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Environ Sci Technol

Title:		New Linear Partitioning Models Based on Experimental Water: Supercritical CO2 Partitioning Data of Selected Organic Compounds
Author(s):		Burant A; Thompson C; Lowry GV; Karamalidis AK;
Address:		"Department of Civil and Environmental Engineering, Carnegie Mellon University , Pittsburgh, Pennsylvania 15213, United States. Pacific Northwest National Laboratory , Richland, Washington 99352, United States"
Journal Title:		Environ Sci Technol
Year:		2016
Volume:		20160429
Issue:		10
Page Number:		5135 - 5142
DOI:		10.1021/acs.est.6b00301
ISSN/ISBN:		1520-5851 (Electronic) 0013-936X (Linking)
Abstract:		"Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 degrees C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set"
Keywords:		Linear Models Organic Chemicals/*chemistry Solubility Temperature Water/*chemistry;
Notes:		"MedlineBurant, Aniela Thompson, Christopher Lowry, Gregory V Karamalidis, Athanasios K eng Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. 2016/04/16 Environ Sci Technol. 2016 May 17; 50(10):5135-42. doi: 10.1021/acs.est.6b00301. Epub 2016 Apr 29"