Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

For decades, bad practices in municipal and industrial waste management have had negative environmental impacts, generating high health risks for people and the environment. The use of badly designed, not engineered, and not well-operated landfills has, around the world, produced a large number of potentially contaminated sites, for which there are urgent needs to assess the actual risk and to proceed, in case, with reclamation activities. One of these sites, an abandoned waste disposal site located near a Site of Community Importance on the central-eastern coast of Sardinia (Italy), is the subject of the case history described in this work. As a part of a multi-method geophysical characterisation, a frequency-domain electromagnetic (FDEM) mapping survey was carried out with the specific aim of detecting the presence of buried materials (waste) and of delineating the lateral extent of the landfill by identifying the electrical conductivity anomalies produced, for the most part, by the conductive waste fill. Using an EM31 device in the vertical-dipole configuration, at a height of 0.9 m above the ground, both quadrature and in-phase electromagnetic responses were collected over a 7-hectare area with elevation varying between 6 m and 2.8 m above sea level. After removing the measurements identified as data coming from any recognisable surface man-made features within the survey area or near its perimeter, the filtered quadrature response (expressed as apparent conductivity) ranged from 5.5 mS/m to about 188.6 mS/m. All values are beyond the low induction number (LIN) condition and valid for the classical EM31 mapping, thus requiring advanced data processing. To obtain undistorted, meaningful, and interpretable high-resolution maps, measured data have been processed to correct the bias, introduced by the nonlinearity of the device, as a function of height above ground and the topography. The comparative analysis of the apparent conductivity map, obtained by the properly processed EM31 data and some aerial photos that clearly documented the site history, has allowed unequivocal delineation of the landfill extent, in good agreement with the results obtained with other geophysical methods (not described in this paper) and with the ground truthing data provided by three boreholes, which were core-drilled at the end of the study at three locations selected on the basis of the apparent conductivity map.

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“…Figure 14 shows the plots for the LIN ECa, which is the variable on w we focus our attention in the following sections. [64].…”

Section: Geostatistical Analysismentioning

confidence: 99%

Frequency-Domain Electromagnetic Mapping of an Abandoned Waste Disposal Site: A Case in Sardinia (Italy)

et al. 2022

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“…The spatial variability of heavy metal(loid)s was determined using the semi-variogram. The theoretical fitting of the variogram, and the nugget, sill, lag, and the range were defined as stated in [32,33], while the nugget effect (NE)was calculated as stated in Equation ( 4):…”

Section: Statistical Analysis Spatial Modeling and Validationmentioning

confidence: 99%

Sources, Spatial Distribution and Extent of Heavy Metals in Relation to Land Use, Lithology and Landform in Fuzhou City, China

Sodango

Sha

et al. 2021

Minerals

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Assessing the spatial distribution of soil heavy metals in urban areas in relation to land use, lithology and landform may provide insights for soil quality monitoring. This study evaluated the spatial distribution, the sources and the extent of heavy metal(loid)s in the topsoil of Fuzhou city, China. A combination of GIS and multivariate approaches was used to determine the spatial distribution and the sources of heavy metals. Additionally, analysis of variance was used to determine the variability of selected heavy metals across land use, landform, and lithology. The result show that the mean concentrations of Cd, Zn, As and Pb were higher than background values. Most of the heavy metals had significant correlations with each other. In particular, V and Fe (0.84 at p < 0.01) and Ni and Cr (0.74 at p < 0.01) had strong correlations, while Cu and Fe (0.68 at p < 0.01), Cu and V (0.63 at p < 0.01), Cu and Co (0.52 at p < 0.01), Zn and Ni (0.51 at p < 0.01), Co and Fe (0.54 at p < 0.01), and Cu and Zn (0.55 at p < 0.01) had moderate correlations. Arsenic, Cu, and Zn had significant positive correlations with total nitrogen (TN). Similarly, arsenic, Zn and Cr had positive correlations with total carbon (TC), while Co had negative correlations with TN and TC at p < 0.01. The peak values for Cr, Ni, Pb, Mn, and Zn were observed in the intensively urbanized central and eastern parts of the study area, suggesting that the main sources might be anthropogenic activities. Agricultural land use had the highest content of Cd, which may be attributed to the historical long-term application of agrochemicals in the area. Additionally, its content was significantly higher in agricultural land use with shale lithology, implying that shale lithology was a key geogenic source for Cd of soils in the study area. Pb content was affected by urban land use, which may be attributed to intensive human activities such as emissions from vehicles, industrial effluents, mining activities, and other discharges. The results show the high spatial variability of heavy metal(loid)s, implying that the soils in the study area were highly influenced by both geogenic variability and human activities. Moreover, land use and lithology had significant impacts on the variability of Cd, As and Pb. Sustainable agricultural practices and urban management are recommended to sustain the eco-environment of coastal city.

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“…Then, taking into account the six models, from the lowest to the highest MF value, the following ordered arrangement can be generated: Pentaspherical, Exponential, Cubic, Spherical, Gaussian and Wave. However, if the equivalent SDI (SDI*), proposed by Barbosa et al (2017), for the Power semivariogram model, whose equivalent MF (MF*) ranges from 0 to 0.667 (0<MF*<0.667), it is considered that the lowest value (SDI*>0%) and the highest value (SDI*<66.7%) of the SDI can be obtained for this model. But, this does not mean that one model is better than another, being only an inherent characteristic of each model, not representing fragility or less power to describe the spatial dependence behavior.…”

Section: Bulletin Of Geodeticmentioning

confidence: 99%

“…This index holds a spatial dependence classification proposed by Seidel, Oliveira (2016) and it is categorized as weak, moderate or strong spatial dependence. Recently, Barbosa et al (2017) constructed indexes and respective classifications for the evaluation of the spatial dependence for the power semivariogram model (model without sill). According to Barbosa et al (2017), the proposed indexes are analogous to the SPD and SDI.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Dependence Index for Cubic, Pentaspherical and Wave Semivariogram Models

et al. 2018

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This study aims to propose a spatial dependence index (and its classification), from the concept of spatial correlation areas, for the Cubic, Pentaspherical and Wave models. The index, called Spatial Dependence Index (SDI), covers the following parameters: the range (a), the nugget effect (C 0 ) and the contribution (C 1 ), beyond considering the maximum distance (MD) between sampled points and the model factor (MF). The proposed index, unlike the most used in the literature, considers the influence of the range parameter to describe the spatial dependence, highlighting the importance of this formulation. The spatial dependence classification, based on the observed asymmetric behavior in the SDI, was performed considering categorizations from the median and the 3rd quartile of the index. We obtain the spatial dependence classification in terms of weak, moderate, and strong, just as it is usually described in literature.

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Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

Cited by 4 publications

References 26 publications

Frequency-Domain Electromagnetic Mapping of an Abandoned Waste Disposal Site: A Case in Sardinia (Italy)

Frequency-Domain Electromagnetic Mapping of an Abandoned Waste Disposal Site: A Case in Sardinia (Italy)

Sources, Spatial Distribution and Extent of Heavy Metals in Relation to Land Use, Lithology and Landform in Fuzhou City, China

Spatial Dependence Index for Cubic, Pentaspherical and Wave Semivariogram Models

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