Pb In Topsoil Research Articles

Levels and pedogeochemical mapping of lead and chromium in soils of Barcelona province (NE Spain)

In this paper an analysis of the spatial distribution of Cr and Pb in topsoils in Barcelona province using 319 samples (approximately in a regular 5 km spaced grid) is presented. Ordinary kriging (OK) maps are obtained after examining and modelling observational variograms. A verification using cross-validation methods has been applied to evaluate the performance of the resulting maps for both Cr and Pb OK maps, which have also been compared to inverse distance weighting (IDW) interpolated maps, used as an additional benchmark. Both Cr and Pb OK maps have been obtained with better cross-validation scores in terms of median residuals and correlations between estimates and sampled values. Interpolated maps have been used to calculate the area of the province above a soil target threshold (Cr: 100 mg kg −1 and Pb: 85 mg kg −1)—higher intervention values were not observed. Target values corresponded to the 99.8 percentile for the Cr (9 km 2, 0.2% of the area of Barcelona province) and 92.7% percentile for the Pb (567 km 2, i.e. 7.2% of the province).

Predictive Quality of Pedotransfer Functions for Estimating Bulk Density of Forest Soils

Pedotransfer functions (PTFs) based on easily measured soil variables offer an alternative for labor‐intensive bulk density (ρb) measurements. The predictive quality of 12 published PTFs was evaluated using an independent dataset of forest soils (1614 samples) from Flanders, Belgium. For all samples, PTF accuracy and precision was calculated, and for topsoil and subsoil samples separately. All functions were found to produce a systematic underestimation of predicted ρb, with mean prediction errors (MPEs) ranging between −0.01 and −0.51 Mg m−3 Most PTFs performed differently when applied to topsoil or subsoil data. Prediction of topsoil ρb showed the highest prediction error. The evaluation demonstrated the poor performance of some published PTFs, and raised concern that the predictive ability of even the better models may not be adequate. Therefore, two candidate PTFs were recalibrated and validated. With recalibration, accuracy improved considerably and showed a near‐zero bias, but precision increased only slightly. The best fitted empirical model was based on loss‐on‐ignition (LOI): ρb = 1.775 − 0.173(LOI)1/2 Its predictive capacity was not significantly better than the Adams physical two‐component model ρb = 100/{(LOI/0.312) + [(100 − LOI)/1.661]}. For the prediction of ρb in forest soils, LOI was two times more important than texture variables, and LOI alone accounted for >55% of the total variation. The lowest root mean squared prediction error (RMSPE) was 0.16 Mg m−3 for LOI‐based, and 0.21 Mg m−3 for texture‐based models. Separate calibration of topsoil and subsoil layers did not enhance the predictive capacity significantly.

Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination.

Describing contaminant spatial distribution is an integral component of risk assessment. Application of geostatistical techniques for this purpose has been demonstrated previously. These techniques may provide both an estimate of the concentration at a given unsampled location, as well as the probability that the concentration at that location will exceed a critical threshold concentration. This research is a comparative study between multiple indicator kriging and kriging with the cumulative distribution function of order statistics, with both local and global variograms. The aim was to determine which of the four methods is best able to delineate between "contaminated" and "clean" soil. The four methods were validated with a subset of data values that were not used in the prediction. Method performance was assessed by calculating the root mean square error (RMSE), analysis of variance, the proportion of sites misclassified by each method as either "clean" when they were actually "contaminated" or vice versa, and the expected loss for each misclassification type. The data used for the comparison were 807 topsoil Pb concentrations from the inner-Sydney suburbs of Glebe and Camperdown, Australia. While there was very little difference between the four methods, multiple indicator kriging was found to produce the most accurate predictions for delineating "clean" from "contaminated" soil.

The distribution of V, Ni, Cr, Cd, and Pb in topsoils of the Shuaiba industrial area of Kuwait

Soil samples were collected from selected locations in the Shuaiba Industrial Area of Kuwait from August 1993 to June 1994. The samples were analyzed for trace metal levels to determine the effect of the large oil spill in this area during the Gulf War of 1991. Sample solutions were prepared and analyzed using atomic absorption spectrophotometry. The results showed that the surface soil samples from 0 to 10 cm contain as much as 10.96–112.80 mg/kg of V, 2.49–131.60 mg/kg of Ni, 26.80–138.12 mg/kg of Cr, 0–0.06 mg/kg of Cd, and 0.99–41.29 mg/kg of Pb. The levels reported had an abundance order of Cr > Ni > V > Pb > Cd in the soil samples. This is the first study providing information on a number of toxic metal distributions in the soil of Kuwait. It is recommended that appropriate measures be put in place by concerned industries to treat waste materials before discharging them into the immediate environment and to fund monitoring activities of research organizations in Kuwait. © 1996 by John Wiley & Sons, Inc.

The distribution of V, Ni, Cr, Cd, and Pb in topsoils of the Shuaiba industrial area of Kuwait

The distribution of V, Ni, Cr, Cd, and Pb in topsoils of the Shuaiba industrial area of Kuwait

The mobility of heavy metals in soils in the Kru?n� Hory and Slavkovsk� Les mountains

In the North Bohemian forest soils the total content and EDTA, HAc and H2O-extractable portions of Cr, Co, Ni, Cu, Pb and Zn were investigated. Increased concentration of Pb in topsoils, in comparison with subsoils, is the result of air pollution. The extractable portion of all studied heavy metals is higher in topsoil than in subsoil.