New Linear Partitioning Models Based on Experimental Water: Supercritical CO2 Partitioning Data of Selected Organic Compounds

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• 作者：Aniela Burant ; Christopher Thompson ; Gregory V. Lowry ; Athanasios K. Karamalidis
• 刊名：Environmental Science & Technology
• 出版年：2016
• 出版时间：May 17, 2016
• 年：2016
• 卷：50
• 期：10
• 页码：5135-5142
• 全文大小：396K
• 年卷期：0
• ISSN：1520-5851

文摘

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 °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.