A Mechanism Study of Reflectance Spectroscopy for Investigating Heavy Metals in Soils

Wu, Yunzhao; Chen, Jun; Ji, Junfeng; Gong, Ping; Liao, Qilin; Tian, Qingjiu; Ma, Hongchao

doi:10.2136/sssaj2006.0285

Cited by 195 publications

(148 citation statements)

References 35 publications

Supporting

Mentioning

126

Contrasting

Unclassified

Order By: Relevance

“…The parent material in Qatar soils is dominated by dolomite and limestone and normally has very low organic matter, clay and iron contents [38], and the toxic metals' concentrations in Qatar soils are generally low. Therefore, it would be difficult to capture spectral features of toxic metals directly from the soils, even though most of the soil in Qatar is bare [26,27,29,32,36,65].…”

Section: Seasonal Dependency On Soil Toxic Metals' Determinationmentioning

confidence: 99%

“…Melendez-Pastor et al [25] comprehensively reviewed the role of RS in soil toxic metals' detection and found it to be an efficient tool for detecting sources of toxic metals' contamination in soils. This is despite the fact that toxic metals in soils appear in low or moderate concentrations (e.g., Cr and Cu <4000 mg·kg −1 ) and are spectrally featureless, although they can easily bind to minerals and organic matter [26][27][28]. Especially the reflectance spectral information of minerals in soils plays an important role in predicting toxic metals because the spectral assignments' position of minerals can change with their chemical composition and surface activity [27,[29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…This is despite the fact that toxic metals in soils appear in low or moderate concentrations (e.g., Cr and Cu <4000 mg·kg −1 ) and are spectrally featureless, although they can easily bind to minerals and organic matter [26][27][28]. Especially the reflectance spectral information of minerals in soils plays an important role in predicting toxic metals because the spectral assignments' position of minerals can change with their chemical composition and surface activity [27,[29][30][31][32]. This indirect mechanism allows the relationships between spectral information and soil toxic metals to be deduced since the information of Fe-oxides, clays and organic matter in soils or vegetation cover can be extracted from the RS spectra [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0-30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R 2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R 2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals' monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

show abstract

Section: Seasonal Dependency On Soil Toxic Metals' Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In the vis-NIR many trace elements are spectrally featureless, and only exhibit inherent spectral features at high concentrations (i.e., Cr and Cu at >4000 mg kg -1 ; Wu et al, 2007). Low levels of heavy metals can be indirectly predicted from spectra due to their association with Fe oxides, clays, and organic matter (Wu et al, 2010).…”

mentioning

confidence: 99%

“…Low levels of heavy metals can be indirectly predicted from spectra due to their association with Fe oxides, clays, and organic matter (Wu et al, 2010). The pollutant metals As, Cd, Cr, Cu, Hg, Pb, Ni, Sb, and Zn have been successfully determined by vis-NIR spectroscopy in soils and sediments (Kemper and Sommer, 2002;Kleinebecker et al, 2013;Malley and Williams, 1997;Wu et al, 2005Wu et al, , 2007, however, not all pollutant metals are predicted satisfactorily for each study site. Assessment of a full suite of soil geochemistry from large geographical datasets has shown a high number of elements to be determined by MIR spectroscopy Al, B, Be, Bi, Ca, Ce, Co, Cr, Cu, Cs, Fe, Ga, In, K, Li, Mg, Mn, Na, Nb, Ni, P, Rb, S, Sc, Sr, Ti, Th, V, Y, Zn, and Zr (Soriano-Disla et al, 2013a;Reeves and Smith, 2009).…”

mentioning

confidence: 99%

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

O’Rourke

Minasny

Holden

et al. 2016

Soil Science Soc of Amer J

View full text Add to dashboard Cite

Proposed legislation to secure and maintain soil quality in Europe has generated interest surrounding how best to characterize soil geochemistry, and how to assess and monitor soil contamination. Visible-near infrared (vis-NIr), mid-infrared (MIr), and portable X-ray fluorescence (pXrF) spectroscopy can reduce time and cost associated with new soil monitoring programs. Before becoming deployable, accuracy of these techniques needs to be quantified. This study investigated potential of these techniques to characterize a full suite of soil geochemistry (40 elements), pH, and soil organic carbon (SOC) in a diverse set of agricultural soils from the Irish National Soil Database (NSDB) archive. Cubist models were employed to assess goodness of fit from spectrally derived estimates of each soil property. Then a model ensemble, or model averaging, approach was tested to examine if combining model outcomes of either vis-NIr or MIr with pXrF could improve accuracy of soil property prediction. In total, 15 of the 42 soil properties could be predicted to a good accuracy status using individual spectral methods, mostly achieved by MIr and pXrF. Combining model outcomes of vis-NIr or MIr with pXrF resulted in a positive improvement, increasing the number of soil properties that could be predicted from 15 to 25. Most notably is the large number of trace elements (As, Cd, Co, Cu, Hg, Mn, Ni, and Zn) predicted to good or reasonable accuracy. It was concluded that the synergistic use of vis-NIr, MIr, and pXrF spectral methods is well placed as a tool to permit large scale routine soil monitoring.Abbreviations: ICP, inductively coupled plasma; IQ, interquartile range; MIR, midinfrared; NSDB, Irish National Soil Database; pXRF, portable X-ray fluorescence; RPIQ, ratio of performance to inter quartile distance; SOC, soil organic carbon; vis-Nir, visiblenear infrared.T he strategy for protection of soils in Europe (European Commission, 2006a) recognizes a number of degradation processes or 'threats' to important soil functions. Among these is soil contamination arising from the introduction of substances on/in the soil that hamper soil functions, or present risks to human health or the environment. Proposed legislation for the protection of soils in Europe outlines a plan for member states to identify contaminated sites and introduce a program of remediation (European Commission, 2006b). A lack of legislation for soil protection in Europe (aside from the Directive on the management of waste from extractive industries [European Commission, 2006c]) makes regulation of contaminated sites difficult ( Jones et al., 2012). In the 39 countries of the European Environment Agency, the total number of contaminated soil sites in 2011 was estimated to be 2.5 million. The key sources of soil contamination arose from waste disposal and treatment, industrial, and commercial activities (e.g., mining, oil extraction, and power plants), storage (e.g., oil, oil extraction, and obsolete chemicals), transport spills on land (e.g., oil and other hazardou...

show abstract

A spectroscopic approach to assess trace–heavy metal contents in contaminated floodplain soils via spectrally active soil components

Vohland

Bossung

Fründ

2009

Z. Pflanzenernähr. Bodenk.

View full text Add to dashboard Cite

Soil samples from grassland plots in the Hase floodplain near Osnabrück (NW Germany) were analyzed using visible and near‐infrared laboratory spectroscopy (VNIRS) by means of an ASD FieldSpec II Pro FR‐ spectroradiometer (spectral range 0.4–2.5 µm), paralleled by atomic‐absorption spectrometry (AAS), automated combustion for Corg quantification, and texture analysis. By AAS, contents of Cu, Zn, and Pb were found to be clearly elevated which is due to industrial effluents into the river in the 19th and 20th century. As these heavy metals (HMs) cannot be assessed directly by VNIRS, it was one major task to clarify whether they can be quantified indirectly using intercorrelations with spectrally active soil components. A second goal was to identify the specific spectroscopic predictive mechanism which may also be applicable to assess trace‐HM contents of other soil samples similar to those investigated here. For the latter, Corg was found to be most indicative, whereas the binding of the metals to other constituents (Fe oxides, clay) was not utilizable in the spectroscopic approach. The measured spectra were subjected to a multiplicative scatter correction and afterwards used to establish partial least‐squares regression (PLSR) models to estimate the contents of the different soil constituents. For Corg, very reliable estimates were obtained for both calibration and validation samples (in the validation, r² amounted to 0.90 and the percentage root mean square error [RMSE] was equal to 30.6%). Estimation accuracies obtained by PLSR for the trace HMs were considerably lower (r² between 0.56 and 0.71, percentage RMSE > 50%), which can be traced back to moderate correlations with Corg as main spectral determinant. According to these results, VNIRS can be applied as rapid and precise screening method for Corg to complement traditional analytical methods and to be used efficiently for a large number of samples. The method of VNIRS may also be applied for an indirect estimation of Corg‐associated metals to address their spatial variability, for example. This issue appears to be of high importance for digital soil mapping by imaging spectroscopy on a local to medium spatial scale.

show abstract

A Mechanism Study of Reflectance Spectroscopy for Investigating Heavy Metals in Soils

Cited by 195 publications

References 35 publications

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

A spectroscopic approach to assess trace–heavy metal contents in contaminated floodplain soils via spectrally active soil components

Contact Info

Product

Resources

About