Turbulent mixing accelerates PAH desorption due to fragmentation of sediment particle aggregates View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-09-22

AUTHORS

Magdalena I. Rakowska, Martijn P. J. Smit, Darya Kupryianchyk, Jinyi Qin, Albert A. Koelmans, Huub H. M. Rijnaarts, Tim Grotenhuis

ABSTRACT

PurposeStripping contaminants from sediments with granular activated carbon (GAC) is a promising remediation technique in which the effectiveness depends on the rate of contaminant extraction from the sediment by the GAC. The purpose of the present study was to investigate the effect of mixing intensity on the short-term extraction rate of polycyclic aromatic hydrocarbons (PAHs) from contaminated sediment.Materials and methodsPAH desorption from sediment at a wide range of rotational speeds (min−1; rotations per minute (rpm)) was monitored by uptake in Tenax polymeric resins using a completely mixed batch reactor. Desorption data were interpreted using a radial diffusion model. Desorption parameters obtained with the radial diffusion model were correlated with particle size measurements and interpreted mechanistically.Results and discussionFast desorption rate constants, De/r2, with De the effective diffusion coefficient and r the particle radius, ranged from 3.7 × 10−3 to 1.1 × 10−1 day−1 (PHE) and 6 × 10−6 to 1.9 × 10−4 day−1 (CHR), respectively, and increased with the intensity of mixing. The De/r2 values would correspond to De ranges of 1.8 × 10−14–1.2 × 10−16 m2 × day−1 and 1.8 × 10−12–3.7 × 10−15 m2 × day−1, assuming fast desorption from the measured smallest particle size (9 μm) classes at 200 and 600 rpm, respectively.ConclusionsDesorption of PAHs was significantly accelerated by a reduction of particle aggregate size caused by shear forces that were induced by mixing. The effective intra-particle diffusion coefficients, De, were larger at higher mixing rates. More... »

PAGES

277-285

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11368-016-1556-5

DOI

http://dx.doi.org/10.1007/s11368-016-1556-5

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https://app.dimensions.ai/details/publication/pub.1052299561


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