Predicting trace organic compound breakthrough in granular activated carbon using fluorescence and UV absorbance as surrogates

This study investigated the applicability of bulk organic parameters like dissolved organic carbon (DOC), UV absorbance at 254nm (UV₂₅₄), and total fluorescence (TF) to act as surrogates in predicting trace organic compound (TOrC) removal by granular activated carbon in water reuse applications. Using rapid small-scale column testing, empirical linear correlations for thirteen TOrCs were determined with DOC, UV₂₅₄, and TF in four wastewater effluents. Linear correlations (R²>0.7) were obtained for eight TOrCs in each water quality in the UV₂₅₄ model, while ten TOrCs had R²>0.7 in the TF model. Conversely, DOC was shown to be a poor surrogate for TOrC breakthrough prediction. When the data from all four water qualities was combined, good linear correlations were still obtained with TF having higher R² than UV₂₅₄ especially for TOrCs with log D_ow>1. Excellent linear relationship (R²>0.9) between log D_ow and the removal of TOrC at 0% surrogate removal (y-intercept) were obtained for the five neutral TOrCs tested in this study. Positively charged TOrCs had enhanced removals due to electrostatic interactions with negatively charged GAC that caused them to deviate from removals that would be expected with their log D_ow. Application of the empirical linear correlation models to full-scale samples provided good results for six of seven TOrCs (except meprobamate) tested when comparing predicted TOrC removal by UV₂₅₄ and TF with actual removals for GAC in all the five samples tested. Surrogate predictions using UV₂₅₄ and TF provide valuable tools for rapid or on-line monitoring of GAC performance and can result in cost savings by extended GAC run times as compared to using DOC breakthrough to trigger regeneration or replacement.