Elsevier

Water Research

Volume 48, 1 January 2014, Pages 269-279
Water Research

Heteroaggregation and sedimentation rates for nanomaterials in natural waters

https://doi.org/10.1016/j.watres.2013.09.036Get rights and content

Highlights

  • A novel method to estimate heteroaggregation rates from sedimentation is presented.

  • Sedimentation of nanoparticles was studied in presence of natural colloids.

  • Water type and nanoparticle characteristics affect these removal rates.

  • Parameters obtained can be used in applied water quality models for nanoparticles.

Abstract

Exposure modeling of engineered nanomaterials requires input parameters such as sedimentation rates and heteroaggregation rates. Here, we estimate these rates using quiescent settling experiments under environmentally relevant conditions. We investigated 4 different nanomaterials (C60, CeO2, SiO2–Ag and PVP–Ag) in 6 different water types ranging from a small stream to seawater. In the presence of natural colloids, sedimentation rates ranged from 0.0001 m d−1 for SiO2–Ag to 0.14 m d−1 for C60. The apparent rates of heteroaggregation between nanomaterials and natural colloids were estimated using a novel method that separates heteroaggregation from homoaggregation using a simplified Smoluchowski-based aggregation-settling equation applied to data from unfiltered and filtered waters. The heteroaggregation rates ranged between 0.007 and 0.6 L mg−1 day−1, with the highest values observed in seawater. We argue that such system specific parameters are key to the development of dedicated water quality models for ENMs.

Introduction

The production and use of engineered nanomaterials (ENMs) are growing, which increases their emission to environmental compartments (Nowack and Bucheli, 2007). Consequently, understanding the safety, environmental and human health implications of nanotechnology-based products is of worldwide importance (Klaine et al., 2012, Wiesner et al., 2006). Although the benefits of ENMs have shown to be manifold, the implication of large quantities of ENMs entering the environment has yet to be understood (Batley et al., 2013, Hendren et al., 2011). There is a growing need for risk assessment of different nanomaterials in order to support their safe production and use (Morris et al., 2011). The environmental risk assessment is based on the determination of adverse effects on organisms and on evaluation of the environmental concentrations to which biota are exposed (EU, 2008, Quik et al., 2011). Recently, modeling approaches for estimating the environmental exposure concentration of nanomaterials have been suggested (Arvidsson et al., 2011, Gottschalk et al., 2010, Praetorius et al., 2012, Quik et al., 2011). These studies acknowledge the lack of input parameters valid for environmentally relevant conditions, such as sedimentation rates in natural waters (Gottschalk et al., 2010, Quik et al., 2011) and heteroaggregation rates for collisions between natural colloids (NCs) and ENMs (Arvidsson et al., 2011, Praetorius et al., 2012). Since there is no validated framework for calculation of these parameters for ENMs, they need to be estimated experimentally (Hotze et al., 2010, Lin et al., 2010, Petersen et al., 2011, Westerhoff and Nowack, 2013).

The aggregation rate constants for heteroaggregation (khet) can be split up in the product of collision frequency (K) and the attachment efficiency (α), i.e. khet = K × α (Friedlander, 2000; Petosa et al., 2010). For homoaggregation, several studies use this approach to derive the attachment efficiency αhomo as an important parameter driving homoaggregation kinetics for a certain ENM under a range of test conditions, such as ionic strength or DOC concentration (Chen and Elimelech, 2008; Keller et al., 2010). Consequently, such attachment efficiencies are conditional and represent the average behavior of particles present. The uncertain and conditional nature of K and α may be even bigger for heteroaggregation because natural colloids can be assumed to be much more heterogeneous and fundamentally indeterminate. Current methods to estimate attachment efficiencies α from observed aggregation rates rely on the collision frequency K being constant or known among a range of test conditions. However, due to the range of water and NC characteristics present in natural systems, the collision frequency K will not be constant. Furthermore, current theory of colloid behavior is not likely to be sufficient to estimate the collision frequency for natural systems. After all, this theory is based on ideal systems with spherical particles. In practice, fate models or water quality models for nanoparticles do not require separate attachment efficiencies α, nor separate collision frequencies K. They require the aforementioned product khet = K × α (Arvidsson et al., 2011, Praetorius et al., 2012). The heteroaggregation rate constant khet is the primary parameter used in current exposure modeling approaches which take heteroaggregation into account (Praetorius et al., 2012). We argue that conditional values of khet are highly needed for the further development of fate models for ENMs.

In the present study we provide estimates of sedimentation rates and heteroaggregation rate constants, based on sedimentation data for 4 different ENMs in the presence and absence of NCs in 6 different natural water types. Heteroaggregation rates are usually measured by directly measuring the increase in particle size in time (Afrooz et al., 2013, Huynh et al., 2012). For natural waters, direct measurement of aggregation rates is problematic due to the limitations of measurement techniques for such complex systems. We therefore propose a novel method to estimate these heteroaggregation rates from sedimentation data. We used fullerene (C60) as a carbon based ENM, Cerium dioxide (CeO2) ENM as a metal oxide and Silver (Ag) ENM with two different coatings, polyvinylpyrrolidone (PVP) and silicon dioxide (SiO2). Quiescent settling was measured in water from six different water bodies ranging from a small pond and stream to lake and seawater. These water samples cover a range in water quality characteristics such as salinity, acidity and organic matter content. Earlier work showed that NCs governed the sedimentation of ENMs in river water (Rhine and Meuse) (Quik et al., 2012). Here, this mechanism is studied for a much wider range of water types, including brackish tidal water and marine water. Sedimentation rates and heteroaggregation rates for ENMs and NCs are reported. To our knowledge, this is the first study that reports these parameters on the interaction of ENMs with NCs in surface waters.

Section snippets

Engineered nanomaterials

Polyvinylpyrrolidone coated silver (PVP–Ag) nanoparticles (hydrodynamic diameter (dh): 90.5 nm) and SiO2 coated silver (SiO2–Ag) nanoparticles (dh: 124 nm) were purchased from nanoComposix (San Diego, CA). Ceriumdioxide (CeO2) nanoparticles (dh: 175 nm) were kindly supplied by Umicore Ltd. (Brussels), as part of the EU NanoInteract project. CeO2 nanoparticles from the same batch have previously been used in several fate and effect studies (Quik et al., 2010, Quik et al., 2012, Van Hoecke

Natural colloids and water types

In general, NCs increased overall sedimentation of ENMs (mg L-1). The obtained ENM sedimentation rates (m d-1) were not significantly affected by the presence of NCs in the surface waters, nor by the different water types (paired t-test, p > 0.05, Fig. 1). For the non-settling fraction after 15 days (C15/C0), a significant decrease was observed in the presence of NCs (p < 0.01, Fig. 2) which implies that the settling fraction increased in presence of NCs. In combination, identical sedimentation

Conclusions

This study provided sedimentation rates, non-settling fractions and heteroaggregation rates for several representative ENMs and a wide range of natural water types. Heteroaggregation with NCs has been shown to play a key role in the sedimentation of ENMs. Furthermore, dissolution has been shown to be relevant for specific combinations of ENM and water types. We conclude that these data as well as the approach to derive them will advance the development of fate and exposure models for ENMs. This

Acknowledgments

We thank Ruud Jeths, Gerrie Pieper, Leo van Hal, Erik Steenbergen and Mieke Verheij for their assistance and cooperation regarding sampling of the different water types. This work was funded by the European Union Sixth Framework Program NanoInteract NMP4-CT-2006-033231, the RIVM strategic research program SOR-S340030, and by NanoNextNL, a micro and nanotechnology consortium of the Government of the Netherlands and 130 partners.

References (44)

  • T.J. Battin et al.

    Nanostructured TiO2: transport behavior and effects on aquatic microbial communities under environmental conditions

    Environ. Sci. Technol.

    (2009)
  • C.E. Boyd

    Bottom Soils, Sediment, and Pond Aquaculture

    (1995)
  • K.L. Chen et al.

    Interaction of fullerene (C60) nanoparticles with humic acid and alginate coated silica surfaces: measurements, mechanisms, and environmental implications

    Environ. Sci. Technol.

    (2008)
  • G. Cornelis et al.

    A method for determination of retention of silver and cerium oxide manufactured nanoparticles in soils

    Environ. Chem.

    (2010)
  • K.J. Farley et al.

    Role of coagulation in the kinetics of sedimentation

    Environ. Sci. Technol.

    (1986)
  • M. Filella
  • S.K. Friedlander

    Smoke, Dust, and Haze: Fundamentals of Aerosol Behavior

    (2000)
  • F. Gottschalk et al.

    Possibilities and limitations of modeling environmental exposure to engineered nanomaterials by probabilistic material flow analysis

    Environ. Toxicol. Chem.

    (2010)
  • C.O. Hendren et al.

    Estimating production data for five engineered nanomaterials as a basis for exposure assessment

    Environ. Sci. Technol.

    (2011)
  • C.M. Ho et al.

    Oxidative dissolution of silver nanoparticles by biologically relevant oxidants: a kinetic and mechanistic study

    Chem. – An Asian J.

    (2010)
  • E.M. Hotze et al.

    Nanoparticle aggregation: challenges to understanding transport and reactivity in the environment

    J. Environ. Qual.

    (2010)
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