Impacts of Metal Oxide Nanoparticles on Marine Phytoplankton

May 14, 2010 - Information on the toxicity of environmentally relevant concentrations of nanoparticles in marine ecosystems is needed for informed reg...
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Environ. Sci. Technol. 2010, 44, 7329–7334

Impacts of Metal Oxide Nanoparticles on Marine Phytoplankton ROBERT J. MILLER,* HUNTER S. LENIHAN, ERIK B. MULLER, NANCY TSENG, SHANNON K. HANNA, AND ARTURO A. KELLER Bren School of Environmental Science & Management and Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106

Received January 22, 2010. Revised manuscript received May 4, 2010. Accepted May 6, 2010.

Information on the toxicity of environmentally relevant concentrations of nanoparticles in marine ecosystems is needed for informed regulation of these emerging materials. We tested the effects of two types of metal oxide nanoparticles, TiO2 and ZnO, on population growth rates of four species of marine phytoplankton representing three major coastal groups (diatoms, chlorophytes, and prymnesiophytes). These metal oxide nanoparticles (NPs) are becoming common components in many industrial, household, and cosmetic products that are released into coastal ecosystems. Titania NPs showed no measurable effect on growth rates of any species, while ZnO NPs significantly depressed growth rate of all four species. ZnO NPs aggregated rapidly in seawater, forming particles >400 nm hydrodynamic diameter within 30 min, and dissolved quickly, reaching equilibrium concentrations within 12 h. Toxicity of ZnO NPs to phytoplankton was likely due to dissolution, release, and uptake of free zinc ions, but specific nanoparticulate effects may be difficult to disentangle from effects due to free zinc ions. A modeling approach based on a Dynamic Energy Budget (DEB) framework was used to estimate sublethal effects of the two NPs on phytoplankton populations. Concentrations that were estimated to have no effect on population growth (NEC) were (one standard error in parentheses) 428 (58) µg L-1 ZnO for the diatom Skeletonema marinoi and 223 (56) µg L-1 for Thalassiosira pseudonana. NEC could not be estimated for the other taxa but were within the range of 500-1000 µg L-1. Our results suggest that effects of metal oxide NPs on marine organisms is likely to vary with particle type and organism taxonomy.

1. Introduction Marine phytoplankton are highly productive in coastal ecosystems (1), where they comprise the base of food webs. Phytoplankton are also often the first biological taxa that are exposed to and uptake contaminants associated with anthropogenic pollution discharges. As small (0.2-200 µm) single or chain-forming cells suspended in water, phytoplankton have very high surface-to-volume ratios, may respond quickly to suspended toxicants with high uptake †

Part of the special section “William Glaze Tribute”. * Corresponding author e-mail: [email protected], rjmiller@ bren.ucsb.edu. 10.1021/es100247x

 2010 American Chemical Society

Published on Web 05/14/2010

rates, and have often been used as pollution indicator species (2). Information on the impact of emerging contaminants on phytoplankton, therefore, is necessary to predict their potential impacts on coastal marine food webs and the ecosystems that they support. Nanomaterials are an important emerging class of contaminants, with potentially wide-ranging ecological impacts (3) within marine and estuarine ecosystems, the expected destination of most industrially discharged nanomaterials (4). Utilization of nanomaterials in industry and consumer products is proliferating rapidly, and metal oxide nanoparticles (NPs) are now commonly used for a range of applications, including many with inherently high risk of water discharge such as cosmetics, coatings, and pigments (5). Titanium dioxide (TiO2) and zinc oxide (ZnO) (NPs) are among the most abundant nanomaterials discharged into the environment; they are relatively easy to produce (6), and their abundance is expected to continue increasing because of their utility and the continued development of new applications (5). Modeling efforts to estimate concentrations of nanomaterials in the environment indicate that among five of the most common nanomaterials, TiO2 and ZnO may reach high concentrations in surface waters and pose a significant threat to aquatic ecosystems (5). Two general toxicity mechanisms for metal oxide NPs have received the most attention in research, generation of reactive oxygen species (ROS) and release of metal ions (7). Both TiO2 and ZnO NPs generate ROS (8-11), which can damage organisms through a variety of interrelated effects, including lipid peroxidation and DNA damage (3, 9). The NPs differ in solubility characteristics: ZnO is variably soluble in aqueous media, and TiO2 is insoluble in water, requiring strong acids for dissolution (12). ZnO is known to be soluble in water, varying in solubility with particle size (13) and pH (14), but little is known about the dynamics of solubility. Free Zn2+ ions can be toxic to many aquatic organisms (15), including marine phytoplankton (16), and dissolved Zn ions have been implicated as a major mechanism driving toxicity of ZnO NPs in aqueous media (11, 17, 18). Metal oxide NPs aggregate in suspension to form larger particles, which may then settle out of the water column. Aggregation dynamics of NPs and end products depend on particle concentration, pH, ionic strength, ionic composition, natural organic matter (NOM) concentration and composition, and other characteristics of the aqueous media (19). Ultimately, however, organisms in aqueous media will likely seldom encounter NPs at their primary size (19). We examined the effects of low, environmentally relevant (µg L-1) concentrations of two metal oxide NPs, ZnO and TiO2, on population growth rates of four widespread species of phytoplankton representing three major groups, the diatoms (Phylum: Heterokontophyta, Class: Bacillariophyceae), green algae or chlorophytes (Phylum: Chlorophyta, Class: Chlorophyceae), and the prymnesiophytes (Phylum: Haptophyta, Class: Prymnesiophyceae). We measured the rate of dissolution of ZnO NPs in seawater to determine the likelihood that their effects are due to release of free Zn ions. Experiments were conducted using natural seawater media, conditions under which ZnO and TiO2 NPs aggregate rapidly, forming micrometer-sized particles within minutes (19). Low concentrations of aggregated nanoparticles are expected to result from discharges of metal oxide NPs into natural waters (4, 19). Hence, these were the conditions we emulated in our experiments. As a means of detecting sublethal effects at the population level, we use two analytical approaches, a standard hypothesis testing approach to test empirically for VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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differences in population growth rates, and a modeling framework based on Dynamic Energy Budget (DEB) theory. DEB theory describes the rates of energy acquisition and expenditure, such as growth, maintenance, and reproduction by individual organisms (24, 25). In a DEB framework, at sublethal concentrations, toxicants impact the fluxes of energy between the organism and its environment as well as within the organism by changing the values of primary physiological parameters in DEB theory [e.g., (26-28)]. Because these physiological parameters determine the specific population growth rate, toxic impacts on those parameters will lead to changes in the population growth rate. Our primary aim in employing DEB theory is to obtain estimates of No-Effect Concentrations (NEC), the highest concentration in the environment that will not cause detrimental effects.

2. Materials and Methods Nanoparticles. TiO2 was acquired from Evonik Degussa Corp. (U.S.A.), and ZnO from Meliorum Technologies (U.S.A.), and have been characterized physically and chemically by the University of California Center for Environmental Implications of Nanotechnology (UC CEIN) as standard reference materials for a wide variety of fate and transport and toxicological studies (19, 20). The TiO2 NPs were semispherical, 81% anatase, 19% rutile, and 15-20 nm in size; ZnO NPs were spheroid, 100% zincite, and 20-30 nm in size. While the primary size of both types of NPs was in the range from 15 to 30 nm, the NPs tend to quickly aggregate (19). To produce 10 g L-1 stock dispersions, 10 mg of NPs were added to 1 mL of filtered (0.2 µm Millipore) natural seawater, sonicated for 60 s, vortexed briefly, and diluted to 10 mg L-1 with filtered natural seawater. Phytoplankton. Four species of phytoplankton were used, Thalassiosira pseudonana, and Skeletonema marinoi (centric diatoms, Bacillariophyceae: Centrales); Dunaliella tertiolecta (Chlorophyceae: Chlamydomonadales); and Isochrysis galbana (Prymnesiophyceae: Isochrysidales). Axenic cultures were obtained from the Provasoli-Guillard National Center for Culture of Marine Phytoplankton (Bigelow Laboratory for Ocean Sciences, West Boothbay Harbor, Maine, U.S.A.), and were maintained in standard media (f/2 or L1, 21, 22) made with filtered (0.22 µm) natural seawater, which was autoclaved prior to inoculation. To provide inoculant for experiments, algae were incubated under cool white fluorescent lights (14:10 light:dark, 100-120 µmol m-2 s-1) at 15 °C with aeration for 5-7 days, until log phase growth prevailed. Cell densities were measured by hemacytometer (Reichert, Buffalo NY). Phytoplankton Exposure Experiments. All experiments were conducted at 15 °C, 34 ppt salinity, under the same illumination system and schedule described above. All glassware was acid-washed, rinsed with purified water (Barnstead nanopure, resistivity >18 MΩ cm), and autoclaved before use. L1 media (21) was used, with only major nutrients added and no trace metals, to avoid adding EDTA that would complex free metal ions. Cells to inoculate the experiments were first filtered (0.22 µm) and rinsed three times with filtered autoclaved seawater to remove EDTA, and resuspended in experimental media. Experiments were run in 500 mL Erlenmeyer flasks, media volume 200 mL, and were mixed at ∼150 rotations per minute on a rotary shaker (New Brunswick Scientific Co., NJ, U.S.A.). NP concentrations tested were 0, 10, 100, 500, and 1000 µg L-1 (ppb), with three replicates per treatment. Flasks were inoculated with 1-2 × 105 cells mL-1, and cell densities were monitored every 24 h for 96 h by hemacytometer. Data Analysis. Phytoplankton population growth rates for each replicate flask were estimated as the slope of logtransformed cell count data, obtained through least-squares 7330

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regression (23). One-way ANOVA was used to test for an overall effect of NP toxicity on growth rates. Homogeneity of variances was tested with Levene’s test, and when heterogeneous, or when data were not normally distributed, data were transformed. When ANOVA revealed significant differences among treatments, posthoc tests were conducted with Dunnett’s method (EPA 1989), which tests for pairwise differences between each treatment and the control. DEB Model Description. The dynamics of the population density, X, in a constant environment with abundant resources can be described by dX ) rc,0X or X ) X0erc,0t dt

(1)

with rc,0 as the net population growth rate in the absence of toxicants. Assuming that background mortality is negligible, according to Dynamic Energy Budget (DEB) theory, the net population growth rate of dividing unicellular organisms in an environment with abundant resources is (29): rc,0 )

kE - gkM 1+g

(2)

where g is the energy investment ratio, kE is the specific energy conductance, and kM is the maintenance rate coefficient. It is likely that several physiological parameters are affected by a toxicant, unless the molecular mode of action of that toxicant is highly specific (such is not the case with metal toxicity). However, toxic impacts on most parameter values, including the ones in eq 1, can be scaled such that resulting effects on the population growth rate are very similar (28). Therefore, for practical purposes, the most generic DEB toxic effect model can be used to describe the impacts on the population growth rate. This effect model considers effects on the parameters determining the costs to remain viable per unit of time (i.e., maintenance) and the rate of energy acquisition (i.e., energy conductance). Assuming that toxicants are relatively rapidly exchanged between algae and their environment, the population growth rate to be substituted in eq 1 becomes (28): rc )

(

)

kE (C - CNEC)+ - gkM 1 + (C - CNEC)+ CK (1 + g) 1 + CK (3)

(

)

where C, CNEC, and CK represent the toxicant concentration in the environment, the ambient NEC, and the toxicant stress concentration (i.e., the parameter scaling the impact of a toxicant). Dissolution and Particle Aggregation. To determine the rate of dissolution of ZnO NPs in seawater, and to assess whether toxic effects were related to NPs or free Zn, the NPs were dispersed in seawater using a sonicating bath for 30 min and incubated for increasing time periods in Amicon Ultra-15 Ultracel 3 centrifuge tubes (3 kDa cutoff, Millipore, U.S.A.)., after which the tubes were centrifuged for 30 min in a Sorvall RC5B Plus centrifuge (Thermo Scientific, U.S.A.) with a swinging bucket rotor at 4,000 × g. The filtrate was then analyzed via ICP-AES (iCAP 6300, Thermo Scientific, Waltham, MA). Recovery of Zn2+ from the Amicon tubes was measured and varied from 95.1 to 97.9% depending on concentration; measured concentrations were corrected for loss. Effective diameter at different stages of the aggregation process were determined via Dynamic Light Scattering (DLS) using a BI-200SM (Brookhaven Instruments, Holtsville, NY). The dynamic sedimentation process was measured using a UV-vis spectrophotometer (BioSpec 1601, Shimadzu, MD)

FIGURE 1. Effect of ZnO nanoparticle (NP) concentration on growth rate of four species of marine phytoplankton. Horizontal lines are over means that are not statistically different, and asterisks identify means that are significantly less than controls (Dunnett’s method, P e 0.01).

FIGURE 2. Growth curves for S. marinoi (A) and T. pseudonana (B) exposed to ZnO at 0 µg L-1 (open circles), 10 µg L-1 (closed circles), 100 µg L-1 (open squares), 500 µg L-1 (closed squares), and 1000 µg L-1 (diamonds). The curves represent fits to data with eq 1 and 3 (see text). The fits obtained with 0, 10, and 100 µg L-1 (i.e, ppb) ZnO are identical, as the no-effect concentration exceeds 100 µg L-1; the two curves below represent the fits obtained with 500 and 1000 µg L-1 ZnO. via time-resolved optical absorbency (ZnO at 378 nm). Optical absorbency was measured every 6 min over a period of 360 min. The experiments were run in duplicate or triplicate, and the results presented are the mean value of each run.

3. Results Effect of ZnO and TiO2 Nanoparticles on Phytoplankton Growth. TiO2 NPs had no effect on growth of any of the four phytoplankton species tested (ANOVA, F4,10 < 0.50, P g 0.05). In contrast, growth of all four phytoplankton species was significantly reduced when exposed to ZnO NPs (ANOVA, F4,10 > 5.50, P e 0.01; Figure 1). The diatoms were most negatively affected, for at the highest concentration of ZnO NPs (1000 µg L-1) growth rate of S. marinoi was reduced by a factor of 2, and growth rate of T. pseudonana was reduced by a factor of 3 compared with the control. T. pseudonana showed significantly lower growth rate at 500 µg L-1 ZnO

than in the control (Dunnett’s test, P < 0.0001). S. marinoi showed significantly reduced growth rate at 1000 µg L-1 ZnO (Dunnett’s test, P ) 0.01). Growth rate of D. tertiolecta (Dunnett’s test, P ) 0.01) and I. galbana (Dunnett’s test, P < 0.0001) were significantly reduced compared with the control at 1 mg L-1 ZnO. DEB Estimates. We could estimate DEB toxicity parameters for S. marinoi and T. pseudonana with ZnO (Figure 2) but not for D. tertiolecta and I. galbana, as their growth curve at the highest ZnO concentration was too similar to that of the control. For the same reason, the toxicity data with TiO2 exposure could not be successfully analyzed with the DEB model. Parameters for S. marinoi estimated from the data obtained with 0 and 10 µg L-1 ZnO were (SE in parentheses) X0 ) 0.52 (0.04) × 109 cells L-1 and rc,0 ) 1.23 (0.04) day-1. Parameters for S. marinoi estimated from all data were CNEC) 428 (58) µg L-1 ZnO, CK ) 1099 (206) µg L-1 ZnO, and VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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concentration of remaining dispersed NPs is much lower, thus behaving similarly to lower initial concentration dispersions (e.g., 10 mg L-1). Given the lower surface to volume ratio of larger aggregates, rapid aggregation may partly explain the slower dissolution rates at higher initial ZnO concentrations.

4. Discussion

FIGURE 3. (A) Dissolution of ZnO NPs in seawater at four different initial NP concentrations (0.1, 0.5, 1, and 10 mg L-1). (B) Aggregation of ZnO NPs in seawater at an initial 10 mg L-1 of ZnO. (C) Sedimentation rate of ZnO NPs in seawater at three initial ZnO concentrations (10, 50, and 100 mg L-1). gkM/(1 + g) ) 0.2 day-1. Parameters for T. pseudonana estimated from the data obtained with 0 and 10 µg L-1 ZnO were X0 ) 0.76 (0.05) × 109 cells L-1 and rc,0 ) 0.60 (0.03) day-1. Parameters for T. pseudonana estimated from all data were CNEC ) 223 (56) µg L-1 ZnO, CK ) 857 (127) µg L-1 ZnO, and gkM/(1 + g) ) 0.2 day-1. Dissolution and Aggregation of ZnO Nanoparticles in Seawater. ZnO NPs dissolve rapidly in seawater, particularly at low NP concentrations (Figure 3A). Within 12 h an equilibrium concentration is reached. At the lower initial concentrations (0.1-1.0 mg L-1 ZnO), around 70% of the initial NP mass is dissolved within the volume in the centrifuge tubes (10 mL). At an initial concentration of 10 mg L-1 ZnO, 32% of the mass was dissolved at 12 h, but was still dissolving after 96 h. At the same time, the NPs were rapidly aggregating (Figure 3B). Aggregation of the particles begins from the moment they are placed in an aqueous medium, such that the initial effective diameter after the ZnO NPs have been dispersed is 250-300 nm. At 10 mg L-1 ZnO, the particles aggregate within 30 min to ∼450 nm. This results in relatively rapid sedimentation (Figure 3C), particularly at higher initial concentration of particles. The behavior at 100 mg L-1 ZnO suggests two regimes, one of very rapid aggregation (diffusion limited), followed by a slower aggregation regime in which the 7332

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Toxicity of TiO2 Nanoparticles. Despite their potential for ROS generation, we found no evidence that TiO2 NPs inhibited population growth of marine phytoplankton. Similar results have been shown for bacteria and aquatic crustaceans (30), with TiO2 showing lack of toxicity even at g L-1 concentrations. Toxic effects of TiO2 NPs were shown for some freshwater algae, but toxic concentrations were in the mg L-1 range (18, 31), greatly exceeding environmentally relevant concentrations (5). Similar results have been found for aquatic invertebrates (32). UV-excited TiO2 has long been known to generate cytotoxic ROS (33), but experimental UV levels, typically generated with UV lamps, are often much higher than natural levels. UV levels in our experiments were relatively low (50-70 mW m-2 in air; measured with General Instruments UV radiometer, model UV513AB). Further work probing the possibility of toxic effects of TiO2 NPs on phytoplankton at higher UV levels is warranted. ZnO Nanoparticles: Toxicity and Dissolution. ZnO NPs at relatively low concentrations (1-1000 µg L-1) inhibited population growth rate of all four species of marine phytoplankton that we tested. These results have important implications for marine ecosystems, because even a small decrease in growth rate can profoundly affect ecosystem productivity, since phytoplankton growth rate and productivity are tightly coupled (34). In freshwater algae, toxicity of ZnO NPs has been linked to dissolution and release of free zinc ions (17, 18). Despite the fact that phytoplankton, like most organisms, need Zn2+ for functioning enzymes and other proteins (35), ionic Zn2+ can be toxic to marine phytoplankton at relatively low concentrations (36, 37), with growth inhibition of diatoms occurring at concentrations as low as 10-11 M (0.65 ng L-1 (38),). LC50 concentrations of zinc that have been estimated for phytoplankton encompass a wide range, ∼20-10,000 µg L-1 (39), but these estimates are difficult to interpret because (1) most studies do not hold trace metal concentrations constant for the duration of experiments, and (2) toxicity of Zn and other trace metals to phytoplankton depends on the concentration of other limiting trace metals and nutrients (40). The mechanism of Zn2+ toxicity in phytoplankton can be antagonism between toxic metal, in this case zinc, and nutrient metal. That is, excessive free Zn2+ ion competitively inhibits manganese uptake, causing Mn deficiency (36). Zn2+ has also been shown to cause increased ATP production in diatoms, which may be linked to increased thiol and glutathione production (41). Zn-thiol binding may act as a detoxification mechanism, but the energy required may decrease cell division rate and therefore population growth rate (41). Dissolution of ZnO NPs in seawater was relatively rapid, with equilibrium concentrations reached within 12 h for [ZnO] < 1 mg L-1.Nevertheless, total dissolution was not observed over the 96-h time scale of our experiments, indicating that ZnO NPs probably acted as a continuous source of toxic concentrations of Zn2+ ions to phytoplankton in the experiment, particularly since algal uptake lowers the concentration below equilibrium, inducing further dissolution. The dynamic nature of trace metal uptake by phytoplankton and other organisms implies that toxic concentrations of free metal ions cannot be reliably measured without a buffering system to hold concentrations steady throughout the experimental period, as uptake and other processes lower the metal ionic concentration experienced by the organisms during the experiment (40). Some workers

have argued that toxic effects of free metal ions are separate from nanoparticle-specific effects (17). However, both mechanisms may interact with the behavior of particles. For example, adhesion of particles to cell surfaces may facilitate damage because of both ROS production and dissolution of free metal ions (4). Furthermore, without knowledge of free metal ion activity throughout experiments, and complementary experiments evaluating constant ionic concentrations (obtained using chelators), the release of free metal ions as the toxicity mechanism of metal oxide NPs cannot be unambiguously assessed. For this reason and others (38), simple inclusion of ionic metal treatments in experiments cannot be used to separate so-called NP effects from dissolved ion toxicity. Our estimations of NEC parameters, should therefore, be taken conservatively: if toxicity is solely due to dissolution of free zinc ions, then NECs are likely considerably lower than we measured, and identical to those for dissolved Zn, since the time scale for dissolution, even if the particles are discharged directly into the coastal ocean, will be short. DEB Modeling. The central idea in modeling sublethal toxicant effects in a DEB framework is that toxicants impact the fluxes of energy between the organism and its environment, as well as within the organism, by changing the values of primary parameters in DEB theory (26-28). In principle, the primary parameter(s) targeted by a toxicant can be inferred from experimental data. In practice, however, such an inference requires measurements quantifying toxicant impact on several processes, for example, growth, photosynthesis, and respiration. Data on population growth, such as in this study, do not contain sufficient information to identify the primary parameter(s) being affected by a toxicant (26-28). Those data are typically fitted equally well by DEB models with different assumptions regarding the target primary parameter(s) of a toxicant. In particular, estimates of NECs, our primary reason for using DEB theory in this study, are relatively insensitive to the choice of target primary parameter(s). NECs were estimated for the two diatom species (S. marinoi: 428 (58) µg L-1; T. pseudonana: 223 (56) µg L-1), but could not be estimated for D. tertiolecta or I. galbana because of data limitations. However, the NEC for these species is probably within the range of 500 and 1000 µg L-1 (Figure 1). DEB theory offers substantial advantages over purely descriptive methods, such as those applied to estimate values for the NEC and LC50, since the interpretation of toxicity assessments from process-based models are independent of experimental protocol. Instead, toxicity parameters estimated with the DEB framework are independent of exposure time and choice of toxicant test concentrations and, therefore, less ambiguous and more accurate than the classic measures mentioned above (42-44). Moreover, DEB theory provides a framework within which different end points (e.g., reproduction and growth) can be compared, since those end points are based on shared processes in DEB theory (44). In other words, the two toxicity parameters, the NEC and the toxicant scaling parameter, determine the impact of a toxicant on any end point, implying that parameter values estimated with one end point may be used to calculate the impact of that toxicant on another end point. This means that the NECs estimated in this study are not confined to population growth rates, but apply to other end points as well. We recommend including the estimation of DEB toxicity parameters in protocols for the testing of toxic substances.

Acknowledgments This work was supported in part by the National Science Foundation and the U.S. Environmental Protection Agency under Cooperative Agreement # NSF-EF0830117, and by National Science Foundation Grant EF-0742521. Any opinions, findings, and conclusions or recommendations ex-

pressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the U.S. Environmental Protection Agency. The authors also thank Dongxu Zhou, Hongtao Wang, and Maia Colyar for their help with the dissolution, aggregation, and sedimentation experiments, and Scott Pease and Alex Moreland for help with phytoplankton toxicity experiments.

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