Abstract:This paper describes statistical and geostatistical analyses of data from a soil sampling survey. Soil sampling was performed, in October and November of 1985, to obtain information on the level, extent, and spatial structure of metal pollution of the soil in and around the Palmerton, Pennsylvania, NPL Superfund site. Measurements of the concentrations of cadmium, copper, lead, and zinc in the soil samples were obtained. An appropriate variance stabilizing transformation was determined. Estimation of variance … Show more
“…Heavy Metal Data Sets. The different kriging algorithms are illustrated and compared using two different data sets: 200 soil lead concentrations measured in the Dallas metropolitan area ( , ), and 412 soil cadmium concentrations collected in Palmerton, PA ( , ). The Dallas metropolitan area is the site of an industrial lead smelter that is depicted by a diamond in the location map of Figure a.…”
Section: Methodsmentioning
confidence: 99%
“…For each data set, the mean square error (MSE) of prediction by OK and various KLT algorithms was assessed using cross-validation whereby one observation at a time is temporarily removed from the data set and re-estimated from remaining data (). Prediction errors were systematically computed over all possible major wind directions (0°, 1°, ..., 359°) to identify the direction with the best prediction performance, which was then compared to the one reported by several authors for each site ( 11 , − . 5 Impact of the choice of wind direction on the proportion of variance explained ( R 2 ) by various global trend models for the two data sets and sampling intensities.…”
Section: Methodsmentioning
confidence: 99%
“…In this paper, we propose a variant of KT algorithm that allows one to integrate information about the location of the pollutant source and transport process into the spatial mapping of contaminants. The technique is applied to a Dallas metropolitan area contaminated with lead emitted by an industrial smelter ( , ) and a Palmerton (PA) site contaminated with cadmium emitted by two zinc smelters ( , ). Cross-validation is used to compare, over a range of wind directions, the prediction performances of the new technique with ordinary kriging, the traditional implementa tion of kriging with a trend, and the kriging with a global trend model.…”
This paper presents a variant of the well-known kriging with a trend that allows one to account for the pollutant source coordinates and information about transport process into the spatial prediction of pollutant concentration. The new technique is illustrated using lead data from a Dallas metropolitan area and cadmium data from Palmerton (PA) NPL Superfund site. Instead of modeling the local spatial trend as low-order polynomials of coordinates, it is here expressed as a function of two factors that likely control the pollution spread: the distance to the smelter and the deviation from the major wind direction. Four different combinations of these two factors are developed, and their prediction performances are evaluated for a range of wind directions using cross-validation. Comparison with two traditional algorithms (OK and KT) shows that the proposed approach leads to smaller mean square errors of prediction when the correct wind direction is determined. The best combination of trend model and wind direction is site-dependent and derived using cross-validation. Kriging of residuals from a global trend model is an alternative to the use of local trends, and both techniques are shown to outperform ordinary kriging.
“…Heavy Metal Data Sets. The different kriging algorithms are illustrated and compared using two different data sets: 200 soil lead concentrations measured in the Dallas metropolitan area ( , ), and 412 soil cadmium concentrations collected in Palmerton, PA ( , ). The Dallas metropolitan area is the site of an industrial lead smelter that is depicted by a diamond in the location map of Figure a.…”
Section: Methodsmentioning
confidence: 99%
“…For each data set, the mean square error (MSE) of prediction by OK and various KLT algorithms was assessed using cross-validation whereby one observation at a time is temporarily removed from the data set and re-estimated from remaining data (). Prediction errors were systematically computed over all possible major wind directions (0°, 1°, ..., 359°) to identify the direction with the best prediction performance, which was then compared to the one reported by several authors for each site ( 11 , − . 5 Impact of the choice of wind direction on the proportion of variance explained ( R 2 ) by various global trend models for the two data sets and sampling intensities.…”
Section: Methodsmentioning
confidence: 99%
“…In this paper, we propose a variant of KT algorithm that allows one to integrate information about the location of the pollutant source and transport process into the spatial mapping of contaminants. The technique is applied to a Dallas metropolitan area contaminated with lead emitted by an industrial smelter ( , ) and a Palmerton (PA) site contaminated with cadmium emitted by two zinc smelters ( , ). Cross-validation is used to compare, over a range of wind directions, the prediction performances of the new technique with ordinary kriging, the traditional implementa tion of kriging with a trend, and the kriging with a global trend model.…”
This paper presents a variant of the well-known kriging with a trend that allows one to account for the pollutant source coordinates and information about transport process into the spatial prediction of pollutant concentration. The new technique is illustrated using lead data from a Dallas metropolitan area and cadmium data from Palmerton (PA) NPL Superfund site. Instead of modeling the local spatial trend as low-order polynomials of coordinates, it is here expressed as a function of two factors that likely control the pollution spread: the distance to the smelter and the deviation from the major wind direction. Four different combinations of these two factors are developed, and their prediction performances are evaluated for a range of wind directions using cross-validation. Comparison with two traditional algorithms (OK and KT) shows that the proposed approach leads to smaller mean square errors of prediction when the correct wind direction is determined. The best combination of trend model and wind direction is site-dependent and derived using cross-validation. Kriging of residuals from a global trend model is an alternative to the use of local trends, and both techniques are shown to outperform ordinary kriging.
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