乡镇垃圾分类循环利用现状——以闽侯县上街镇为例

由于我国乡镇经济的迅速发展以及乡镇村民生活水平加速提高,乡镇的垃圾种类及数量趋向于城市.乡镇村民居住较为分散,垃圾分类、收集、循环利用相较于城市困难,因此推进乡镇垃圾分类循环利用迫在眉睫.本文从闽侯县上街镇展开调查,以乡镇垃圾分类、回收循环利用为关键词展开分析,并从上街镇垃圾分类循环利用模式存在的问题进行分析、总结和建...     

Indigenous Systems of Forest Classification: Understanding Land Use Patterns and the Role of NTFPs in Shifting Cultivators’ Subsistence Economies

This article discusses the system of classification of forest types used by the Pwo Karen in Thung Yai Naresuan Wildlife Sanctuary in western Thailand and the role of nontimber forest products (NTFPs), focusing on wild food plants, in Karen livelihoods. The article argues that the Pwo Karen have two methods of forest classification, closely related to their swidden farming practices. The first is used for forest land that has been, or can be, swiddened, and classifies forest types according to growth conditions. The second system is used for land that is not suitable for cultivation and looks at soil properties and slope. The article estimates the relative importance of each forest type in what concerns the collection of wild food plants. A total of 134 wild food plant species were recorded in December 2004. They account for some 80–90% of the amount of edible plants consumed by the Pwo Karen, and have a base value of Baht 11,505 per year, comparable to the cash incomes of many households. The article argues that the Pwo Karen reliance on NTFPs has influenced their land-use and forest management practices. However, by restricting the length of the fallow period, the Thai government has caused ecological changes that are challenging the ability of the Karen to remain subsistence oriented. By ignoring shifting cultivators’ dependence on such products, the involvement of governments in forest management, especially through restrictions imposed on swidden farming practices, is likely to have a considerable impact on the livelihood strategies of these communities.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

我国不同类型地下工程中的氡水平

为了解我国地下工程内氡的浓度水平及其可能对人体产生的危害,采用核固体径迹法,在不同季节对全国23个城市的234个地下工程测点的氡浓度进行了测量。根据工程的建筑类型及用途的不同,将所有被测工程分为坑道、干道(包括掘进式地道工程)、地下室(高层建筑地下室和掘开式工程)和停车场四类,各类工程的氡浓度平均值分别为327、619、96.9、59.1 Bq.m-3。对于不同山体岩性的坑道,氡浓度值由高到低的顺序是花岗岩>凝灰岩>石英砂岩>石灰岩>安山玢岩。比较各类工程不同季节的氡浓度,得出,停车场氡浓度季节变化不大,其它三类工程都有氡浓度夏季明显高于冬季的特点,而且通风差的未使用的坑道和干道比使用中的同类工程具有相对高的氡浓度夏/冬比值。     

浅析危险化学品分类

阐述了危险化学品分类的意义,介绍了国内外主要化学品分类的标准和一般程序,以及危险化学品分类的最新研究进展。     

模糊聚类分析在中国火灾危害分类中的应用

火灾现象具有随机性、模糊性,是个复杂的模糊系统行为。运用模糊聚类分析方法,借助于M ATLAB计算软件,对中国火灾的危害程度进行了分类。根据2000年中国火灾统计资料,把31个省市分为重灾区、较重灾区、一般灾区、轻灾区等4类,与传统的概率统计方法相比,本方法更符合客观实际。城市火灾分类是动态的,重灾区吸取教训,增强消防意识,增大消防投入,可减少火灾隐患;而轻灾区如不注意防范,也会增加火灾隐患。因此不论轻重灾区,均应时时增强防范意识,把火灾危害降至最低。     

安全风险分级防控在油气开发领域的探索与应用

为推动油气开发领域安全风险分级防控有效落地,提高风险预控能力,本文基于风险防控现状评估结果,探索性地研究安全风险分级防控思路,构建了油气开发领域安全风险分级防控体系推进模型,提出了逆向与正向危害因素识别方法,明确了风险评价关键隐性过程原则,构建了风险-后果-能资分级防控决断模型。选取典型试点单位进行实践,结果证明,油气开发领域安全风险分级防控体系推进模型是科学合理的,其他领域也可借鉴该模型开展安全风险分级防控工作。     

锂电池检测的安全标准及安全防护研究

为评估并提高锂电池在使用、储存和运输过程中的安全,本文归纳总结了国内外锂电池安全检测的相关标准,对标准中测试项目异同作了重点分析和对比,并讨论了锂电池的安全风险及风险分级情况。同时,鉴于锂电池检测过程中可能出现的起火、爆炸等危险,探讨了电池检测过程的安全防护技术,并从样品区和试验区两方面提出了安全防护要求和建议。     

生活垃圾强制分类形势下的多源废荧光灯管回收管理机制建议

废荧光灯管的来源广泛,包括工业来源和社会来源,社会来源又包括居民来源,企业事业单位、公共场所等机构来源,具有多来源的特性。废荧光灯管中含有汞,若不经过妥善处理,会造成环境污染,对人的身体健康造成危害。随着国内生活垃圾分类的开展,废荧光灯管作为生活垃圾中有害垃圾的典型类别被强制分类,但仍存在很多问题。本文梳理了我国废荧光灯管的产生情况、LED灯替代情况,并对美国、欧盟、日本和我国国内典型城市的废荧光管理情况进行了总结。针对我国废荧光灯管的回收现状,从责任主体、责任划分、收集渠道、资金机制等角度提出了相关的管理建议,为推动我国废荧光灯管的管理提供借鉴。     

自愿减排项目碳泄漏：内涵、类别及应对

自愿减排项目是碳排放交易市场抵消机制的运行载体,灵活的自愿减排抵消机制是碳排放交易市场的补充,对应对全球气候变化问题具有重要意义。碳泄漏,作为严重影响抵消项目减排效率的热点问题值得关注。目前,中国已形成较大规模的自愿减排市场,截至2018年底,国家发展改革委共签发了约7200万tCO_2当量减排量,我国在建立健全自愿减排抵消市场机制的过程中,需要把握项目碳泄漏的负外部性这一实质,厘清其运行机理并积极应对。本文旨在系统阐述该问题,并为进一步实现抵消项目减排潜力提供思路和方向:从自愿减排项目碳泄漏的内涵与定义出发,首先对项目碳泄漏的分类进行梳理,并依据产生途径的不同重点分析了活动转移排放、生命周期、市场路径、生态以及技术泄漏的作用机制、影响因素及其相应的研究方法;其次从具体项目应用、系统项目应用和宏观应用三个层面讨论了应对自愿减排项目碳泄漏问题的管理对策;最后结合我国自愿减排抵消市场的实际情况,建议未来抵消项目碳泄漏的应对和管理应在分门别类对其概念进行精准定义的基础上将解决措施的成本效益考虑在内,此外,在评估泄漏量时需要同时测算不同类型的碳泄漏,探究其协同效应以免高估其泄漏程度。