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Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom

1
Faculty of Natural Sciences, Life and Earth Sciences, University Akli Mohand Oulhadj of Bouira, 10000 Bouira, Algeria
2
Department of Geography and Earth Sciences, University of Aberystwyth, Ceredigion, Wales SY23 3DB, UK
3
Department of Soil and Water Resources, Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1, Theofrastou St., 41335 Larisa, Greece
4
School of Mineral and Resources Engineering, Technical University of Crete, Kounoupidiana Campus, 73100 Crete, Greece
5
Faculty of Biological Sciences, University of Sciences and Technology Houari Boumediene, BP 32, El Alia, Bab Ezzouar 16111, Algeria
6
Institute of Environment and Sustainable Development & DST Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi 221005, India
7
Department of Agroecology, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
8
Department of Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania, 73100 Crete, Greece
9
School of Geography, College of Science, University of Lincoln, Brayford Pool, Lincoln, Lincolnshire LN6 7TS, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 762; https://doi.org/10.3390/s19040762
Received: 23 November 2018 / Revised: 30 January 2019 / Accepted: 8 February 2019 / Published: 13 February 2019
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Abstract

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations. View Full-Text
Keywords: hyperspectral data; heavy metals; floodplain; soil spectral library; regression modelling hyperspectral data; heavy metals; floodplain; soil spectral library; regression modelling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Lamine, S.; Petropoulos, G.P.; Brewer, P.A.; Bachari, N.-E.-I.; Srivastava, P.K.; Manevski, K.; Kalaitzidis, C.; Macklin, M.G. Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom. Sensors 2019, 19, 762.

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