A method to analyze field predictors of heavy metal pollution in riparian soils and plants
Marco Polo Robaldi-Vázquez, Norma Patricia López-Acosta, Agueda E. Ceniceros‐Gómez, David Francisco Barba-Galdámez
Abstract
• A method to analyze heavy metals and their predictors in soil and plants is developed. • Thermal and electrical conductivities predict Pb and Ni content in lacustrine soils. • Vegetation indices (REP, MSAVI2, NPCI) predict Pb, Ni, and Ag in lacustrine soils. • The proposed statistical sequence reduces dimensionality and removes outlier effects. Heavy metal pollution impacts several rivers worldwide, particularly in low- and middle-income countries. Flooding can transport and spread this pollution to plain and riparian zones during extreme hydrometeorological events. Remote sensing can predict soil pollution levels, but this becomes complex with high variability datasets, outliers, or parts per billion concentrations. This study proposes a novel method for effectively analyzing associations between geochemical data (heavy metal concentrations in soil samples) and pollution predictors in riparian soil and plants. The method applies robust multivariate analysis techniques combined with biplots and a data exclusion criterion to field-collected datasets. A potentially polluted area in Mexico was selected, and ten soil properties (chemical, physical, and thermal) along with eight Hyperspectral Vegetation Indices (HVIs) were determined and evaluated via comparative analysis for their potential to predict heavy metal pollution. The method revealed associations between pollutants and predictors, minimizing dataset variability and removing outliers. The pilot area exhibited anthropogenic pollution, including high Ag, Se, and Tl levels in some samples and moderate Pb and Ni pollution. Findings suggest that thermal and electrical conductivities are effective indicators for Pb and Ni pollution in soils, comparable to the Red Edge Position (REP) and Modified Soil Adjusted Vegetation Index (MSAVI2). Soil Organic Matter (OM) and the Normalized Pigment Chlorophyll Index (NPCI) performed best for Ni and Ag at higher concentrations.