Spectroscopic signatures for expeditious monitoring of contamination risks at abandoned coal mine sites

Abstract

Soil acts as a natural ‘filter’, playing a crucial role in the transfer of geogenic and anthropogenic pollutants from abandoned coal mine sites to surrounding water bodies. Key indicators of soil contamination, such as pH, electrical conductivity (EC), and organic matter (OM), expressed as loss-on-ignition (LOI), can signal contamination risks when they deviate from optimal ranges. To enable sustainable risk assessment through monitoring of pH, EC, and LOI, streamlined spectroscopic techniques Fourier transform infrared (FTIR), near-infrared (NIR), Raman, and X-ray fluorescence (XRF) were applied in combination with multivariate analysis (MVA), to soil samples from two abandoned coal mines in NW Portugal. Partial least squares (PLS) regression models demonstrated that XRF spectroscopic data provided the most accurate assessment of soil pH, EC, and LOI at the local scale (R² = 0.92–0.99). The most significant spectroscopic signatures, identified through weighted regression coefficients (B_w), enabled robust predictions of these key soil parameters. These findings highlight that these geochemical variables outperform molecular spectroscopy techniques for efficient and environmentally relevant risk monitoring of contamination in abandoned coal mine sites.