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

Tarun Anumol, Massimiliano Sgroi, Minkyu Park, Paolo Roccaro, Shane A Snyder

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

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 Dow>1. Excellent linear relationship (R2>0.9) between log Dow 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 Dow. 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.

Original languageEnglish (US)
Pages (from-to)76-87
Number of pages12
JournalWater Research
Volume76
DOIs
StatePublished - Jun 1 2015

Fingerprint

absorbance
Organic compounds
Activated carbon
activated carbon
organic compound
fluorescence
Fluorescence
Organic carbon
dissolved organic carbon
Water quality
water quality
prediction
Coulomb interactions
removal
savings
Effluents
Wastewater
regeneration
replacement
effluent

Keywords

  • Adsorption
  • Granular activated carbon
  • PFC
  • Pharmaceuticals
  • Real time monitoring
  • Trace organic compounds

ASJC Scopus subject areas

  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution
  • Ecological Modeling

Cite this

Predicting trace organic compound breakthrough in granular activated carbon using fluorescence and UV absorbance as surrogates. / Anumol, Tarun; Sgroi, Massimiliano; Park, Minkyu; Roccaro, Paolo; Snyder, Shane A.

In: Water Research, Vol. 76, 01.06.2015, p. 76-87.

Research output: Contribution to journalArticle

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abstract = "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 Dow>1. Excellent linear relationship (R2>0.9) between log Dow 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 Dow. 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.",
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