Inclusion of key soil parameters in the modified contamination factor (MCF) model as a tool for assessing heavy metal pollution in agricultural soils

This study aimed to evaluate the applicability of a Modified Contamination Factor (MCF) model to assess heavy metal (HM) pollution in rural, urban, and industrial agricultural soils of Haridwar, India. Traditional Contamination Factor (CF) indices, though widely employed, fail to consider intrinsic soil buffering capacity, potentially misrepresenting ecological risk. To address this limitation, an MCF model was developed by integrating soil pH, organic matter (OM; %), and cation exchange capacity (CEC; cmol/kg) through a principal component analysis (PCA)-derived weight assignment method. Soil samples were collected and analyzed for nine HMs (Cd, Cr, Cu, Co, Fe, Mn, Ni, Pb, Zn) along with key soil properties including pH, organic matter (OM), and cation exchange capacity (CEC). Results revealed significant declines in soil pH (7.10 in rural to 6.30 in industrial), organic matter (2.50% to 1.20%), and cation exchange capacity (18.20 to 12.30 cmol/kg) from rural to industrial areas, reflecting progressive soil degradation. Concurrently, HM concentrations showed a significant increase, with Pb (79.5 mg/kg; permissible limit 85 mg/kg, USEPA) and Zn (188.3 mg/kg; permissible limit 300 mg/kg, WHO/FAO) approaching or exceeding guideline values in industrial zones. The MCF model yielded refined contamination estimates by incorporating adjustment factors (f), thereby magnifying contamination in low-retention soils and suppressing overestimation in resilient soils. Validation parameters (R² = 0.9729, RMSE = 0.190, MAE = 0.152, NSE = 0.939) also demonstrated high agreement between MCF and traditional CF values, supporting model strength. The MCF model improves upon the traditional CF by accounting for variations in soil properties, offering a more accurate and ecologically meaningful assessment of contamination risk.