基于快速聚类方法分析常州市区PM2.5的统计特性

运用统计方法研究常州市区2013~2014年6个国控点六项基本污染物(SO_2、NO_2、CO、O_3、PM_(2.5)和PM_(10))月平均浓度变化,结果表明,除O_3外,其它五项污染物月平均浓度夏季较低冬季较高.颗粒物与风速之间的关系为PM_(2.5)浓度随风速的升高一直降低,PM_(10)随风速的升高浓度先降低后升高.采用快速聚类分析(k-means)并运用SWV和DIV指数对六项基本污染物进行分类,得到4个样本分类.与依据颗粒物化学成分或粒径谱对PM进行源解析方法不同,本研究更多是从PM_(2.5)与其它污染物相关关系以及污染程度等角度按照欧式距离进行分类.不同类中PM_(2.5)来源明显不同,类1中PM_(2.5)与化石燃料燃烧排放密切相关,类2与O_3密切相关,类3与城市不完全燃烧排放、区域灰霾污染密切相关,类4可以归类于城市"背景"类.快速聚类分析结果也表明常州市区PM_(2.5)有着复杂的来源.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

香港灌丛植被的数量分类与环境关系分析

灌丛是香港植被的主要组成部分,2005年1—3月在香港地区(包括香港岛、九龙、新界和大屿山岛)设置样地,利用组平均法(UPGMA)和除趋势对应分析法(DCA)对香港灌丛群落进行聚类和排序分析. 结果表明,灌丛植被可划分为7个群落类型:桃金娘、米碎花、岗松、苏铁蕨、大头茶、山油柑和细齿柃. DCA排序揭示了灌丛群落的分布格局与环境因子的生态关系,DCA排序第1轴反映了灌丛群落所在环境的湿度梯度,排序图的对角线指示出海拔梯度,也反映了温度的变化. 结合物种多样性分析发现,物种多样性指数随海拔的升高而减少,说明水热条件和海拔是制约灌丛植被分布格局的主要因素.      

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。