Multivariate analysis reveals significant diuron-related changes in the soil composition of different Brazilian regions.

Sorption and desorption determine the amount of an herbicide in soil solution. Therefore, knowledge of the sorption and desorption coefficients in different soils is an essential factor to estimate the potential for environmental contamination by herbicides. We evaluated the feasibility of multivariate and linear discriminant analyses to predict the sorption and desorption capacity of a soil for diuron, one of the most used herbicides on sugarcane plantations. The adsorptive capacity in twenty-seven Brazilian soil samples was estimated using the sorption constant (Kfs) and desorption constant (Kfd) obtained by the Freundlich isotherms. The regression model was created from the sorbed and nonsorbed concentrations of diuron in soils. Ultra-performance liquid chromatography was applied to quantify the diuron concentrations. The multivariate analysis separated the soils into four groups considering the similarity of the following attributes: pH, organic matter, clay, and base saturation. The groups showed a similar pattern of sorption and desorption for diuron: Lom-Lclay: low sorption (5.9 ± 1.2) and high desorption (10.9 ± 0.6); Lclay: low sorption (7.5 ± 1.1) and high desorption (11.4 ± 1.3); Hom-Hclay: high sorption (11.2 ± 1.2) and low desorption (13.8 ± 1.2); HpH-Hclay: high sorption (10.1 ± 1.1) and medium desorption (11.5 ± 1.4). Linear discriminant analysis of these soil attributes was used to classify other soils described in the literature with adsorption capacity. This analysis was able to identify soils with high and low sorption using the pH, organic matter, clay, and base saturation, demonstrating the enormous potential of the technique to group soils with different contamination risks for subterranean waters. Sugarcane crops in northeastern Brazil showed a higher pollution risk through the leaching of diuron. Multivariate analysis revealed significant diuron-related changes in the soil composition of different Brazilian regions; therefore, this statistical analysis can be used to improve understanding of herbicide behavior in soils.