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Environ Sci Technol. 2009 Aug 1;43(15):5576-85.

Estimating the in situ sediment-porewater distribution of PAHs and chlorinated aromatic hydrocarbons in anthropogenic impacted sediments.

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  • 1Department of Environmental Engineering, Norwegian Geotechnical Institute (NGI), P.O. Box 3930 UllevĂ„l Stadion, N-0806 Oslo, Norway hpa@ngi.no

Abstract

It has become increasingly apparent that the in situ sediment-porewater distribution behavior of organic compounds within anthropogenic impacted sediments is quite diverse, and challenging to generalize. Traditional models based on octanol-water partitioning generally overestimate native porewater concentrations, and modern approaches accounting for multiple carbon fractions, including black carbon, appear sediment specific. To assess the diversity of this sorption behavior, we collected all peer-reviewed total organic carbon (TOC)-normalized in situ sediment-porewater distribution coefficients, K(TOC), for impacted sediments. This entailed several hundreds of data for PAHs, PCBs, PCDD/Fs, and chlorinated benzenes, covering a large variety of sediments, locations, and experimental methods. Compound-specific K(TOC) could range up to over 3 orders of magnitude. Output from various predictive models for individual carbonaceous phases found in impacted sediments, based on peer-reviewed polyparameter linear free energy relationships (PP-LFERs), Raoult's Law, and the SPARC online-calculator, were tested to see if any of the models could consistently predict literature K(TOC) values within a factor of 30 (i.e., approximately 1.5 orders of magnitude, or half the range of K(TOC) values). The Raoults Law model and coal tar PP-LFER achieved the sought-after accuracy for all tested compound classes, and are recommended for general, regional-scale modeling purposes. As impacted sediment-porewater distribution models are unlikely to get more accurate than this, this review underpins that the only way to accurately obtain accurate porewater concentrations is to measure them directly, and not infer them from sediment concentrations.

PMID:
19731647
[PubMed - indexed for MEDLINE]
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