Source and distribution of metals in urban soil of Bombay,India, using multivariate statistical techniques

Simplification of a complex system of geochemical variables obtained from the soils of an industrialized area of Bombay is attempted by means of R-mode factor analysis. Prior to factor analysis, discriminant analysis was carried out taking rock and soil chemical data to establish the anthropogenic contribution of metals in soil. Trace elements (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) are expressed in terms of three rotated factors. The factors mostly indicate anthropogenic sources of metals such as atmospheric fallout, emission from different industrial chimneys, crushing operations in quarries, and sewage sludges. Major elements (Na, Mg, Al, Si, P, K, Ca, Ti, Mn, and Fe) are also expressed in terms of three rotated factors indicating natural processes such as chemical weathering, presence of clay minerals, and contribution from sewage sludges and municipal refuse. Summary statistics (mean, standard deviation, skewness, and kurtosis) for the particle size distribution were interpreted as moderate dominance of fine particles. Mineralogical studies revealed the presence of montmorillonite, kaolinite, and illite types of clay minerals. Thus the present study provides information about the metal content entering into the soil and their level, sources, and distribution in the area.     

Mineralogical and geochemical characteristics of Miocene pelitic sedimentary rocks from the south-western part of the Pannonian Basin System (Croatia): Implications for provenance studies

Fifty-two samples of Miocene pelitic sedimentary rock from outcrops on Medvednica,Moslavacka Gora and Psunj Mts.,and boreholes in the Sava Depression and the Pozega Sub-depression were investigated.These sediments formed in different marine(with normal and reduced salinity),brackish,and freshwater environments,depending on the development stage of the Pannonian Basin System.Carbonate minerals,clay minerals and quartz are the main constituents of all pelitic sedimentary rocks,except in those from Moslavacka Gora Mt in which carbonate minerals are not present.Feldspars,pyrite,opal-CT,and hematite are present as minor constituents in some rocks.Besides calcite,dependent on the sedimentary environment and diagenetic changes,high-magnesium calcite,aragonite,dolomite and ankerite/Cadolomite are also present.Smectite or illite-smectite is the main clay minerals in the samples.Minor constituents,present in almost all samples,are detrital illite and kaolinite.In some samples chlorite is also present in a low amount.Major elements,trace elements and rare earth elements patterns used in provenance analysis show that all analysed samples have a composition similar to the values of the upper continental crust(UCC).The contents of major and trace elements as well as SiO_2/Al_2O_3,K_2O/Al_2O_3,Na_2O/K_2O,Eu/Eu~*,La/Sc,Th/Sc,La/Co Th/Co,Th/Cr,Ce/Ce~* and LREE/HREE ratios,show that the analysed pelitic sedimentary rocks were formed by weathering of different types of mostly acidic(silicic),i.e.felsic rocks.     

Discriminant analysis of the geomorphic characteristics and stability of landslide dams

Landslides can cause the formation of dams, but these dams often fail soon after lake formation. Thus, rapidly evaluating the stability of a landslide dam is crucial for effective hazard mitigation. This study utilizes discriminant analysis based on a Japanese dataset consisting of 43 well documented landslide dams to determine the significant variables, including log-transformed peak flow (or catchment area), and log-transformed dam height, width and length in hierarchical order, which affect the stability of a landslide dam. The high overall prediction power (88.4% of the 43 training cases are correctly classified) and the high cross-validation accuracy (86%) demonstrate the robustness of the proposed discriminant models PHWL (with variables including log-transformed peak flow, and log-transformed dam height, width and length) and AHWL (with variables including log-transformed catchment area, and log-transformed dam height, width and length). Compared to a previously proposed “DBI” index-based graphic approach, the discriminant model AHV – which uses the log-transformed catchment area, dam height, and dam volume as relevant variables – shows better ability to evaluate the stability of landslide dams. Although these discriminant models are established using a Japanese dataset only, the present multivariate statistical approach can be applied for an expanded dataset without any difficulty when more completely documented worldwide landslide-dam data are available.     

The Effect of Particulate Matter Components on the Acidity of Rain in Upper Silesia (Poland)

Analysis of data characterizing the chemical composition of atmospheric precipitation was presented, with an emphasis on components responsible for neutralization of rain acidity. For this purpose, chemometric methods were applied. Based on a principal component analysis (PCA) a strong correlation between precipitation pH and potassium and ammonium ions in the heating period (October–March) and potassium and sodium ions in the non‐heating period (April–September) was observed. Additionally, a classification of eight variables, i.e., Na⁺, K⁺, Mg²⁺, Ca²⁺, , , Cl^?, and according to their similarities was made using a cluster analysis. Based on this study, two ions, potassium and ammonium, together with the pH value were classified into one group (cluster) in the heating period while in the non‐heating period ions of potassium and sodium were clustered together with the pH. The results of the cluster analysis indicated that the selected ions contributed the most to the neutralization of the atmospheric precipitation acidity. This relationship was confirmed by a discriminant analysis in which potassium and ammonium ions were selected as components of the highest potential for precipitation classification according to its acidity degree. The relationship between the precipitation pH and the number of non‐precipitation days preceding the precipitation was also analyzed. It was found that although the observed an increase of the pH value was not very high, nevertheless, the effect of the duration of the period preceding the precipitation on the pH value recorded on the day of the precipitation occurrence was quite evident.     

Classifiers vs. input variables—The drivers in image classification for land cover mapping

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.     

Who’s at-risk? A case study of the demographic and socioeconomic characteristics of census tracts experiencing predatory and abusive mortgage lending in Philadelphia

Predatory lending and abusive practices arose as a serious danger by increasing the threat of foreclosure and bankruptcy and decreasing the equity accumulated in a housing unit, reducing many of the economic benefits of homeownership. This article describes the characteristics of areas who likely suffered from abusive mortgage lending using Census data, Home Mortgage Disclosure Act data, and publicly recorded mortgage data. A proxy measure of predatory lending is developed based on the loan-to-value (LTV) ratio and state anti-predatory lending regulations, and represents excessively leveraged properties. Discriminant analysis is used to distinguish between areas with these highly leveraged properties and other parts of the City of Philadelphia. The residuals are mapped to evaluate the spatial dimension of the discriminant models across the city. This examination of demographic and socioeconomic characteristics informs efforts to mitigate the consequences and prevent future occurrences of both predatory lending and other manifestations of inequality by viewing it as a spatial and neighborhood phenomenon.     

Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices

The quality and yield of tea depends upon management of tea plantations, which takes into account the factors like type, age of plantation, growth stage, pruning status, light conditions, and disease incidence. Recognizing the importance of hyperspectral data in detecting minute spectral variations in vegetation, the present study was conducted to explore applicability of such data in evaluating these factors. Also stepwise discriminant analysis and principal component analysis were conducted to identify the appropriate bands for accessing the above mentioned factors. The Green region followed by NIR region was found as most appropriate best band for discriminating different types of tea plants, and the tea in sunlit and shade condition. For discriminating age of plantation, growth stage of tea, and diseased and healthy bush, Blue region was most appropriate. The Red and NIR regions were best bands to discriminate pruned and unpruned tea. The study concluded that field hyperspectral data can be efficiently used to know the plantation that need special care and may be an indicator of tea productivity. The spectral signature of these characteristics of tea plantations may also be used to classify the hyperspectral satellite data to derive these parameters at regional scale.     

Derivation of the chalk superficial deposits of the North Downs, England: an application of discriminant analysis

Stepwise discriminant analysis was used on particle size and heavy mineral data from 276 sediment samples, including 39 from published sources, to distinguish between Palaeocene and Plio–Pleistocene deposits, found above the Cretaceous Chalk in southeast England, and to determine parent materials of the soils and superficial deposits of the central North Downs. Of 71 known cases (in situ deposits), 85.9% were correctly classified using textural data (sig. 99.9%), and 89.7% of 29 known cases (sig. 99.9%), using heavy mineral data. The best textural discriminating variables were %sand, graphic mean, %clay and graphic skewness, and the best heavy mineral discriminants were %kyanite plus staurolite, %andalusite and %garnet. Discriminant analysis has achieved good differentiation between marine Plio–Pleistocene sands, Clay-with-flints, loess and Palaeocene groups, and allowed objective classification of sediments of unknown origin. The distribution of parent materials revealed, strongly upholds the earlier interpretation [Z. Geomorphol. 39 (1995) 433] that in the central North Downs, except where Palaeocene residuals survive, the superficial cover above the Chalk consists of in situ Clay-with-flints sensu stricto, beneath Plateau Drift and loess. Clay-with-flints is seen as residual, and Plateau Drift as a Pleistocene soliflucted deposit. Textural affinities of Reading Beds Clay and Clay-with-flints elicited by a third discriminant analysis, using additional published data from 22 samples, suggest their common derivation from Chalk residua.     

Climatic control of the assemblages of secondary clay minerals in the A-horizon of United States soils

The concentrations (per cent) of secondary clay minerals (mica, vermiculite, montmorillonite, chlorite and kaolinite) in the A-horizon of 99 soil sample sites were entered into a cluster program to determine the common clay mineral assemblages found in U.S. soils. Five major assemblages were identified with the most common assemblage consisting of mica, montmorillonite and kaolinite. A suite of climatic variables was determined for each of the 99 sites, including the water balance variables and Arkley's leaching index. A discriminant analysis was then used to predict the occurrence of a clay assemblage from the climate data. Sixty per cent of the sites could be correctly predicted, although not all assemblages could be predicted equally well; two assemblages could be predicted with 88 and 82 per cent accuracy. The precipitation of the wettest month and Arkley's leaching index accounted for most of the predictive power. Thus this study supports theoretical models which suggest that seasonal moisture supply is an important control of clay genesis and persistence. A residuals analysis indicated that many of the palaeosols in the data set and many of the soils from glacial parent materials possessed clay mineral assemblages not expected on the basis of modern climate.     

反褶积γ测井特征参数α的根式计算及其与判别计算的对比研究

本文提出了一个新的计算方法——反褶积γ测井特征参数α的根式计算,论述了α的判别式计算,通过实测对比得出根式计算优于判别式计算的结论。