Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China

Chen, Tao; Chang, Qingrui; Liu, Jing; Clevers, J.G.P.W.; Kooistra, Lammert

doi:10.1016/j.scitotenv.2016.04.163

Cited by 193 publications

(59 citation statements)

References 46 publications

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“…() and Jo & Koh () stated that organic carbon increased the concentration of heavy metals in soil because of increasing heavy metals in the soil solution and preventing them from being complexed with other ions. Moreover, some researchers have found a significant and positive correlation between TOC and total heavy metals (Massas et al ., ; Yang et al ., ; Chen et al ., ).…”

Section: Resultsmentioning

confidence: 99%

The Influence of Organic Carbon and pH on Heavy Metals, Potassium, and Magnesium Levels in Lithuanian Podzols

et al. 2016

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Industrial activities can contribute to the accumulation of heavy metals in soils, which could potentially threaten public health and the environment. This research was conducted to investigate the relationships between pH and total organic carbon (TOC) with soil chemical parameters, including exchangeable and total Cu, Zn, Cd, Pb, K, and Mg concentrations in soils near Panevėžys and Kaunas, Lithuania. Principal component regression (PCR) and non‐linear regression were used to find statistical relationships between pH, TOC, and the other soil properties studied. The results of correlation tests indicated that pH and TOC had strong relationships with most of the soil properties. The results of PCR [R2 = 0·87, RMSE = 0·046] and non‐linear regression [R2 = 0·91, RMSE = 0·041] (pH and the entire parameters), PCR [R2 = 0·777, RMSE = 0·058] and non‐linear regression [R2 = 0·871, RMSE = 0·046] (pH and the exchangeable parameters) to model the relationships between pH and soil chemical properties were promising and significant. Exchangeable heavy metal concentrations increased for pH > 5. Even though the relationships between TOC and heavy metals were significant, they were not as powerful as the relationships between pH and these metals. It was concluded that total metal concentrations in the study soils can be predicted by either pH or TOC. Metal mobility could most likely be controlled at the study site by manipulating soil pH and/or TOC. Finally, it is suggested that when there are financial and time limitations, assessment of total exchangeable metal concentrations using soil pH and/or TOC could be productive. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

The Influence of Organic Carbon and pH on Heavy Metals, Potassium, and Magnesium Levels in Lithuanian Podzols

et al. 2016

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“…PCA is widely used for distinguishing between different sources of heavy metals in complex matrices by reducing the number of observed variables. 5,6,35 The tests for normality on raw and log-transformed data were performed before PCA. To make the results more easily interpretable, PCA with VARIMAX normalized rotation was applied, which can maximize the variances of factor loadings across variables for each factor.…”

Section: Discussionmentioning

confidence: 99%

“…[9][10][11] Heavy metals in soils mainly originate from the weathering of parent materials and through anthropogenic emissions such as industrial waste, mining and smelting activities, and traffic. [4][5][6] Heavy metals in terrestrial ecosystems are also important sources of pollution of aquatic environments downstream, through surface runoff. [12][13][14] Additionally, some heavy metals or heavy-metal-containing compounds may be emitted into the atmospheric environment through volatilization (e.g., elemental Hg and monomethylarsine).…”

Section: Introductionmentioning

confidence: 99%

Variability of heavy metal content in soils of typical Tibetan grasslands

Liu

Yang

et al. 2016

RSC Adv.

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Relatively high contents of heavy metals were recently reported in the high-altitude Tibetan Plateau (TP) environment, but the source and distribution characteristics of heavy metals in grassland environments of the TP remain unclear. Here, we report the contents of Hg, Cd, As, Pb, Ni, Cr, Cu, and Zn in soils of typical grasslands from the western to eastern parts of the TP, and the factors regulating accumulation of these heavy metals in the soil. Results show a large degree of variability of the eight heavy metals in the topsoil (0-20 cm) of different grasslands of the TP. Distribution characteristics of the heavy metals in the subsoil (20-40 cm) from the different grasslands were similar to those in the topsoil. Concentrations of As, Cd, Cu, and Zn in grassland soil tended to decrease as longitude increased, whereas Hg content displayed an increasing trend with increasing longitude after 88 E. These observations may be related to different sources of the heavy metals in the soils. Our results suggest that Cu, Zn, Cr, Cd, Ni, and As were mainly derived from natural sources, whereas anthropogenic activities could be responsible for the accumulation of Pb and Hg in the soil. Positive correlation between average annual rainfall amounts and soil Hg content may suggest the contribution of precipitation to soil Hg content. However, accumulation and distribution of heavy metals in the grasslands also depend on soil characteristics which could influence the mobility of heavy metals. These findings have important implications for an understanding of the occurrence and accumulation of soil heavy metals in grasslands of high-altitude environments.

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“…The Pearson correlation coefficient is a measure of the linear dependence (correlation) between two variables X and Y, the greater the absolute value of the correlation coefficient, the greater the correlation between the two variables [20,21]. Thus, it is possible to predict heavy metal contents in the soil with the high correlation between heavy metal content and soil spectrum [22].…”

Section: Spectral Analysis and Model Developmentmentioning

confidence: 99%

Estimate of Heavy Metals in Soil Using Combined Geochemistry and Field Spectroscopy in Miyi Mining Area

Ji¹,

Yao²,

Chen³

et al. 2018

Heavy Metals

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Heavy metal-contaminated soil and water is a major environmental issue in the mining areas. However, as the heavy metals migrate frequently, the traditional method of estimating the soil's heavy metal content by field sampling and laboratory chemical analysis followed by interpolation is time-consuming and expensive. This chapter intends to use field hyperspectra to estimate the heavy metals in the soil in Bai-ma, De-sheng and YuanBaoshan mining areas, Miyi County, Sichuan Province. By analyzing the spectra of soil, the spectral features derived from the spectra of the soils can be found to build the models between these features and the contents of Mn and Co in the soil by using the linear regression method. The spectral features of Mn are 2142 and 2296 nm. The spectral features of Co are 1918, 1922 and 2205 nm. With these feature spectra, the best models to estimate the heavy metals in the study area can be built according to the maximal determination coefficients (R 2 ). The determination coefficients (R 2 ) of the models of retrieving Mn and Co in the soil are 0.645 and 0.8, respectively. The model significant indexes of Mn and Co are 2.04507E-05 and 7.73E-06. These results show that it is feasible to predict contaminated heavy metals in the soils during mining activities for soil remediation and ecological restoration by using the rapid and cost-effective field spectroscopy.Keywords: contaminated heavy metals in the soils, spectral measured, spectral analysis Heavy Metals 136

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Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China

Cited by 193 publications

References 46 publications

The Influence of Organic Carbon and pH on Heavy Metals, Potassium, and Magnesium Levels in Lithuanian Podzols

The Influence of Organic Carbon and pH on Heavy Metals, Potassium, and Magnesium Levels in Lithuanian Podzols

Variability of heavy metal content in soils of typical Tibetan grasslands

Estimate of Heavy Metals in Soil Using Combined Geochemistry and Field Spectroscopy in Miyi Mining Area

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