The removal of organic micropollutants (MPs) from water by means of adsorption is determined by the physicochemical properties of the adsorbent and the MPs. It is challenging to predict the removal of MPs by specific adsorbents due to the extreme diversity in physicochemical properties among MPs of interest. In this research, we established Quantitative StructureeActivity Relationships (QSARs) between the physicochemical properties of a diverse set of MPs and their distribution coefficients (KD) measured on coconut shell activated carbon (CCAC) and porous b-cyclodextrin polymer (P-CDP) adsorbents. We conducted batch experiments with a mixture of 200 MPs and used the data to calculate KD values for each MP on each adsorbent under conditions of infinite dilution (i.e., low adsorbate concentrations). We used computational software to calculate 3656 molecular descriptors for each MP. We then developed and applied a model-selection workflow to identify the most significant molecular descriptors for each adsorbent. The functional stability and predictive power of the resulting QSARs were confirmed with internal cross validation and external validation. The applicability domain of the QSARs was defined based on the most significant molecular descriptors selected into each QSAR. The QSARs are predictive tools for evaluating adsorption-based water treatment processes and provide new insights into CCAC and P-CDP adsorption mechanisms.

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