快建设新能源汽车配套设施,严格规范成品油标准以减排汽车尾气,通过内化环保产业外部性实现优化资源配置。     

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants’ incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.     

基于云物元的老旧社区内涝灾害脆弱性评价*

为降低内涝灾害评价中信息模糊性和冲突性对评价结果的影响,促进老旧社区内涝灾害适灾化和海绵化改造,提出基于云物元模型的老旧社区内涝灾害脆弱性评价方法。从防灾减灾角度和社区尺度出发,围绕致灾因子危险性、孕灾环境敏感性和承灾体易损性建立评价指标体系,运用AHP-熵权法确定指标组合权重,建立老旧社区内涝灾害脆弱性云物元评价模型,并以长沙市科学村社区为例进行验证。研究结果表明:本文模型能直接客观反映老旧社区内涝灾害脆弱性的主要影响因素和脆弱性等级,研究结果可为提高老旧社区内涝灾害应对能力提供支持。     

粉尘云浓度对HDPE粉尘云最低着火温度的影响

为准确评价高密度聚乙烯（HDPE）粉尘爆炸敏感性和开展有效的粉尘防爆工作,采用Godbert-Greenwald恒温炉标准实验装置研究了典型HDPE粉尘云最低着火温度的分布特性,着重探讨了粉尘云浓度对不同喷尘压力条件下HDPE粉尘云最低着火温度的影响规律。研究表明:测试条件下HDPE粉尘云最低着火温度的变化处于360~445 ℃范围,随粉尘云浓度的增加呈现先降低后升高的总体趋势,粉尘云浓度为1.111 kg/m3时出现拐点,且粉尘云最低着火温度随喷尘压力的增加而降低。     

基于Sentinel-1A极化SAR数据与面向对象方法的山区地表覆被分类

地表覆被分类对国土资源调查评估及全球变化具有基础性和关键性意义,但山区由于地形和云雾等的影响,可利用光学遥感影像和其他资源十分稀缺。因此,论文以渝东南山区为研究区,基于Sentinel-1A极化合成孔径雷达（SAR）数据,通过系列预处理,得到后向散射系数值,同时对各类地物的VV/VH极化后向散射系数、纹理、高程和坡度等特征值统计分析,综合这些特征值运用面向对象分类方法对单时相与多时相SAR数据进行地表覆被分类,最后将这两种分类方法与Landsat 8 OLI数据分类作对比。研究表明：1）在同时运用面向对象分类方法的前提下,单时相SAR数据分类和Landsat 8 OLI数据分类精度相当,多时相SAR数据面向对象分类精度最高,总精度为85.65%,Kappa系数为0.829 9;2）与光学数据相比,SAR数据对阔叶林、人工建筑提取有优势,精度提高了10%以上,多时相特征有利于耕地和针阔混交林提取,分类精度比单时相提高了9%左右;3）研究区土地覆被类型以林地为主,占总面积的42.68%,耕地、草灌次之,人工建筑、草地与河流占地面积较少。