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

Area of Science:

• Environmental Science
• Geospatial Analysis
• Analytical Chemistry

Background:

• Hyperspectral remote sensing offers rapid, cost-effective assessments for heavy metal soil contamination (HMSC).
• Previous studies highlight the potential of spectral data but require integration with ground-truth geochemical data for robust modeling.

Purpose of the Study:

• To investigate the combined potential of field/laboratory spectroradiometry and geochemical data for quantifying HMSC.
• To develop and validate heavy metal prediction models (HMPM) for lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd).

Main Methods:

• Collected field and laboratory spectra (350-2500 nm) from 85 contaminated soil samples.
• Developed spectral libraries and conducted geochemical analyses using atomic absorption spectrometry.
• Utilized stepwise multiple linear regression to build eight HMPMs integrating spectral features and metal concentrations.

Main Results:

• Successfully developed field- and lab-based spectral libraries.
• Identified specific spectral regions crucial for HMSC modeling.
• Demonstrated the feasibility of predicting HMSC in a highly contaminated floodplain using combined spectral and geochemical data.

Conclusions:

• This study presents the first successful prediction of HMSC in a floodplain site by integrating soil geochemistry and field spectroradiometry.
• The developed HMPMs can be utilized with space-borne hyperspectral sensors for large-area heavy metal concentration mapping.
• Confirms the viability of combining geochemical analyses with field spectroradiometric data for accurate heavy metal prediction.