The response of steroid estrogens bioavailability to various sorption mechanisms by soil organic matter extracted with sequential alkaline-extraction method from an agriculture soil

The long-term groundwater contamination risks posed by steroidal estrogens (SEs) in animal-manured agricultural soils are closely associated with the soil organic matter (SOM) content and composition. In this study, the bioavailability of estrone (E1) and 17β-estradiol (17β-E2) under different sorption mechanism in humic acids (HA1 and HA2) and humin (HM) extracted with sequential alkaline-extraction technique (SAET) were examined. These SOMs extracted by SAET showed various properties and sorption characteristics for SEs. The alkyl carbon and condensed SOM increased during SAET, but aromatic carbon decreased and the same trend for polarity. Quick sorption was the major SEs sorption mechanism on HA1 and HA2, which contributed more than 69%; whilst slow sorption rate was about 50% in soil and HM. The logKₒc values were proportional to the TOC of SOM according to Freundlich fitting, and the sorption capacity of sorbent for E1 and 17β-E2 was related to the logKₒw values, indicating that the main mechanism controlling the SEs sorption was hydrophobic interaction. The larger micropore volume of HM and soil was more conducive to the micropore filling of SEs. Meanwhile, the specific sorption of SEs on condensed domain of SOM was the main reason for the strong desorption hysteresis and slow sorption in HM and soil. The SEs degradation rate was positively correlated with the contribution rate of quick adsorption and negatively correlated with the contribution rate of slow adsorption, indicating that the bioavailability of SEs sorbed by hydrophobic interaction was higher than that of micropore filling or specific sorption, which was also the reason for the low bioavailability of SEs in HM and soil. This work confirms the regulation of on-site SOM compositions and their properties on SEs sorption and bioavailability. Characterization of these details is crucial for the improved prediction of long-term risks to groundwater.