Models for predicting organic micropollutant breakthrough in carbon adsorbers based on water quality, adsorbate properties, and rapid small‐scale column tests

Rapid small‐scale column tests (RSSCTs) have been developed for simulating the removal of organic micro‐pollutants (OMPs) in full‐scale fixed‐bed carbon adsorbers to reduce time, materials, and labor costs. However, when dissolved organic matter (DOM) is present, OMP breakthrough profiles obtained from RSSCTs often do not match those at full‐scale because DOM fouling scales differently than OMP adsorption for different‐sized adsorbent particles. To overcome this limitation, single and multiple linear regression analyses were used to develop relationships between DOM concentration, OMP physical–chemical properties (log D, Abraham solvation parameters), and early (10%) OMP breakthrough at full‐scale with and without RSSCT data. It was found that statistically significant regressions could be developed using the largest database assembled to date consisting of 175 RSSCT and full‐/pilot‐scale data pairs, including activated carbons generated from bituminous coal, lignite, coconut shells, and wood‐based biochar, 58 OMPs with a range of adsorbabilities, and waters containing DOM from diverse origins.