Modeling for the estimating the adsorption property of fruit waste-based biosorbents for the removal of organic micropollutants.

The enormous production of fruit waste and the generation of countless organic micropollutants are serious environmental problems. To solve the problems, the biowastes, i.e., orange, mandarin, and banana peels, were used as biosorbents to remove the organic pollutants. In this application, the difficult challenge is knowing the degree of adsorption affinity of biomass for each type of micropollutant. However, since there are numerous micropollutants, it requires enormous material consumption and labor to physically estimate the adsorbability of biomass. To address this limitation, quantitative structure-adsorption relationship (QSAR) models for the adsorption assessment were established. In this process, the surface properties of each adsorbent were measured with instrumental analyzers, their adsorption affinity values for several organic micropollutants were determined through isotherm experiments, and QSAR models for each adsorbent were developed. The results showed that the tested adsorbents had significant adsorption affinity for cationic and neutral micropollutants, while the anionic one had low adsorption. As a result of the modeling, it was found that the adsorption could be predicted for a modeling set with an R2 of 0.90-0.915, and the models were validated via the prediction of a test set that was not included in the modeling set. Also, using the models, the adsorption mechanisms were identified. It is speculated that these developed models can be used to rapidly estimate adsorption affinity values for other micropollutants.