Institut für Funktionelle Grenzflächen (IFG)

Low biosorption of PVA coated engineered magnetic nanoparticles in granular sludge assessed by magnetic susceptibility

  • Autor:

    Herrling, M., P. / Fetsch, K., L. / Delay, M. / Blauert, F. / Wagner, M. / Franzreb, M. / Horn, H. / Lackner, S. (2015)

  • Quelle:

    Science of Total Environment (2015), 537, 43-50

  • Datum: August 2015


When engineered nanoparticles (ENP) enter into wastewater treatment plants (WWTP) their removal from the
water phase is driven by the interactions with the biomass in the biological treatment step. While studies focus
on the interactions with activated flocculent sludge, investigations on the detailed distribution of ENP in other
types of biomass, such as granulated sludge, are needed to assess their potential environmental pollution.
This study employedengineeredmagneticnanoparticles (EMNP)coated with polyvinyl alcohol(PVA) asmodelnano-
particles to trace their fate in granular sludge from WWT. For the first time, magnetic susceptibility was used as a
simple approach for the in-situ quantification of EMNP with a high precision (error <2%). Compared to other an-
alytical methods, the magnetic susceptibility requires no sample preparation and enabled direct quantification of
EMNP in both the aqueous phase and the granular sludge.
In batch experiments granular sludge was exposed to EMNP suspensions for 18 h. The results revealed that the removal of EMNP from the water phase (5–35%) and biosorption in the granular sludge were rather low. Less than 2.4% of the initially added EMNP were associated with the biomass. Loosely bounded to the granular sludge, desorption of EMNP occurred. Consequently, the removal of EMNP was mainly driven by physical co-sedimentation with the biomass instead of sorption processes.
A mass balance elucidated that the majority of EMNP were stabilized by particulate organic matter in the water
phase and can therefore likely be transported further. The magnetic susceptibility enabled tracing EMNP in com-
plex matrices and thus improves the understanding of the general distribution of ENP in technical as well as en-
vironmental systems.