Deriving parametric and probabilistic Kd values for fluoroquinolones in soils

Abstract

The evaluation of the sorption affinity of fluoroquinolone antibiotics (FQs) in soils, by means of the derivation of solid-liquid distribution coefficients (Kd), is a valuable information for assessing their environmental mobility. Aiming to develop Kd (FQ) prediction tools in soils, in the first stage of this study we constructed a Kd (FQ) sorption dataset using current literature data. Furthermore, additional sorption and desorption data for norfloxacin were obtained in seven different soils of contrasting properties. Sorption isotherms of norfloxacin were linear under the experimental conditions tested and desorption percentages increased for scenarios in which low sorption was noted. Sorption tests in the same soils were then extended to ciprofloxacin, enrofloxacin and ofloxacin and pooled in the dataset, revealing comparable Kd (FQ) values among the FQ tested after analyzing the overall dataset consisting in 312 entries of Kd (FQ). A partial least square (PLS) regression model was then developed to predict values of Kd (FQ) based on specific relevant soil properties (i.e., pH, cation exchange capacity and organic carbon and texture information), and, for the first time, FQ properties (fraction of cationic FQ species) affecting sorption. Additionally, probabilistic, Kd (FQ) best estimates in soils were derived through cumulative distribution functions (CDFs) for the overall and for partial datasets created by grouping Kd (FQ) values according to key soil properties affecting FQ sorption (i.e., pH, organic carbon content and texture information). This latter approach permitted to derive more representative Kd (FQ) best estimates for the soils to be assessed, and with a lower related variability than that derived from the overall dataset. Best estimates Kd (FQ) values were > 1000 L kg−1 for most acidic to neutral soils, suggesting strong sorption, although lower sorption and thus higher environmental mobility may be expected in scenarios with soils with alkaline pH, low OC and high sand contents.