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.     

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.     

废黄河水质的多元统计分析

用主成分分析的方法,找出影响废黄河2007年水质的主成分,计算各样本的主成分得分,用聚类分析的方法将样本水质分类,结合分类结果及水质综合评分对各样本水质进行评价。     

泸州市降水监测布点优化研究

在泸州市2016—2020年大气降水监测数据的基础上,借助聚类分析、多元方差分析等统计分析手段,对原有降水监测点位进行优化研究,并验证优化结果。在遵循《酸沉降监测技术规范》(HJ/T 165—2004)点位布设要求的前提下,建立了降水监测点位评价体系,从原有7个降水监测点位中优化筛选出3个。优化后的点位对监测结果的影响显著性均>0.05,表明优化前后全市降水数据无显著性差异,该优化方案不会影响区域整体代表性。     

南昌典型园林树种燃烧性实验研究

选择南昌西郊和北郊12个园林树种不同器官理化性状测定分析和燃烧试验,根据各树种的含水率、燃点、热值、粗灰分、粗脂肪、木质素、粗纤维、燃烧时间、火烧强度和生物生态学特性等10个因子,应用DPS聚类分析法,对12个园林树种的燃烧性进行了排序,结果表明,难燃树种有5个,依次是：乐昌含笑、珊瑚树、大叶黄杨、白玉兰和海桐;较难燃树种有4个,依次是：女贞、山茶、夹竹桃和含笑;易燃树种有3个,依次是：桂花、巴东木连和红花檵木。为南方防火林带的建设提供了目前公认以外的防火树种。     

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.     

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。