Soil contamination from urban and industrial activity: example of the mining district of Linares (southern Spain)

The Linares region (southern Spain) has been subjected to two important sources of pollution: the intensive mining works and the urban-industrial activity. To obtain a geochemical characterisation of the soil, 31 trace elements were analysed and 669 soil samples were collected. By means of clustering analysis, we identified groups of elements and grid squares in which relations could be established concerning soil lithology, urban and industrial activities and the degree of pollution impact; in addition, we were able to characterise the geochemical background of the study area. The multivariate study led us to identify four factors. Particularly important was factor 2, which represented the elements associated with mineral paragenesis (Cu, Pb, As, Co, Mn, Zn, Sn, Ba). This factor also contains elements related with an urban-industrial activity, such as Pb, Cu, Zn, As and Ba. Furthermore, we identified factor 4, associating Ni, V and Cr, and which is related to the use of fuels.     

Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily

A certain number of studies have been carried out in recent years that aim at developing and applying a model capable of assessing water erosion of soil. Some of these have tried to quantitatively evaluate the volumes of soil loss, while others have focused their efforts on the recognition of the areas most prone to water erosion processes. This article presents the results of a research whose objective was that of evaluating water erosion susceptibility in a Sicilian watershed: the Naro river basin. A geomorphological study was carried out to recognize the water erosion landforms and define a set of parameters expressing both the intensity of hydraulic forces and the resistance of rocks/soils. The landforms were mapped and classified according to the dominant process in landsurfaces affected by diffuse or linear water erosion. A GIS layer was obtained by combining six determining factors (bedrock lithology, land use, soil texture, plan curvature, stream power index and slope-length factor) in unique conditions units. A geostatistical multivariate approach was applied by analysing the relationships between the spatial distributions of the erosion landforms and the unique condition units. Particularly, the density of eroded area for each combination of determining factors has been calculated: such function corresponds, in fact, to the conditional probability of erosion landforms to develop, under the same geoenvironmental conditions. In light of the obtained results, a general geomorphologic model for water erosion in the Naro river basin can be depicted: cultivated areas in clayey slopes, having fine-medium soil texture, are the most prone to be eroded; linear or diffuse water erosion processes dominate where the topography is favourable to a convergent or divergent runoff, respectively. For each of the two erosion process types, a susceptibility map was produced and submitted to a validation procedure based on a spatial random partition strategy. Both the success of the validation procedure of the susceptibility models and the geomorphological coherence of the relationships between factors and process that such models suggest, confirm the reliability of the method and the goodness of the predictions.     

Heavy metals in marine sediments of Taranto Gulf (Ionian Sea, Southern Italy)

Al, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Ti and Zn concentrations have been determined in surface sediment samples collected in the Taranto Gulf (Ionian Sea, Southern Italy) in order to evaluate their levels and spatial distribution in this important area of the Mediterranean Sea. For various metals, the geoaccumulation index has been calculated as a criterion to assess if their concentrations represent contamination levels or can be considered as background levels. The results show that metals concentrations in sediments can be considered near the background levels found in the Mediterranean Sea.Analytical results have been elaborated by using a Geographical Information System (GIS) software to show metals accumulation areas. Using multivariate statistical analysis, we evaluate the possibility to distinguish the sampling stations, in relation to their geographical position. Results have showed metals distribution in the Taranto Gulf is principally influenced by industrial and urban wastes, located mostly in the northern coastal area of the Ionian Sea. Rivers in the Basilicata region and prevailing anticlockwise marine currents are further factors influencing metal accumulation in sediments.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms

The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model (Y−E _Y = (X−E _X ) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E _Y and E _X . Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem by using the nonlinear Euler–Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition. For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335–342). The differences between the standard least-squares and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion matrix of the estimated parameters.     

A multivariate statistical and geostatistical study on the geochemistry of allochtonous karst bauxite deposits in Hungary

A geochemical evaluation of the Szc-Halimba-Kisld area, Hungary, covering an area of more than 200 km² is presented using different statistical and geostatistical methods. The study area is a representative example of allochtonous karst bauxite accumulation. The three groups of deposits studied here have been explored and mined since 1950. Several thousand boreholes have been drilled, and bauxite cores were analyzed for the five main chemical components. A total of 80,000 pleces of analytical data were processed, followed by a geological examination of borehole logs and of mining excavations.The quantitative geochemical evaluation of the data set led to both geochemical and practical results: The geochemical behavior of the allochtonous, clastic karst bauxite deposits differs essentially from that of the autochtonous and parautochtonous ones, as well as that of the lateritic bauxite deposits. The deposits of the study area can be split into several subsequent geochemical-sedimentological units, each representing an event of bauxite transport and accumulation. Clear regional patterns can be revealed in the composition of these units. The geostatistically measured chemical variability of the geochemical units is rather different, the lowest units showing the smallest variability. The interrelations of the main chemical components are weaker and more irregular in the studied deposits than in the autochtonous lateritic bauxite deposits. Additional local genetic features, such as transport routes, can be delineated by the methods applied. Within each deposit, local changes of chemical composition and of its variability can be determined more precisely. These results can be used in bauxite prospecting and exploration, because areas of high or low bauxite quality can be predicted.     

Impact of Southern Oscillation on the Frequency of Monsoon Depressions in the Bay of Bengal

The impact of Southern Oscillation on thecyclogenesis over the Bay of Bengal duringthe summer monsoon has been investigated.The analysis of correlation coefficients(CCs) between the frequency of monsoondepressions and the Southern OscillationIndex (SOI) reveals that more depressionsform during July and August of El Niñoyears. Due to this, the seasonal frequencyof monsoon depressions remains little higherduring El Niño epochs even though thecorrelations for June and September are notsignificant. The CCs for July and August aresignificant at the 99% level.The El Niño-Southern Oscillation (ENSO)is known to affect Indian MonsoonRainfall (IMR) adversely. The enhancedcyclogenesis over the Bay of Bengal duringJuly and August is an impact of ENSO whichneeds to be examined closely. Increasedcyclogenesis over the Bay of Bengal may bereducing the deficiency in IMR duringEl Niño years by producing more rainfallover the eastern parts of India duringJuly and August. Thus there is a considerablespatial variation in the impact of ENSOon the monsoon rainfall over India and El Niñoneed not necessarily imply a monsoonfailure everywhere in India.The area of formation of monsoon depressionsshifts eastward during El Niño years.Warmer sea surface temperature (SST) anomaliesprevail over northwest and adjoiningwestcentral Bay of Bengal during premonsoon andmonsoon seasons of El Niño years.May minus March SOI can provide useful predictionsof monsoon depression frequencyduring July and August.     

基于地震属性的煤层厚度预测模型及其应用

地震属性技术在岩性和构造解释等方面得到了越来越广泛的应用,特别是在煤、油气资源勘探中具有重要的作用.基于淮南矿区谢桥1区13 1煤层地震勘探资料,提取了28种地震属性数据;通过地震属性的分析,优选出平均峰值振幅、振幅的峰态、最大绝对振幅、瞬时频率斜率等4种地震属性作为煤层厚度预测模型基本参数,结合已知钻孔资料,利用多元多项式回归以及BP人工神经网络方法,求出了各属性与煤厚之间的多元多项式回归模型及人工神经网络模型,并对模型进行了误差分析和应用结果对比分析,反映出人工神经网络模型在煤厚预测中具有好的应用效果.     

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.     

The role of water mass dynamics in controlling bacterial abundance and production in the middle Adriatic Sea

Month-to-month fluctuations in the abundance of bacteria and heterotrophic nanoflagellates (HNF) and bacterial production, as well as various chemical (nutrients, oxygen) and physical (salinity, temperature) parameters were analysed at a station located in the open middle Adriatic Sea during one decade (1997-2006). Being influenced by both coastal waters and open Adriatic circulation in the surface layer, and by the deep Adriatic water masses in the deep layers (100 m), this station is quite suitable for detecting the environmental changes occurring in the open Adriatic Sea with respect to the circulation of its water masses and their long-term changes and anomalies. Multivariate methods were used to identify seasonal and inter-annual changes of the investigated parameters, associating observed changes to the changes in Adriatic water masses and circulation regimes. The analyses showed that bacterial abundance and production were controlled by different water mass dynamics during 1997-2001 compared to 2002-2006 period, particularly noticeable in different seasonal patterns of biological parameters. The interplay between North Adriatic Dense Water (NAdDW) and Levantine Intermediate Water (LIW) resulted in a change in the available nutrients (NAdDW is poor in orthophosphates), and as a consequence different bacterial abundance and production. A few periods were examined in detail, such as 2004, when LIW inflow was particularly strong and was accompanied by an increase of bacterial and HNF abundances, as well as of bacterial production.