Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters

This article introduces a software package named GeoSurveillance that combines spatial statistical techniques and GIS routines to perform tests for the detection and monitoring of spatial clustering. GeoSurveillance provides both retrospective and prospective tests. While retrospective tests are applied to spatial data collected for a particular point in time, prospective tests attempt to incorporate the dynamic nature of spatial patterns via analyzing time-series data to detect emergent clusters as quickly as possible. This article will outline the structure of GeoSurveillance as well as describe the statistical cluster detection methods implemented in the software. It concludes with an illustration of the use of the software to analyze the spatial pattern of low birth weights in Los Angeles County, California.      

全球地理信息系统(G2IS)架构体系初探

随着人们对全球GIS的需求不断增加,现有的地理信息系统在技术上已不能适应,因此,建设新一代全球GIS成为当前GIS发展的客观要求。本文基于地球剖分理论,以计算机集群技术、时空一体化技术和虚拟现实技术为辅助技术,提出了关于全球地理信息系统(G2IS)的架构体系的初步设想,认为新一代全球地理信息系统将成为GIS发展的一个重要方向。     

Cluster-scale magnetic fields

The search for non thermal radio emission from clusters of galaxies is a powerful tool to investigate the existence of magnetic fields on such large scale. Unfortunately, such observations are scarce thus far, mainly because of the very faint large scale radio emission expected in clusters of galaxies. In the present contribution we will first review the status of the radio observations of clusters of galaxies, carried out with the aim of detecting large scale radio emission.We will then focus on the large scale radio emission detected at 327 MHz and 610 MHz in the Coma cluster of galaxies. The features of the detected radio emission suggest that a magnetic field with an intensity of the order of ~ 10^–7 Gauss must be present on a scale of about 2 Mpc (forH _o = 100km s ^–1 Mpc ^–1). The morphology of the radio emission is similar to that of the most recent X-ray images derived with ROSAT, and follows the distribution of the galaxies in the cluster. All these pieces of information will be taken into account in the discussion on the possible origin of this large scale magnetic field.     

Combination of single and sequential chemical extractions to study the mobility and host phases of potentially toxic elements in airborne particulate matter

Risk assessment of metals associated to airborne particulate matter (PM) has usually been based on the analysis of their total concentrations, which is a poor indicator of metal mobility. Chemical fractionation processes may provide an additional level of information, however, chemical complexity and small sample sizes do not allow to combine several extraction methods. Additionally, analysing the metal concentrations during the extractions exceptionally provides restricted information about metals’ speciation. To overcome these limitations we collected total suspended particulate matter (TSP) samples from the air filters placed in the air supply channel of methane-heated turbines of thermal power stations which allows collecting large amounts of TSP materials. Additionally, we combined single and sequential chemical extractions in which not only the concentrations of potentially toxic elements (PTE) (Cd, Cr, Cu, Ni, Pb, Zn) but also that of the major chemical components (Al, Ca, Fe, K, Mg, Mn, Na, P, S, Ti) were analysed. Our aims were to study these metals’ mobility and speciation through the study of their association to major chemical components.Accumulation of the studied PTEs in the TSP material suggests moderate contamination for Ni, Cd and Cr whereas a heavy one for Cu, Zn and Pb. Three groups of these PTEs could be distinguished based on their mobility. The highly mobile Zn and Cd (large ratios of water and weak acid soluble fractions) can be considered as especially harmful elements to environment. The moderately mobile Pb and Cu (large ratios of reducible and oxidizable fractions, respectively) may potentially have a negative effect on the environment, whereas the immobile Cr and Ni cannot be expected to pose a serious risk. Based on the statistical evaluation of extraction data, the potential phases for Zn and Cu are presented by metal-sulphates, -nitrates, -chlorides, -carbonates and -hydroxides, as well as sorbed forms. Lead primarily hosted by metal-carbonates and sorbed forms, as well as by -hydroxides, whereas Cu by organic matter. Finally, Cr and Ni are mostly incorporated into very resistant phases, most probably by magnetite or other resistant metal-oxides.Combination of single and sequential extractions, as well as that of the analysis of not only the target elements but also the major chemical components were found to be a very effective tool to study the host phases of PTEs in the TSP material. The necessity for relatively large sample amounts for such analyses could be fulfilled using special sampling methodology; however, obvious disadvantages of this kind of sampling must be taken into account when resulted data are evaluated.     

Assessing the social context of wildfire-affected areas. The case of mainland Portugal

Wildfires cause different impacts, depending on the conditions and resilience level of the exposed communities. Wildfire occurrence in mainland Portugal was assessed with regard to socioeconomic and demographic parameters, to identify the most distinctive conditions of fire-affected areas, without implying the existence of causal relationships. The latest population and agriculture census data were used to retrieve conditions at the civil parish level, regarding demographic patterns, social and labor conditions, physical structures and agricultural activities. To identify differences between parishes, two groups were created with the communities that showed the highest and lowest 20% of wildfire incidence between 2007 and 2014, separately for density of fire events and for burned area. A stepwise approach based on classification trees and random Forest methods was applied to identify the best discriminant variables between the groups. First, irrelevant variables were removed by an interactive process based on misclassification rates. The second step used random Forest analysis to the remaining variables to evaluate their importance in distinguishing the groups. In the final step, cluster analysis was applied to test the correspondence between the clusters created with the selected variables and the initial groups. Results showed that parishes with higher fire density have higher population density, higher proportion of young and educated people, larger families and more overcrowded buildings. On the contrary, parishes with larger burned area are less populated, less attractive to foreigners, have a higher proportion of elderly people, more degraded housing conditions and agricultural activities, visible in the density of sheep and goat and pastures, are still relevant. The cluster analysis demonstrated a better performance of the model for wildfire density, revealing a strong association with socioeconomic dynamics with an agreement above 0.85, much higher than for burned areas which is 0.29. Overall, the spatial distribution of wildfire impacts is framed by societal settings and particular conditions must be further understood to improve the coping capacity of affected communities.     

Source analysis and risk evaluation of heavy metal in the river sediment of polymetallic mining area: Taking the Tonglüshan skarn type Cu-Fe-Au deposit as an example,Hubei section of the Yangtze River Basin,China

In this paper, 25 sampling points of overlying deposits in Tonglushan mining area, Daye City, Hubei Province, China were tested for heavy metal content to explore pollution characteristics, pollution sources and ecological risks of heavy metals in sediments. A geo-accumulation index method was used to evaluate the degree of heavy metal pollution in the sediment. The mean sediment quality guideline quotient was used for evaluating the ecological risk level of heavy metal in the sediment. And a method of correlation analysis, clustering analysis, and principal component analysis was used for preliminary analysis on the source of heavy metal in the sediment. It was indicated that there was extremely heavy metal pollution in the sediment, among which Cd was extremely polluted, Cu strongly contaminated, Zn, As, and Hg moderately contaminated, and Pb, Cr, and Ni were slightly contaminated. It was also indicated by the mean sediment quality guideline-quotient result that there was a high ecological risk of heavy metals in the sediment, and 64% of the sample sites had extremely high hidden biotoxic effects. For distribution, the contamination of branches was worse than that of the main channel of Daye Dagang, and the deposition of each heavy metal was mainly influenced by the distance from this sample site to the sewage draining exit of a tailings pond. The source analysis showed that the heavy metals in the sediment come from pollution discharging of mining and beneficiation companies, tailings ponds, smelting companies, and transport vehicles. In the study area, due to the influence of heavy metal discharging from these sources, the ecotoxicity of heavy metals in the sediment was extremely high, and Cd was the most toxic pollutant. The research figured out the key restoration area and elements for ecological restoration in the sediment of the Tonglüshan mining area, which could be referenced by monitoring and governance of heavy metal pollution in the sediment of the polymetallic mining area.©2022 China Geology Editorial Office.     

Influence of traffic emissions on the composition of atmospheric particles of different sizes—Part 2: SEM–EDS characterization

In urban areas traffic is the major contributor to atmospheric particulate matter and exposure to these particles currently represents a serious risk to human health. The attention has been recently focused more on the particles of smaller sizes (PM_2.5) which penetrate deeper in respiratory system causing severe health effects. Therefore, more information on PM_2.5 should be provided, namely concerning morphological and chemical characterization. Aiming further evaluation of the impact of traffic emissions on public health, this work evaluated the influence of traffic on the chemical and morphological characteristics of PM₁₀ and PM_2.5, collected at one site influenced by traffic emissions and at one reference site. Chemical and morphological characteristics of 1,000 individual particles were determined by scanning electron microscopy combined with energy dispersive spectrometer (SEM–EDS). Cluster analysis (CA) was used to identify different types of particles that occurred in PM, aiming the identification of the respective emission sources. Traffic PM_2.5 were dominated by particles composed of Fe oxides and alloys (67%) which were related to traffic emissions (this percentage was 3.7 times higher than at the background site); in PM_2.5–10 the abundance of Fe oxides and alloys were 20% and 0% for the traffic and background sites, respectively. Background PM_2.5 were mainly constituted by aluminum silicates (63%) related to natural sources (this percentage was 2.5 times higher than at the traffic site); the abundances of aluminum silicates in PM_2.5–10 were 74% and 73% for traffic and background sites, respectively. It was concluded that traffic emissions were mainly present in PM_2.5 (the percentage of particles associated to these emissions was 3.4 times higher than in PM_2.5–10), while coarse particles were dominated by material of natural origin (the percentage of particles associated was 1.2 and 3.0 times higher than in PM_2.5 for traffic and background sites, respectively). Previous results obtained by proton induced X-ray emission (PIXE) were consistent with SEM–EDS analysis that showed to be very useful to complement elemental analysis of different PM_2.5 and PM_2.5–10.