福州CRT产业集群废物产生特征及管理模式研究

根据对福州开发区阴离子射线管(CRT)产业集群进行调查、访谈和统计分析,实证研究了该产业集群的废物产生特征和管理模式.结果发现,福州CRT产业集群在不断发展壮大的同时,随着主导产品生产规模的扩大以及配套企业的进入,废物的产生总量不断增加,单位主导产品的废物产生强度持续上升,废物种类也逐渐丰富,给区域环境带来的压力不断增大;但同时,产业集群借助其规模和集聚效应,不仅为废物交换和工业共生体系的构建提供了基础平台,而且为专营性废物回收组织和处理处置设施的良好运转营造了外部环境.     

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.     

Evaluation of a classification method for biodegradable solid wastes using anaerobic degradation parameters

We studied the biochemical and anaerobic degradation characteristics of 29 types of materials to evaluate the effects of a physical composition classification method for degradable solid waste on the computation of anaerobic degradation parameters, including the methane yield potential (L₀), anaerobic decay rate (k), and carbon sequestration factor (CSF). Biochemical methane potential tests were conducted to determine the anaerobic degradation parameters of each material. The results indicated that the anaerobic degradation parameters of nut waste were quite different from those of other food waste and nut waste was classified separately. Paper was subdivided into two categories according to its lignin content: degradable paper with lignin content of <0.05 g g VS^?1, and refractory paper with lignin content >0.15 g g VS^?1. The L₀, k, and CSF parameters of leaves, a type of garden waste, were similar to those of grass. This classification method for degradable solid waste may provide a theoretical basis that facilitates the more accurate calculation of anaerobic degradation parameters.     

Ecological scenarios analyzed and evaluated by a shallow lake model

We applied the complex ecosystem model EMMO, which was adopted to the shallow lake Müggelsee (Germany), in order to evaluate a large set of ecological scenarios. By means of EMMO, 33 scenarios and 17 indicators were defined to characterize their effects on the lake ecosystem. The indicators were based on model outputs of EMMO and can be separated into biological indicators, such as chlorophyll-a and cyanobacteria, and hydro-chemical indicators, such as phosphorus. The question to be solved was, what is the ranking of the scenarios based on their characterization by these 17 indicators? And how can we handle high quantities of complex data within evaluation procedures? The scenario evaluation was performed by partial order theory which, however, did not provide a clear result. By subsequently applying the hierarchical cluster analysis (complete linkage) it was possible to reduce the data matrix to indicator and scenario representatives. Even though this step implies losses of information, it simplifies the application of partial order theory and the post processing by METEOR. METEOR is derived from partial order theory and allows the stepwise aggregation of indicators, which subsequently leads to a distinct and clear decision. In the final evaluation result the best scenario was the one which defines a minimum nutrient input and no phosphorus release from the sediment while the worst scenario is characterized by a maximum nutrient input and extensive phosphorus release from the sediment. The reasonable and comprehensive results show that the combination of partial order, cluster analysis and METEOR can handle big amounts of data in a very clear and transparent way, and therefore is ideal in the context of complex ecosystem models, like that we applied.     

Cluster analysis and quality assessment of logged water at an irrigation project, eastern Saudi Arabia

A multivariate statistical technique, cluster analysis, was used to assess the logged surface water quality at an irrigation project at Al-Fadhley, Eastern Province, Saudi Arabia. The principal idea behind using the technique was to utilize all available hydrochemical variables in the quality assessment including trace elements and other ions which are not considered in conventional techniques for water quality assessments like Stiff and Piper diagrams. Furthermore, the area belongs to an irrigation project where water contamination associated with the use of fertilizers, insecticides and pesticides is expected. This quality assessment study was carried out on a total of 34 surface/logged water samples. To gain a greater insight in terms of the seasonal variation of water quality, 17 samples were collected from both summer and winter seasons. The collected samples were analyzed for a total of 23 water quality parameters including pH, TDS, conductivity, alkalinity, sulfate, chloride, bicarbonate, nitrate, phosphate, bromide, fluoride, calcium, magnesium, sodium, potassium, arsenic, boron, copper, cobalt, iron, lithium, manganese, molybdenum, nickel, selenium, mercury and zinc. Cluster analysis in both Q and R modes was used. Q-mode analysis resulted in three distinct water types for both the summer and winter seasons. Q-mode analysis also showed the spatial as well as temporal variation in water quality. R-mode cluster analysis led to the conclusion that there are two major sources of contamination for the surface/shallow groundwater in the area: fertilizers, micronutrients, pesticides, and insecticides used in agricultural activities, and non-point natural sources.     

B—P网络逐步聚类法在环境测点优选中的应用

将Ｂ－Ｐ网络原理与逐步聚类分析思想相结合,用于环境测点聚类优选。该方法用于水清河几个监测断面的优选结果是符合客观实际的。     

基于多元统计分析的石头口门水库汇水流域水质综合评价

根据石头口门水库汇水流域的4个监测断面2001～2007年的水质监测数据,应用多元统计分析方法(聚类分析与因子分析)确定主要污染因子并计算权重,从而对流域的水质进行综合评价。结果表明,通过因子分析,提取了3个公因子,第一主因子主要包括溶解氧、氨氮、总氮、高锰酸盐指数、化学需氧量、生化需氧量;第二主因子的主要代表指标是总磷;氟化物、总大肠菌群数对第三主因子贡献明显。由综合评价结果得出,石头口门水库总体属Ⅲ类水质,主要污染因子为总磷;饮马河(烟筒山断面)和岔路河(星星哨水库断面)水质属Ⅲ类,主要受第一主因子影响;双阳河(新安断面)水质属Ⅴ类。流域水质主要受到了农业非点源污染和生活污染的影响。     

Inner City regeneration: perspectives on the G.E.A.R. Project

Government responses to the problems of the inner city currently focus on comprehensive programmes of positive discrimination. The first and most extensive is the G.E.A.R. project in Glasgow. Regular monitoring of the effectiveness of such large‐scale initiatives is essential. If the physical and social aims of such policies are to be realised, assessments must utilise both objective and subjective indicators.     

Cluster detection using Bayes factors from overparameterized cluster models

In this paper, we consider the use of a partition model to estimate regional disease rates and to detect spatial clusters. Formal inference regarding the number of partitions (or clusters) can be obtained with a reversible jump Markov chain Monte Carlo algorithm. As an alternative, we consider the ability of models with a fixed, but overly large, number of partitions to estimate regional disease rates and to provide informal inferences about the number and locations of clusters using local Bayes factors. We illustrate and compare these two approaches using data on leukemia incidence in upstate New York and data on breast cancer incidence in Wisconsin.     

Trends and sources of particulate matter in the Superstition Wilderness using air trajectory and aerosol cluster analysis

Ambient aerosols adversely affect human health and visibility and impact climate. Identification of sources of particulate matter and its precursors is necessary for developing control strategies. The goal of this research is to utilize long-term speciated particulate matter data and back-trajectory cluster analyses to determine trends and sources of particulate matter in the Superstition Wilderness, a rural area east of Phoenix, Arizona. Twenty-four hour back-trajectories were calculated for every hour of every 24-h particulate matter sample obtained by IMPROVE from 1991 to 2004. Days that included back-trajectories with considerable spatial variance were excluded from further analyses. To minimize uncertainties inherent in single trajectories, all calculated trajectories for each sampling day were averaged to represent the air mass sampled during that day. Cluster analysis of trajectories identified four unique regions, including a region with Phoenix, a region with copper smelters, and one with coal-fired power plants. Yearly averages of sulfate, nitrate, soil, and carbon concentrations were calculated for each region. Statistically significant trends in species concentrations by region and independent of region and differences in concentrations between regions were examined.Sulfate concentrations from the region with smelters were higher than other regions but decreased during the study period. Emissions data from the smelters indicate that much of the sulfate from the region was due to the smelters. The overall 2.2% year⁻¹ decrease in sulfate concentrations at TNM is likely due to decreased emissions from the copper smelters. A 3.6% year⁻¹ increase in nitrate concentrations was driven largely by increasing NO_x concentrations from Phoenix and to a lesser extent the region southwest of the site which includes Tucson and suburban/urban areas between Phoenix and Tucson. Soil concentrations were higher from regions with deserts than the region without desert. This method could not identify trends or source regions of carbonaceous aerosols at this site.