Environmental Toxicology and Chemistry, Vol. 9999, No. 9999, pp. 1–16, 2015 # 2015 SETAC Printed in the USA
Hazard/Risk Assessment SPECIES SENSITIVITY WEIGHTED DISTRIBUTION FOR ECOLOGICAL RISK ASSESSMENT OF ENGINEERED NANOMATERIALS: THE n-TiO2 CASE STUDY ELENA SEMENZIN, ELISA LANZELLOTTO, DANAIL HRISTOZOV, ANDREA CRITTO, ALEX ZABEO, ELISA GIUBILATO, and ANTONIO MARCOMINI* Department of Environmental Sciences, Informatics, and Statistics, University Ca’ Foscari Venice, Venice, Italy (Submitted 20 January 2015; Returned for Revision 5 March 2015; Accepted 4 June 2015) Abstract: Societal concerns about the environmental risks of engineered nanomaterials (ENMs) have recently increased, but
nano-ecological risk assessments are constrained by significant gaps in basic information on long-term effects and exposures, for example. An approach to the ecological risk assessment of ENMs is proposed that can operate in the context of high uncertainty. This approach further develops species sensitivity weighted distribution (SSWD) by including 3 weighting criteria (species relevance, trophic level abundance, and nanotoxicity data quality) to address nano-specific needs (n-SSWD). The application of n-SSWD is illustrated for nanoscale titanium dioxide (n-TiO2), which is available in different crystal forms; it was selected because of its widespread use in consumer products (e.g., cosmetics) and the ample availability of data from ecotoxicological studies in the literature (including endpoints for algae, invertebrates, bacteria, and vertebrates in freshwater, saltwater, and terrestrial compartments). The n-SSWD application resulted in estimation of environmental quality criteria (hazard concentration affecting 5% and 50% of the species) and ecological risk (potentially affected fraction of species), which were then compared with similar results obtained by applying the traditional species sensitivity distribution (SSD) approach to the same dataset. The n-SSWDs were also built for specific trophic levels (e.g., primary producers) and taxonomic groups (e.g., algae), which helped to identify the most sensitive organisms. These results showd that n-SSWD is a valuable risk tool, although further testing is suggested. Environ Toxicol Chem 2015;9999:1–16. # 2015 SETAC Keywords: Species sensitivity distribution
Ecological risk assessment
Nanomaterials
Data quality
Titanium dioxide
some nonconventional tools have been recently proposed to address them [9,10]. Most of these tools address human health risk assessment, although there are only few specific ecological risk assessment tools, which mainly focus on environmental exposure assessment [1,11,12]. This is mainly because ecological risk assessment [13] presents a number of specific issues when applied to ENMs [1]. For example, the scarcity of characterization data for ENMs in environmentally relevant media and quantitative toxicity models makes it difficult to identify which physicochemical characteristics determine the effects documented in ecotoxicity studies. In addition to the difficulties in assessing the ecotoxicity of ENMs, there are various issues hampering the assessment of the environmental exposure including, but not limited to: 1) environmental behavior and fate of ENMs, which is largely unexplored; and 2) lack of adequate methods and tools for effective monitoring of ENMs in the different media and distinguishing them from the background particles [14]. Another issue is that most of the ecotoxicological studies reported in literature have used nonstandardized tests and test materials from different sources (responsible, in part, for the generation of nonreproducible/comparable results) [15]; the Organisation for Economic Co-operation and Development introduced standardized test guidelines for regulatory testing of chemicals to ensure global recognition of the methods used and is currently evaluating their applicability to nanomaterials (for more information, see Organisation for Economic Co-operation and Development [16]). Data quality is crucial for all risk assessment steps; in fact, data gathered based on standard testing guidelines often offer the most robust and transparent information [17]. Therefore, the need to ensure quality control (validity, reliability) and relevance of the ecotoxicological data on ENMs [17] before using them for an ecological risk assessment is highlighted.
INTRODUCTION
Nanotechnology is an emerging field in the area of science and technology involving the design, production, and use of structures at the nano-scale (i.e., from 1 nm to 100 nm) called nanomaterials [1,2]. Presently there is no internationally harmonized definition of engineered nanomaterials (ENMs) [3]; according to the European Commission’s recommendation, a nanomaterial is defined as “a natural, incidental or manufactured material containing particles in a unbound state or as aggregate or as an agglomerate and where, for 50% or more of the particles in the number size distribution, one or more external dimension is in the size range 1–100 nm” [4]. Because of their unique or enhanced physicochemical properties (e.g., tiny size, large surface area, surface reactivity, charge, shape), some ENMs are suitable for a wide variety of applications in many sectors (e.g., information technology, energy production, food, agriculture) [5]. In the last decade, there has been a significant increase in the development and widespread use of nano-products, and at the same time societal concerns regarding the potential ecological and human health risks from ENMs have increased [6]. The Organisation for Economic Co-operation and Development and its member countries concluded that “the approaches for the testing and assessment of traditional chemicals are in general appropriate for assessing the safety of nanomaterials, but may have to be adapted to the specificities of nanomaterials” [7]. Although the scientific community is not unanimous in recognizing some specificities of nanomaterials [8], All Supplemental Data may be found in the online version of this article. * Address correspondence to [email protected]. Published online 8 June 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/etc.3103 1
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Very recently, Gottschalk et al. [12] proposed the first quantitative ecological risk assessment approach for ENMs. The aim of their study was to quantify the environmental risk of ENMs by building species sensitivity distribution (SSD) curves that were then compared with probability distributions of predicted environmental concentrations (PECs) developed by Gottschalk et al. [18]. The approach was tested on 5 ENMs (n-Ag, n-TiO2, n-Zn, carbon nanotubes, and fullerenes), and the results demonstrated a marginal risk of these ENMs in surface water and some risk in sewage treatment plant effluent, although in the terrestrial compartment no risk was predicted except for a marginal value for n-TiO2 in sludge-treated soils [12]. In contrast to the traditional SSD approach [19], the technique of Gottschalk et al. [12] makes use of all available ecotoxicological data in building the cumulative distribution curves. Not reducing the dataset to the number of tested species is indeed very positive, particularly when data are scarce (as they are for most nanomaterials). However, we consider that the integration of such a dataset in a single SSD should take into account the relevance of the ecotoxicological data in representing the ecosystem of concern as well as the reliability of the data, a factor that is critical for all chemicals and that presents some peculiarities for nanomaterials [20]. For this reason we propose an ecological risk assessment approach, called nano-species sensitivity weighted distribution (n-SSWD), which is able to critically integrate the available information and update the risk estimation as soon as additional ecotoxicological data are available. In n-SSWD, the emphasis is on assessment of the quality of ecotoxicological data, according to the nanospecific method proposed by Card and Magnuson [20], to allow a more appropriate use of the available, mainly heterogeneous, ecotoxicological information in ecological risk assessment. In the present study the n-SSWD approach is compared with both the traditional SSD [19] and the probabilistic SSD proposed by Gottschalk and colleagues [12]; as a case study, we used nanoscale titanium dioxide (n-TiO2), which is available in different crystal forms as well as an amorphous form. (Additional background information on traditional SSD and SSWD approaches is included in the Supplemental Data.) MATERIALS AND METHODS
Case study: n-TiO2
Nanoscale titanium dioxide was selected as the case study because, as a result of its unique or enhanced physicochemical properties, it is suitable for a wide variety of applications in many sectors (e.g., cosmetics, household cleaning products, paints). For this reason the market for n-TiO2 has been exponentially growing, and in 2012 worldwide production of up to 10 000 t/yr was estimated [21]. At the same time, societal concerns regarding the potential environmental and health risks from this material have increased and need to be addressed through robust risk analysis [6]. Numerous toxicological and ecotoxicological tests have been carried out and published to better understand whether n-TiO2 may cause adverse effects on the environment and human health because of its enhanced reactivity. The studies conducted so far have shown that the toxicity of n-TiO2 is far higher than that of its bulk form in both aquatic and terrestrial environments and have suggested that n-TiO2 has size-dependent toxic effects [22,23]. Several studies showed the ability of n-TiO2 to generate reactive oxygen species (ROS) after exposure to a strong artificial source of ultraviolet (UV) radiation. These ROS are able to cause cytotoxicity in the test organism and for this reason oxidative
E. Semenzin et al.
stress mediated by photoactive n-TiO2 is the likely mechanism of its toxicity [24]. However, no studies have demonstrated so far that photoactivity is responsible for the environmental toxicity of n-TiO2 under natural levels of UV radiation [24], and also that the effectiveness was not demonstrated of n-TiO2 interaction with ultraviolet light strongly depends on particle size [25] as well as the crystal form of TiO2. Regarding the fate and behavior of ENMs and in particular of n-TiO2 in environmental compartments, Gottschalk et al. [17] and Sun et al. [26] published environmental concentrations of different ENMs, including n-TiO2, modeled for 3 different areas (the United States, Europe, and Switzerland) in different environmental compartments (aquatic and terrestrial) [17,26]. The results, although affected by uncertainties related to both input data (e.g., emission estimates) and modeling assumptions, showed that n-TiO2 can reach concentrations of 220 mg/L in a freshwater compartment (i.e., sewage treatment plant effluent) [26] and 89.2 mg/kg in a terrestrial compartment (i.e., sludge-treated soil) [18]. Accordingly, it can be concluded that there are still too many uncertainties about the toxicity, behavior, and fate of n-TiO2 in the environment, and that this uncertainty strongly influence ecological risk assessment. Nevertheless, as mentioned in the Introduction, a recent study has published a probabilistic ecological risk assessment based on SSD for 5 ENMs (n-Ag, n-TiO2, n-ZnO, carbon nanotubes, and fullerenes) and 4 environmental compartments (surface freshwater, sewage treatment plant effluents, soils, and sludge-treated soils) [12]. The study [12] collected 34 ecotoxicological endpoints in 23 papers for n-TiO2 in different crystalline forms (e.g., rutile or anatase). With this dataset, SSD curves were built by applying an innovative probabilistic approach and subsequently compared with PEC distributions for estimating the risk for both aquatic and terrestrial environments [12,18]. The results indicated that there is only a marginal risk from n-TiO2 (5% of affected species) for the freshwater compartment (higher than the marginal risk estimated by Gottschalk et al. [12]). In terms of the PEC value for STP effluent, the comparison with both SSD and n-SSWD allowed calculation of the potentially affected fraction of species. Specifically, at the considered PEC value the affected percentages were: 20% of species (according to the best estimate curve; 10–35% considering the 5th percentile and 95th percentile, respectively) by using SSD, and 30% of species (28– 32%) by using n-SSWD. Therefore, the conventional SSD seemed to be less conservative than the n-SSWD, showing, at the same time, a higher variability in the response value range (25% for SSD compared with 4% for n-SSWD). Overall, our risk estimations for surface water and STP effluent in the present study appeared to be similar to those of Gottschalk et al. [12]. However, because in the present study we used the higher PEC values proposed by Sun et al. [26] for Europe (400 times and 100 times higher for 0.85 upper percentile for surface water and STP effluent, respectively, compared with the PECs proposed by Gottschalk et al. [18] and used in Gottschalk et al. [12]), it is expected that use of the
complete PEC distributions would result in a significantly higher risk estimation (i.e., >5% for surface water and >40% for STP effluent). Trophic level and taxonomic group-specific n-SSWD curves. By applying the approach described in the Materials and Methods section, specific n-SSWD curves were built for each trophic level (primary producers, primary consumers, and secondary consumers) as well as taxonomic group (algae, bacteria, invertebrates, and vertebrates) represented in the ecotoxicological dataset for the freshwater compartment. The results are presented in Figure 3 and Figure 4 and discussed in the following paragraphs. The log-normal n-SSWD values obtained for different trophic levels (Figure 3A–C) have high and very similar R2 values (0.9 or higher), but none of them have a good KSpvalue (greater than 0.1). This means that the log-normal distribution is not adequate to represent the 3 datasets. Primary consumers and secondary consumers have a wider range of distribution of the best estimate curves (0.001– 1000 mg/L and 0.01–100 mg/L, respectively) than the primary producers (0.01–10 mg/L). The trophic level that shows by far the wider range of distribution, and therefore the higher
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Table 4. Calculated no-observed-effect concentration values for saltwater primary producer and primary consumers and related weighting criteria and weighting coefficient used to build the nano-specific species sensitivity weighted distributionsa Toxicological endpoint
Trophic level
Taxonomic group
Test organism
SW_1
NOEC
Algae
Dunaliella tertiolecta
0.5
0.64 0.6 0.192
0.5
SW_2
HONEC
Algae
Dunaliella tertiolecta
0.5
0.64 0.6 0.192
0.5
SW_3
NOEC
Algae
Isochrysis galbana
0.5
0.64 0.6 0.192
0.5
SW_4
NOEC
Algae
Isochrysis galbana
0.5
0.64 0.6 0.192
0.5
SW_5
HONEC
Algae
Skeletonema costatum
0.5
0.64 0.6 0.192
0.5
SW_6
HONEC
Algae
Skeletonema costatum
0.5
0.64 0.6 0.192
0.5
SW_7
NOEC
Algae
Thalassiosira pseudonana
0.5
0.64 0.6 0.192
0.5
SW_8
HONEC
Algae
Thalassiosira pseudonana
0.5
0.64 0.6 0.192
0.5
SW_9
EC50
Invertebrates
0.26 0.7 0.061
0.333
EC50
0.333
0.26 0.7 0.061
0.333
SW_11
NOEC
0.333
0.26 0.7 0.061
0.333
SW_12
EC50
Invertebrates
Haliotis diversicolor supertexta Haliotis diversicolor supertexta Haliotis diversicolor supertexta Artemia salina
0.333
SW_10
0.125
0.26 0.8 0.026
0.125
SW_13
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_14
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_15
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_16
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_17
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_18
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_19
EC50
Primary producers Primary producers Primary producers Primary producers Primary producers Primary producers Primary producers Primary producers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
ID
Invertebrates Invertebrates
Calculated NOEC (mg/L)
Ws
Wt
Wq
Weighting coefficient
a
For each ID, a reference is reported in the Supplemental Data, Table S1.4. NOEC ¼ no-observed-effect concentration; Ws ¼ species relevance criterion; Wt ¼ trophic levels abundance criterion; Wq ¼ data quality criterion; SW ¼ saltwater; HONEC ¼ highest-observed-no-effect concentration; EC50 ¼ median effective concentration.
ecotoxicological variability, is represented by the primary consumers. This wide variability can be explained by the facts that 1) most of the ecotoxicological tests published in the peerreviewed literature were conducted using species belonging to this trophic level; and 2) the tests reported differ for several parameters such as the testing procedure, type of n-TiO2 tested (e.g., different particles size, or purity), exposure pathway (e.g., ingestion, dermal contact), test duration (i.e., chronic, acute), and pretreatment of the sample. The HC5 values obtained (reported in Table 6) differ by 1 order of magnitude (from 0.00 mg/L to 0.08 mg/L), and the most sensitive trophic level was primary consumers. When the HC50 values were analyzed, the results again differed by 1 order of magnitude (from 0.16 mg/L to 1.12 mg/L) but in the present study the lowest value was obtained for primary producers. This means that for relatively low n-TiO2 concentrations (10–3 mg/L), the first trophic level to be affected is primary consumers, whereas for higher concentrations the most endangered trophic level is primary producers, for which 70% to 85% of species (according to the 5th and 95th centiles, respectively) are affected at an nTiO2 concentration of 1 mg/L (at the same environmental
concentration, 50% and 2.5–10% of primary consumers and secondary consumers are affected, respectively). When the log-normal n-SSWD values are compared for different taxonomic groups (Figure 4A–D), only the n-SSWD curves of algae, invertebrates, and vertebrates have a high R2 (close to or greater than 0.9), whereas the n-SSWD curve for bacteria has a lower R2 (0.7648) but is still acceptable. The only n-SSWD curve with a good KSpvalue (greater than 0.1) is that for bacteria; thus the log-normal distribution is adequate to represent this dataset. In terms of the 8 curves (both log-normal and logempirical) reported in Figure 4, bacteria, invertebrates, and vertebrates have a wider range of distribution of the best estimate curve (0.001–10 mg/L, 0.001–100 mg/L, and 0.01– 100 mg/L, respectively) than algae (0.01–10 mg/L). The taxonomic group that shows by far the wider range of distribution, and therefore the higher ecotoxicological variability, is invertebrates. According to the HC5 values reported in Table 6, the more sensitive taxonomic groups are bacteria and invertebrates, compared with algae and vertebrates. Moreover, Vibrio fischeri is the most affected species among bacteria, Pseudokirchneriella
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Table 5. Calculated no-observed-effect concentration values for terrestrial primary consumers and secondary consumers and related weighting criteria and weighting coefficient used to build the nano-specific species sensitivity weighted distributionsa Toxicological endpoint
Trophic level
Taxonomic group
TR_1
GI
Plant
TR_2
EC50
TR_3
EC50
TR_4
EC50
TR_5
LC50
TR_6
EC50
TR_7
HONEC
TR_8
HONEC
TR_9
HONEC
TR_10
HONEC
TR_11
HONEC
TR_12
HONEC
TR_13
HONEC
TR_14
HONEC
TR_15
HONEC
TR_16
HONEC
TR_17
HONEC
TR_18
HONEC
TR_19
HONEC
Primary producers Primary consumers Primary consumers Primary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers
ID
Test organism
Calculated NOEC (mg/kg) 0.0112
Wq
Weighting coefficient
0.7
0.448
1.000 0.26 0.28
0.073
Ws
Wt
Bacteria
Lactuca sativa, Cucumis sativus, Solanum lycopersicum, Spinacia oleracea Mix of soil bacteria
10
Fungi
Saccharomyces cerevisiae
200
0.500 0.26
0.4
0.052
Fungi
Saccharomyces cerevisiae
200
0.500 0.26
0.4
0.052
Invertebrates
Caenorhabditis elegans
1.000
0.1
0.8
0.080
Invertebrates
Eisenia fetida
100
1.000
0.1
1
0.100
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
30
0.077
0.1
0.7
0.005
0.799
1.000 0.64
a
For each ID, a reference is reported in the Supplemental Data, Table S1.5. NOEC ¼ no-observed-effect concentration; Ws ¼ species relevance criterion; Wt ¼ trophic levels abundance criterion; Wq ¼ data quality criterion; TR ¼ terrestrial; GI ¼ germination index; EC50 ¼ median effective concentration; LC50 ¼ median lethal concentration; HONEC ¼ highest-observed-noeffect concentration.
subcapitata among algae, Daphnia magna among invertebrates, and Xenopus laevis and Danio rerio among vertebrates. As previously discussed in section Trophic level and taxonomic group-specific n-SSWD curves, the trophic level of primary consumers was the most sensitive, and at the same time had a higher variability in toxicological endpoints. The results obtained at the taxonomic group level confirm this observation; in fact, the more sensitive taxonomic group is invertebrates followed by bacteria, both of which belong to the trophic level of primary consumers. Moreover, as shown by the HC50 values obtained, bacteria were also one of the most endangered taxonomic groups (together with algae) at relatively high nTiO2 concentrations (0.5 mg/L). The species with the highest number of ecotoxicological data and at the same time showing the highest variability in ecotoxicological endpoints is D. magna. Daphnia magna is widely used to conduct ecotoxicological tests for various reasons, such as: 1) there are several standardized protocols for this species; 2) the animal has a short life cycle that allows different types of test to be conducted (e.g., inhibition of reproduction, chronic effects on offspring); 3) it is relatively
easy to breed the animal in the laboratory; 4) costs and execution time of the test are contained; and 5) the species is one of the most representative for the freshwater environment. Moreover, in the dataset we used D. magna was tested under different test conditions, thus resulting in the highest variability in ecotoxicological endpoints (as shown in Figure 4G). A similar pattern (i.e., highest presence, highest variability in ecotoxicological response) is shown by some of the most sensitive species identified (V. fischeri for bacteria, P. subcapitata for algae, and X. laevis for vertebrates). Aquatic environment: saltwater
Both log-normal and log-empirical SSD and n-SSWD curves were built for saltwater species (Figure 5). In the present study both curves were constructed using all available data because the application of geometric mean and minimum operators at the species level resulted in only 6 data (i.e., 6 species), a number that was not sufficient to build the traditional SSD curve. (According to Electricite de France [32], the minimum acceptable dataset is 10–15.) For the same reason it was not
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Figure 1. Log-normal (A) and log-empirical (B) traditional species sensitivity distribution (SSD) curves for freshwater compartment. Wm.lg ¼ mean value; wsd.lg ¼ standard deviation value; R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value; Prod. ¼ primary producers, Cons. 1 ¼ primary consumers; Cons. 2 ¼ secondary consumers.
possible to build specific curves for different trophic levels and taxonomic groups. The log-normal SSD obtained has a high R2 (close to 0.9) as well as a high KSpvalue (higher than 0.1), whereas the lognormal n-SSWD curve has a lower R2 (but still acceptable) and an unacceptable KSpvalue. Table 6. Hazard concentration (HC5) and (HC50) values for log-normal and log-empirical species sensitivity distribution and n-SSWD curves and HC5 valuea for the freshwater compartment followed by HC5 and HC50 values for the log-normal and log-empirical n-SSWD curve for the different trophic levels and taxonomic groups in the freshwater compartment Log-normalb
Log-empiricalb
HC5 (mg/L)
HC50 (mg/L)
HC5 (mg/L)
HC50 (mg/L)
Freshwater compartment SSD n-SSWD Gottschalk et al. [11]
0.02 NR NA
0.57 NR NA
0.02 0.02 0.06151 (empirical median value)
0.54 0.22 NA
Freshwater trophic levels Primary producers Primary consumers Secondary consumers
NR NR NR
NR NR NR
0.04 0.00 0.08
0.16 0.99 1.12
Freshwater taxonomic groups Algae Bacteria Invertebrates Vertebrates
NR 0.00 NR NR
NR 0.14 NR NR
0.07 0.01 0.00 0.07
0.21 0.08 0.96 1.23
a
Calculated by Gottschalk et al. [11]. Best estimate (confidence interval of 50%). HCx ¼ hazard concentration for x% of species; SSD ¼ species sensitivity distribution; n-SSWD ¼ nano-specific species sensitivity weighted distribution; NR ¼ not reliable; NA ¼ not available. b
From these curves slightly different HC5 values were derived, ranging from 0.02 mg/L to 0.05 mg/L for SSD (log-normal and log-empirical curves, respectively), and to 0.06 mg/L for n-SSWD (Supplemental Data, Table S2.1). The HC50 values were even more different, with the n-SSWD estimation (0.13 mg/L) being more conservative than the SSD results (0.23 mg/L and 0.37 mg/L for log-empirical and lognormal curves, respectively; Supplemental Data, Table S2.1). Taking into consideration the calculated NOECs for all the species at the different trophic levels, it can be seen that primary producers and primary consumers are equally sensitive, with very close minimum calculated NOEC values (0.07 and 0.04, respectively). However, at higher concentrations, the primary producer trophic level is the most sensitive, with median and maximum calculated NOEC values (0.09 mg/L and 0.70 mg/L, respectively) being lower than the corresponding values for the primary consumers (0.57 mg/L and 9.49 mg/L, respectively). Similar results were obtained by applying the geometric mean at the species level, although they were more conservative (0.07 mg/L, 0.14 mg/L, and 0.26 mg/L as the minimum, median, and maximum values for primary producers compared with 0.68, 0.71, and 0.73 mg/L, respectively, for primary consumers). Because primary producers are represented only by algae species and primary consumers are represented only by invertebrate species, the algae taxonomic group was the most sensitive, with Thalassiosira pseudonana, Dunaliella tertiolecta, and Skeletonema costatum being the more sensitive species. To conclude, because of the paucity of available data, it was not possible, in the present study, to draw robust conclusions on the most sensitive trophic level, taxonomic group, or species. This highlights the need to conduct further ecotoxicological tests on different species (belonging to the 3 trophic levels) for the saltwater environmental compartment. Validation of environmental quality criteria and risk estimation. For saltwater it was not possible to validate our results because no previous studies were available that
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Figure 2. Log-normal (A) and log-empirical (B) nano-specific species sensitivity weighted distribution (n-SSWD) curves for freshwater compartment. Wm. lg ¼ mean value; wsd.lg ¼ standard deviation value; R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value; Prod. ¼ primary producers; Cons. 1 ¼ primary consumers; Cons. 2 ¼ secondary consumers.
addressed n-TiO2 environmental quality standards. Moreover, PEC values were not available, thus preventing any risk estimation for this environmental compartment. Terrestrial environment
Both log-normal and log-empirical SSD and n-SSWD curves were built for terrestrial species (Supplemental Data, Figure S1.1). Both curves were also constructed using all
available data because the application of geometric mean and minimum operators at the species level resulted in only 6 data (i.e., 6 species), which is not sufficient to build the traditional SSD curve. For the same reason it was not possible to build specific curves for different trophic levels and taxonomic groups. The SSD and SSWD curves obtained, including 19 data, were characterized by an inadequate data fitting (R2 < 0.9 and
Figure 3. Log-normal (A–C) and log-empirical (D–F) nano-specific species sensitivity weighted distribution (n-SSWD) curves for the different trophic levels in freshwater compartment. (A,D) Prod. ¼ primary producers (green). (B,E) Cons. 1 ¼ primary consumers (yellow). (C,F) Cons. 2 ¼ secondary consumers (red). Wm.lg ¼ mean value; wsd.lg ¼ standard deviation value; R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value.
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Figure 4. Log-normal (A–D) and log-empirical (E–H) nano-specific species sensitivity weighted distribution (n-SSWD) curves for the different taxonomic groups in freshwater compartment. & ¼ primary producers; D ¼ primary consumers; ^ ¼ secondary consumers. (A,E) Algae: SKE ¼ Skeletonema costatum; SCE ¼ Scenedesmus sp.; PSE ¼ Pseudokirchneriella subcapitata; KAR ¼ Karenia brevis; DES ¼ Desmodesmus subspicatus; CHLO ¼ Chlorella sp.; CHLA ¼ Chlamydomonas reinhardtii. (B,F) Bacteria: VFI ¼ Vibrio fischeri; MIC ¼ 11 microbial species; ECO ¼ Escherichia coli; AVA ¼ Anabaena variabilis. (C,G) Invertebrates: TPL ¼ Thamnocephalus platyurus; OLA ¼ Oryzias latipes; HAT ¼ Hydra attenuata; GFO ¼ Gammarus fossarum; DPU ¼ Daphnia pulex; DPS ¼ Daphnia similis; DMA ¼ Daphnia magna; CSP ¼ Chydorus sphaericus; CDU ¼ Ceriodaphnia dubia. (D,H) Vertebrates: XLA ¼ Xenopus laevis; PPR ¼ Pimephales promelas; OMY ¼ Onchorynchus mykiss; DRE ¼ Danio rerio.
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E. Semenzin et al.
Figure 5. Log-normal (A) and log-empirical (B) species sensitivity distribution (SSD) and log-normal (C) and log-empirical (D) nano-specific species sensitivity weighted distribution (n-SSWD) for the saltwater compartment. Wm.lg ¼ mean value; wsd.lg ¼ standard deviation value, R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value; Prod. ¼ primary producers, Cons. 1 ¼ primary consumers.
KSPvalue < 0.1) and therefore they were judged not reliable. As a consequence, instead of considering the extrapolated HC5 and HC50 values (reported in Supplemental Data, Table S2.2), we decided to use calculated NOEC values for n-TiO2 toxicity for the terrestrial compartment. The whole dataset of calculated NOEC values ranges from 0.0112 mg/kg to 200 mg/kg, with a median value of 100 mg/kg. All the available data are for a few species (6) belonging to the trophic levels of primary producers (1 species), primary consumers (3 species), and secondary consumers (2 species). Most of the data (68%) are for the same species (Porcellio
scaber) and show a high variability in ecotoxicological response. This variability is explained by the fact that the tests conducted on P. scaber used different testing procedures, type of n-TiO2 (e.g., particles size, purity), exposure pathway (e.g., ingestion, dermal contact), exposure time (e.g., chronic, acute test), and pretreatment of the sample. By applying the European Chemicals Agency guidance on information requirements and chemical safety assessment, Chapter R.10 [27] (using the assessment factors given for the terrestrial compartment), it was possible to estimate a predicted no-effect concentration (PNEC) of 0.00112 mg/kg.
Ecological risk assessment of engineered nanomaterials
Taking into consideration the calculated NOEC values for the different trophic levels, it can be observed that the most sensitive trophic level is primary producers, as the minimum, median, and maximum calculated NOEC values (all equal to 0.0112 mg/kg) are lower than the corresponding values for the primary (10 mg/ kg, 200 mg/kg, and 200 mg/kg, respectively) and secondary consumers (0.80 mg/kg, 100 mg/kg, and 100 mg/kg, respectively). Almost the same results are obtained when the geometric mean is applied at the species level to primary producers (0.0112 mg/kg as the minimum, median, and maximum values), primary consumers (10 mg/kg, 105 mg/kg, and 200 mg/kg, respectively), and secondary consumers (0.80 mg/kg, 30 mg/kg, and 100 mg/kg, respectively). When all data are broken down according to taxonomic group, among primary consumers bacteria are more sensitive (10 mg/kg for the minimum, median, and maximum NOEC) than fungi (200 mg/kg for the minimum, median, and maximum NOEC), primary producers are represented only by plants (0.0112 mg/kg for the minimum, median, and maximum NOEC), and secondary consumers are represented only by invertebrates. Invertebrates had the second lowest minimum calculated NOEC value (0.80 mg/kg), but the median (30 mg/kg) and maximum (100 mg/kg) values were higher than those for bacteria. However, because for both plants and bacteria only 1 calculated NOEC is available, no reliable conclusions can be drawn as to which is the most sensitive taxonomic group. To conclude, because of the paucity of available data, it was not possible to draw a robust conclusion on the most sensitive trophic level, taxonomic group, or species. As for the saltwater compartment, this highlights the need to conduct further ecotoxicological tests on different species (belonging to the 3 different trophic levels) for the terrestrial compartment. Validation of environmental quality criteria and risk estimation. Because of the limited data available, validation of the results was not possible: neither Gottschalk et al. [12] nor we in the present study have been able to calculate reliable HC5 values for n-TiO2 in the terrestrial environmental compartment. It was only possible to carry out a risk estimation by comparing the calculated PNEC with the available PECs for soils and sludge-treated soil in Europe proposed by Gottschalk et al. [18] (0.00445 mg/kg and 0.31 mg/kg upper percentile 0.85 estimation, respectively). In terms of the PEC value for soil, comparison with the calculated PNEC (0.00112 mg/L) resulted in a hazard quotient of 3.97, thus suggesting a relevant risk (in contrast to Gottschalk et al. [12], whose results showed no risk occurring in this environmental compartment). In terms of the PEC value for sludge-treated soil, comparison with the same PNEC resulted in an extremely high hazard quotient (276.77), indicating a greater possible risk for this environmental compartment than that given by Gottschalk et al. [12]. However, to draw more robust conclusions and eventually confirm these results, probabilistic distributions of both exposure and effect concentrations should be used. CONCLUSIONS
Although conventional SSD and n-SSWD did not show substantial differences in the environmental quality criteria estimation, the proposed n-SSWD approach appeared to be a valuable tool for ecological risk assessment because it allows better evaluation of endpoint variability and therefore identification of more sensitive trophic levels and taxonomic groups. The inclusion of a weighting coefficient composed of species relevance, trophic level abundance, and data quality weighting criteria allowed us to make use of all the available
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ecotoxicological data and at the same time to account for their relevance (in representing the environmental compartment studied) as well as their reliability. Moreover, the adoption of the method proposed by Card and Magnuson [20] allowed us to assess the quality of nanotoxicity studies by supplementing the Klimisch score [31] with a quantitative estimation of whether the physicochemical characterization provided for the tested nanomaterial is adequate to fully understand and trust the ecotoxicological value to be used in the ecological risk assessment. In our dataset, information was missing in several studies on the shape of the particles, the surface charge, and the surface chemistry (including composition and reactivity), thus significantly reducing the reliability of those data and therefore their weight in the ecological risk assessment. Application of the available ecotoxicological dataset highlighted that n-TiO2 poses marginal risks to organisms exposed to freshwater (mainly primary consumers), and unacceptable risks to organisms exposed to sludge-treated soil (hazard quotient ¼ 19.39), soil (hazard quotient ¼ 276.77), and STP effluent (26% potentially affected fraction). Moreover, it can be expected that use of the complete distributions of the PEC values provided by Sun et al. [26] will result in significantly higher risks for the freshwater environmental compartment. For the saltwater environmental compartment, because PEC values are currently not available, it was not possible to perform a risk estimation. However, to confirm or refine the results we obtained from the present study, this approach should be tested with a more complete dataset, once it is available. Moreover, to further test the performance of the proposed approach, it should be applied to other ENMs (e.g., CuO). The n-SSWD approach demonstrates the benefits derived from integrating a quantitative data quality evaluation (which is crucial given the unreliable datasets resulting from nonstandardized testing and analytical methodologies) into a model for nano-ecological risk assessment. However, substantial expert input is required for its application, which can introduce potential biases, thus increasing uncertainty in the analysis. Therefore we recommend that further work should focus on characterizing the uncertainties stemming from input data, expert judgment, and model performance. SUPPLEMENTAL DATA
Background on SSD and SSWD. Tables S1–S2. Figure S1. (135 KB DOC). Acknowledgment—The present study received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreements 263215 (MAnaging RIsks of Nanomaterials [MARINA]) and 604305 (Sustainable Nanotechnologies [SUN]). The authors acknowledge K. Rasmussen, S. Gottardo, and J. Riego Sintes (Joint Research Centre, Ispra, Italy) for valuable comments and revisions to the first version of the paper. Data availability—Data, associated metadata, and calculation tools are included in the Supplemental Data accompanying the present study. REFERENCES 1. Hristozov DR, Gottardo S, Critto A, Marcomini A. 2012. Risk assessment of engineered nanomaterials: A review of available data and approaches from regulatory perspective. Nanotoxicology 6:880–898. 2. British Standards Institution. 2007. PAS136 terminology for nanomaterials. [cited 2013 February 13]. Available from: http://www. bsiglobal.com
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3. L€ovestam G, Rauscher H, Roebben G, Kl€ uttgen BS, Gibson N, Putaud J-P, Stamm H. 2010. Considerations on a definition of nanomaterial for regulatory purposes. EUR 24403 EN. Joint Research Center of the EU Commission, Ispra, Italy. 4. European Commission. 2011. Commission recommendation of 18 October 2011 on the definition of nanomaterial. 2011/696/EU. Official Journal of the European Union 38–40. 5. Aschberger K, Micheletti C, Sokull-Kluttgen B, Christensen FM. 2011. Analysis of currently available data for characterising the risk of engineered nanomaterials to the environment and human health— Lessons learned from four case studies. Environ Int 37:143–1156. 6. Klaine SJ, Alvarez PJJ, Batley GE, Fernandes TF, Handy RD, Lyon DY, Mahendra S, McLaughlin MJ, Lead JR. 2008. Nanomaterials in the environment: Behavior, fate, bioavailability, and effects. Environ Toxicol Chem 27:1825–1851. 7. Organisation for Economic Co-operation and Development. 2015. Safety of manufactured nanomaterials. Paris, France. [cited 2015 May 18]. Available from: http://www.oecd.org/env/ehs/nanosafety/ 8. Donaldson K, Poland CA. 2013. Nanotoxicity: Challenging the myth of nano-specific toxicity. Curr Opin Biotechnol 24:724–734. 9. Tervonen T, Linkov I, Figueira J, Steevens J, Chappell M, Merad M. 2009. Risk based classification system of nanomaterials. J Nanopart Res 11:757–766. 10. H€ock J, Epprecht T, Hofmann H, H€ ohner K, Krug H, Lorenz C, Limbach L, Gehr P, Nowack B, Riediker M, Schirmer K, Schmid B, Som C, Stark W, Studer C, Ulrich A, G€ otz NV, Wengert S, Wick P. 2010. Guidelines on the Precautionary Matrix for Synthetic Nanomaterials. Federal Office of Public Health and Federal Office for the Environment, Berne, Switzerland. 11. Dale AL, Lowry GV, Casman EA. 2013. Modeling nanosilver transformations in freshwater sediments. Environ Sci Technol 47: 12920–12928. 12. Gottschalk F, Kost E, Nowack B. 2013. Engineered nanomaterials in water and soils: A risk quantification based on probabilistic exposure and effect modeling. Environ Toxicol Chem 32:1278–1287. 13. Suter GW II. 2006. Ecological Risk Assessment, 2nd ed. CRC, Boca Raton, FL, USA. 14. K€uhnel D, Nickel C. 2014. The OECD expert meeting on ecotoxicology and environmental fate—Towards the development of improved OECD guidelines for the testing of nanomaterials. Sci Total Environ 472: 347–353. 15. Byrne HJ, Omen AG, Bos PMJ, Fernandes TF, Hund-Rinke K. Boraschi D, Aschberger K, Gottardo S, von der Kammer F, K€ uhnel D, Hristozov D, Marcomini A, Migliore L. 2014. Concern-driven integrated toxicity testing strategies for nanomaterials—Report of the NanoSafety Cluster Working Group 10. Nanotoxicology 14:195–216. 16. Organisation for Economic Co-operation and Development. 2015. OECD Guidelines for the Testing of Chemicals. Paris France. [cited 2015 May 25]. Available from: http://www.oecd.org/chemicalsafety/ testing/oecdguidelinesforthetestingofchemicals.htm 17. Stone V, Pozzi-Mucelli S, Tran L, Aschberger K, Sabella S, Vogel UB, Poland C, Balharry D, Fernandes T, Gottardo S, Hankin S, Hartl M,
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Hartmann N, Hristozov D, Hund-Rinke K, Johnston H, Marcomini A, Panzer O, Roncato D, Saber AT, Wallin H, Scott-Fordsmand JJ. 2014. ITS-NANO—Prioritising nanosafety research to develop a stakeholder driven intelligent testing strategy. Part Fibre Toxicol 11:9. Gottschalk F, Sonderer T, Scholz RW, Nowack B. 2009. Modeled environmental concentrations of engineered nanomaterials (TiO2, ZnO, Ag, CTN, Fullerenes) for different regions. Environ Sci Technol 43:9216–9222. Posthuma L, Suter II GW, Traas TP. 2002. Species Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA. Card JW, Magnuson BA. 2010. A method to assess the quality of studies that examine the toxicity of engineered nanomaterials. Int J Toxicol 29:402–410. Piccinno F, Gottschalk F, Seeger S, Nowack B. 2012. Industrial production quantities and uses of ten engineered nanomaterials in Europe and the world. J Nanopart Res 14:1109. Xiong D, Fang T, Yu L, Sima X, Zhu W. 2011. Effects of nano-scale TiO2, ZnO and their bulk counterparts on zebrafish: Acute toxicity, oxidative stress and oxidative damage. Sci Total Environ 409:1444–1452. Wang H, Wick RL, Xing B. 2009. Toxicity of nanoparticulate and bulk ZnO, Al2O3 and TiO2 to the nematode Caenorhabditis elegans. Environ Pollut 157:1171–1177. Miller RJ, Bennett S, Keller AA, Pease S, Lenihan HS. 2012. TiO2 nanoparticles are phototoxic to marine phytoplankton. PLoS One 7: e30321. Zhang J, Wages YM, Cox YSB, Maul YJD, Li YY, Barnes ZM, HopeWeeks ZL, Cobb GP. 2012. Effect of titanium dioxide nanomaterials and ultraviolet light coexposure on African clawed frogs (Xenopus laevis). Environ Toxicol Chem 31:176–183. Sun TY, Gottschalk F, Hungerb€ uhler K, Nowack B. 2014. Comprehensive probabilistic modelling of environmental emissions of engineered nanomaterials. Environ Pollut 185:69–76. European Chemicals Agency. 2008. Guidance on information requirements and chemical safety assessment. Chapter R.10: Characterisation of dose [concentration]-response for environment. Helsinki, Finland. Duboudin C, Ciffroy P, Magaud H. 2004. Effects of data manipulation and statistical methods on species sensitivity distributions. Environ Toxicol Chem 23:489–499. Forbes VE, Calow P. 2002. Species sensitivity distributions revisited: A critical appraisal. Hum Ecol Risk Assess 8:473–492. European Union Reference Laboratory for Alternatives to Animal Testing. 2009. ToxRTool. European commission Joint Research Centre, Ispra, Italy. [cited 2015 April 27]. Available from: https://eurl-ecvam.jrc.ec. europa.eu/about-ecvam/archive-publications/toxrtool Klimisch HJ, Andreae M, Tillmann U. 1997. A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25:1–5. Electricite de France (EDF), Institut de l'Environnement Industriel et des Risques (INERIS). 2003. Species Sensitivity Weighted Distribution, Software User’s Guide. Version 1.0. Paris, France.
Hazard/Risk Assessment SPECIES SENSITIVITY WEIGHTED DISTRIBUTION FOR ECOLOGICAL RISK ASSESSMENT OF ENGINEERED NANOMATERIALS: THE n-TiO2 CASE STUDY ELENA SEMENZIN, ELISA LANZELLOTTO, DANAIL HRISTOZOV, ANDREA CRITTO, ALEX ZABEO, ELISA GIUBILATO, and ANTONIO MARCOMINI* Department of Environmental Sciences, Informatics, and Statistics, University Ca’ Foscari Venice, Venice, Italy (Submitted 20 January 2015; Returned for Revision 5 March 2015; Accepted 4 June 2015) Abstract: Societal concerns about the environmental risks of engineered nanomaterials (ENMs) have recently increased, but
nano-ecological risk assessments are constrained by significant gaps in basic information on long-term effects and exposures, for example. An approach to the ecological risk assessment of ENMs is proposed that can operate in the context of high uncertainty. This approach further develops species sensitivity weighted distribution (SSWD) by including 3 weighting criteria (species relevance, trophic level abundance, and nanotoxicity data quality) to address nano-specific needs (n-SSWD). The application of n-SSWD is illustrated for nanoscale titanium dioxide (n-TiO2), which is available in different crystal forms; it was selected because of its widespread use in consumer products (e.g., cosmetics) and the ample availability of data from ecotoxicological studies in the literature (including endpoints for algae, invertebrates, bacteria, and vertebrates in freshwater, saltwater, and terrestrial compartments). The n-SSWD application resulted in estimation of environmental quality criteria (hazard concentration affecting 5% and 50% of the species) and ecological risk (potentially affected fraction of species), which were then compared with similar results obtained by applying the traditional species sensitivity distribution (SSD) approach to the same dataset. The n-SSWDs were also built for specific trophic levels (e.g., primary producers) and taxonomic groups (e.g., algae), which helped to identify the most sensitive organisms. These results showd that n-SSWD is a valuable risk tool, although further testing is suggested. Environ Toxicol Chem 2015;9999:1–16. # 2015 SETAC Keywords: Species sensitivity distribution
Ecological risk assessment
Nanomaterials
Data quality
Titanium dioxide
some nonconventional tools have been recently proposed to address them [9,10]. Most of these tools address human health risk assessment, although there are only few specific ecological risk assessment tools, which mainly focus on environmental exposure assessment [1,11,12]. This is mainly because ecological risk assessment [13] presents a number of specific issues when applied to ENMs [1]. For example, the scarcity of characterization data for ENMs in environmentally relevant media and quantitative toxicity models makes it difficult to identify which physicochemical characteristics determine the effects documented in ecotoxicity studies. In addition to the difficulties in assessing the ecotoxicity of ENMs, there are various issues hampering the assessment of the environmental exposure including, but not limited to: 1) environmental behavior and fate of ENMs, which is largely unexplored; and 2) lack of adequate methods and tools for effective monitoring of ENMs in the different media and distinguishing them from the background particles [14]. Another issue is that most of the ecotoxicological studies reported in literature have used nonstandardized tests and test materials from different sources (responsible, in part, for the generation of nonreproducible/comparable results) [15]; the Organisation for Economic Co-operation and Development introduced standardized test guidelines for regulatory testing of chemicals to ensure global recognition of the methods used and is currently evaluating their applicability to nanomaterials (for more information, see Organisation for Economic Co-operation and Development [16]). Data quality is crucial for all risk assessment steps; in fact, data gathered based on standard testing guidelines often offer the most robust and transparent information [17]. Therefore, the need to ensure quality control (validity, reliability) and relevance of the ecotoxicological data on ENMs [17] before using them for an ecological risk assessment is highlighted.
INTRODUCTION
Nanotechnology is an emerging field in the area of science and technology involving the design, production, and use of structures at the nano-scale (i.e., from 1 nm to 100 nm) called nanomaterials [1,2]. Presently there is no internationally harmonized definition of engineered nanomaterials (ENMs) [3]; according to the European Commission’s recommendation, a nanomaterial is defined as “a natural, incidental or manufactured material containing particles in a unbound state or as aggregate or as an agglomerate and where, for 50% or more of the particles in the number size distribution, one or more external dimension is in the size range 1–100 nm” [4]. Because of their unique or enhanced physicochemical properties (e.g., tiny size, large surface area, surface reactivity, charge, shape), some ENMs are suitable for a wide variety of applications in many sectors (e.g., information technology, energy production, food, agriculture) [5]. In the last decade, there has been a significant increase in the development and widespread use of nano-products, and at the same time societal concerns regarding the potential ecological and human health risks from ENMs have increased [6]. The Organisation for Economic Co-operation and Development and its member countries concluded that “the approaches for the testing and assessment of traditional chemicals are in general appropriate for assessing the safety of nanomaterials, but may have to be adapted to the specificities of nanomaterials” [7]. Although the scientific community is not unanimous in recognizing some specificities of nanomaterials [8], All Supplemental Data may be found in the online version of this article. * Address correspondence to [email protected]. Published online 8 June 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/etc.3103 1
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Very recently, Gottschalk et al. [12] proposed the first quantitative ecological risk assessment approach for ENMs. The aim of their study was to quantify the environmental risk of ENMs by building species sensitivity distribution (SSD) curves that were then compared with probability distributions of predicted environmental concentrations (PECs) developed by Gottschalk et al. [18]. The approach was tested on 5 ENMs (n-Ag, n-TiO2, n-Zn, carbon nanotubes, and fullerenes), and the results demonstrated a marginal risk of these ENMs in surface water and some risk in sewage treatment plant effluent, although in the terrestrial compartment no risk was predicted except for a marginal value for n-TiO2 in sludge-treated soils [12]. In contrast to the traditional SSD approach [19], the technique of Gottschalk et al. [12] makes use of all available ecotoxicological data in building the cumulative distribution curves. Not reducing the dataset to the number of tested species is indeed very positive, particularly when data are scarce (as they are for most nanomaterials). However, we consider that the integration of such a dataset in a single SSD should take into account the relevance of the ecotoxicological data in representing the ecosystem of concern as well as the reliability of the data, a factor that is critical for all chemicals and that presents some peculiarities for nanomaterials [20]. For this reason we propose an ecological risk assessment approach, called nano-species sensitivity weighted distribution (n-SSWD), which is able to critically integrate the available information and update the risk estimation as soon as additional ecotoxicological data are available. In n-SSWD, the emphasis is on assessment of the quality of ecotoxicological data, according to the nanospecific method proposed by Card and Magnuson [20], to allow a more appropriate use of the available, mainly heterogeneous, ecotoxicological information in ecological risk assessment. In the present study the n-SSWD approach is compared with both the traditional SSD [19] and the probabilistic SSD proposed by Gottschalk and colleagues [12]; as a case study, we used nanoscale titanium dioxide (n-TiO2), which is available in different crystal forms as well as an amorphous form. (Additional background information on traditional SSD and SSWD approaches is included in the Supplemental Data.) MATERIALS AND METHODS
Case study: n-TiO2
Nanoscale titanium dioxide was selected as the case study because, as a result of its unique or enhanced physicochemical properties, it is suitable for a wide variety of applications in many sectors (e.g., cosmetics, household cleaning products, paints). For this reason the market for n-TiO2 has been exponentially growing, and in 2012 worldwide production of up to 10 000 t/yr was estimated [21]. At the same time, societal concerns regarding the potential environmental and health risks from this material have increased and need to be addressed through robust risk analysis [6]. Numerous toxicological and ecotoxicological tests have been carried out and published to better understand whether n-TiO2 may cause adverse effects on the environment and human health because of its enhanced reactivity. The studies conducted so far have shown that the toxicity of n-TiO2 is far higher than that of its bulk form in both aquatic and terrestrial environments and have suggested that n-TiO2 has size-dependent toxic effects [22,23]. Several studies showed the ability of n-TiO2 to generate reactive oxygen species (ROS) after exposure to a strong artificial source of ultraviolet (UV) radiation. These ROS are able to cause cytotoxicity in the test organism and for this reason oxidative
E. Semenzin et al.
stress mediated by photoactive n-TiO2 is the likely mechanism of its toxicity [24]. However, no studies have demonstrated so far that photoactivity is responsible for the environmental toxicity of n-TiO2 under natural levels of UV radiation [24], and also that the effectiveness was not demonstrated of n-TiO2 interaction with ultraviolet light strongly depends on particle size [25] as well as the crystal form of TiO2. Regarding the fate and behavior of ENMs and in particular of n-TiO2 in environmental compartments, Gottschalk et al. [17] and Sun et al. [26] published environmental concentrations of different ENMs, including n-TiO2, modeled for 3 different areas (the United States, Europe, and Switzerland) in different environmental compartments (aquatic and terrestrial) [17,26]. The results, although affected by uncertainties related to both input data (e.g., emission estimates) and modeling assumptions, showed that n-TiO2 can reach concentrations of 220 mg/L in a freshwater compartment (i.e., sewage treatment plant effluent) [26] and 89.2 mg/kg in a terrestrial compartment (i.e., sludge-treated soil) [18]. Accordingly, it can be concluded that there are still too many uncertainties about the toxicity, behavior, and fate of n-TiO2 in the environment, and that this uncertainty strongly influence ecological risk assessment. Nevertheless, as mentioned in the Introduction, a recent study has published a probabilistic ecological risk assessment based on SSD for 5 ENMs (n-Ag, n-TiO2, n-ZnO, carbon nanotubes, and fullerenes) and 4 environmental compartments (surface freshwater, sewage treatment plant effluents, soils, and sludge-treated soils) [12]. The study [12] collected 34 ecotoxicological endpoints in 23 papers for n-TiO2 in different crystalline forms (e.g., rutile or anatase). With this dataset, SSD curves were built by applying an innovative probabilistic approach and subsequently compared with PEC distributions for estimating the risk for both aquatic and terrestrial environments [12,18]. The results indicated that there is only a marginal risk from n-TiO2 (5% of affected species) for the freshwater compartment (higher than the marginal risk estimated by Gottschalk et al. [12]). In terms of the PEC value for STP effluent, the comparison with both SSD and n-SSWD allowed calculation of the potentially affected fraction of species. Specifically, at the considered PEC value the affected percentages were: 20% of species (according to the best estimate curve; 10–35% considering the 5th percentile and 95th percentile, respectively) by using SSD, and 30% of species (28– 32%) by using n-SSWD. Therefore, the conventional SSD seemed to be less conservative than the n-SSWD, showing, at the same time, a higher variability in the response value range (25% for SSD compared with 4% for n-SSWD). Overall, our risk estimations for surface water and STP effluent in the present study appeared to be similar to those of Gottschalk et al. [12]. However, because in the present study we used the higher PEC values proposed by Sun et al. [26] for Europe (400 times and 100 times higher for 0.85 upper percentile for surface water and STP effluent, respectively, compared with the PECs proposed by Gottschalk et al. [18] and used in Gottschalk et al. [12]), it is expected that use of the
complete PEC distributions would result in a significantly higher risk estimation (i.e., >5% for surface water and >40% for STP effluent). Trophic level and taxonomic group-specific n-SSWD curves. By applying the approach described in the Materials and Methods section, specific n-SSWD curves were built for each trophic level (primary producers, primary consumers, and secondary consumers) as well as taxonomic group (algae, bacteria, invertebrates, and vertebrates) represented in the ecotoxicological dataset for the freshwater compartment. The results are presented in Figure 3 and Figure 4 and discussed in the following paragraphs. The log-normal n-SSWD values obtained for different trophic levels (Figure 3A–C) have high and very similar R2 values (0.9 or higher), but none of them have a good KSpvalue (greater than 0.1). This means that the log-normal distribution is not adequate to represent the 3 datasets. Primary consumers and secondary consumers have a wider range of distribution of the best estimate curves (0.001– 1000 mg/L and 0.01–100 mg/L, respectively) than the primary producers (0.01–10 mg/L). The trophic level that shows by far the wider range of distribution, and therefore the higher
Ecological risk assessment of engineered nanomaterials
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Table 4. Calculated no-observed-effect concentration values for saltwater primary producer and primary consumers and related weighting criteria and weighting coefficient used to build the nano-specific species sensitivity weighted distributionsa Toxicological endpoint
Trophic level
Taxonomic group
Test organism
SW_1
NOEC
Algae
Dunaliella tertiolecta
0.5
0.64 0.6 0.192
0.5
SW_2
HONEC
Algae
Dunaliella tertiolecta
0.5
0.64 0.6 0.192
0.5
SW_3
NOEC
Algae
Isochrysis galbana
0.5
0.64 0.6 0.192
0.5
SW_4
NOEC
Algae
Isochrysis galbana
0.5
0.64 0.6 0.192
0.5
SW_5
HONEC
Algae
Skeletonema costatum
0.5
0.64 0.6 0.192
0.5
SW_6
HONEC
Algae
Skeletonema costatum
0.5
0.64 0.6 0.192
0.5
SW_7
NOEC
Algae
Thalassiosira pseudonana
0.5
0.64 0.6 0.192
0.5
SW_8
HONEC
Algae
Thalassiosira pseudonana
0.5
0.64 0.6 0.192
0.5
SW_9
EC50
Invertebrates
0.26 0.7 0.061
0.333
EC50
0.333
0.26 0.7 0.061
0.333
SW_11
NOEC
0.333
0.26 0.7 0.061
0.333
SW_12
EC50
Invertebrates
Haliotis diversicolor supertexta Haliotis diversicolor supertexta Haliotis diversicolor supertexta Artemia salina
0.333
SW_10
0.125
0.26 0.8 0.026
0.125
SW_13
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_14
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_15
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_16
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_17
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_18
EC50
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
SW_19
EC50
Primary producers Primary producers Primary producers Primary producers Primary producers Primary producers Primary producers Primary producers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers Primary consumers
Invertebrates
Artemia salina
0.125
0.26 0.8 0.026
0.125
ID
Invertebrates Invertebrates
Calculated NOEC (mg/L)
Ws
Wt
Wq
Weighting coefficient
a
For each ID, a reference is reported in the Supplemental Data, Table S1.4. NOEC ¼ no-observed-effect concentration; Ws ¼ species relevance criterion; Wt ¼ trophic levels abundance criterion; Wq ¼ data quality criterion; SW ¼ saltwater; HONEC ¼ highest-observed-no-effect concentration; EC50 ¼ median effective concentration.
ecotoxicological variability, is represented by the primary consumers. This wide variability can be explained by the facts that 1) most of the ecotoxicological tests published in the peerreviewed literature were conducted using species belonging to this trophic level; and 2) the tests reported differ for several parameters such as the testing procedure, type of n-TiO2 tested (e.g., different particles size, or purity), exposure pathway (e.g., ingestion, dermal contact), test duration (i.e., chronic, acute), and pretreatment of the sample. The HC5 values obtained (reported in Table 6) differ by 1 order of magnitude (from 0.00 mg/L to 0.08 mg/L), and the most sensitive trophic level was primary consumers. When the HC50 values were analyzed, the results again differed by 1 order of magnitude (from 0.16 mg/L to 1.12 mg/L) but in the present study the lowest value was obtained for primary producers. This means that for relatively low n-TiO2 concentrations (10–3 mg/L), the first trophic level to be affected is primary consumers, whereas for higher concentrations the most endangered trophic level is primary producers, for which 70% to 85% of species (according to the 5th and 95th centiles, respectively) are affected at an nTiO2 concentration of 1 mg/L (at the same environmental
concentration, 50% and 2.5–10% of primary consumers and secondary consumers are affected, respectively). When the log-normal n-SSWD values are compared for different taxonomic groups (Figure 4A–D), only the n-SSWD curves of algae, invertebrates, and vertebrates have a high R2 (close to or greater than 0.9), whereas the n-SSWD curve for bacteria has a lower R2 (0.7648) but is still acceptable. The only n-SSWD curve with a good KSpvalue (greater than 0.1) is that for bacteria; thus the log-normal distribution is adequate to represent this dataset. In terms of the 8 curves (both log-normal and logempirical) reported in Figure 4, bacteria, invertebrates, and vertebrates have a wider range of distribution of the best estimate curve (0.001–10 mg/L, 0.001–100 mg/L, and 0.01– 100 mg/L, respectively) than algae (0.01–10 mg/L). The taxonomic group that shows by far the wider range of distribution, and therefore the higher ecotoxicological variability, is invertebrates. According to the HC5 values reported in Table 6, the more sensitive taxonomic groups are bacteria and invertebrates, compared with algae and vertebrates. Moreover, Vibrio fischeri is the most affected species among bacteria, Pseudokirchneriella
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Table 5. Calculated no-observed-effect concentration values for terrestrial primary consumers and secondary consumers and related weighting criteria and weighting coefficient used to build the nano-specific species sensitivity weighted distributionsa Toxicological endpoint
Trophic level
Taxonomic group
TR_1
GI
Plant
TR_2
EC50
TR_3
EC50
TR_4
EC50
TR_5
LC50
TR_6
EC50
TR_7
HONEC
TR_8
HONEC
TR_9
HONEC
TR_10
HONEC
TR_11
HONEC
TR_12
HONEC
TR_13
HONEC
TR_14
HONEC
TR_15
HONEC
TR_16
HONEC
TR_17
HONEC
TR_18
HONEC
TR_19
HONEC
Primary producers Primary consumers Primary consumers Primary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers Secondary consumers
ID
Test organism
Calculated NOEC (mg/kg) 0.0112
Wq
Weighting coefficient
0.7
0.448
1.000 0.26 0.28
0.073
Ws
Wt
Bacteria
Lactuca sativa, Cucumis sativus, Solanum lycopersicum, Spinacia oleracea Mix of soil bacteria
10
Fungi
Saccharomyces cerevisiae
200
0.500 0.26
0.4
0.052
Fungi
Saccharomyces cerevisiae
200
0.500 0.26
0.4
0.052
Invertebrates
Caenorhabditis elegans
1.000
0.1
0.8
0.080
Invertebrates
Eisenia fetida
100
1.000
0.1
1
0.100
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
100
0.077
0.1
0.7
0.005
Invertebrates
Porcellio scaber
30
0.077
0.1
0.7
0.005
0.799
1.000 0.64
a
For each ID, a reference is reported in the Supplemental Data, Table S1.5. NOEC ¼ no-observed-effect concentration; Ws ¼ species relevance criterion; Wt ¼ trophic levels abundance criterion; Wq ¼ data quality criterion; TR ¼ terrestrial; GI ¼ germination index; EC50 ¼ median effective concentration; LC50 ¼ median lethal concentration; HONEC ¼ highest-observed-noeffect concentration.
subcapitata among algae, Daphnia magna among invertebrates, and Xenopus laevis and Danio rerio among vertebrates. As previously discussed in section Trophic level and taxonomic group-specific n-SSWD curves, the trophic level of primary consumers was the most sensitive, and at the same time had a higher variability in toxicological endpoints. The results obtained at the taxonomic group level confirm this observation; in fact, the more sensitive taxonomic group is invertebrates followed by bacteria, both of which belong to the trophic level of primary consumers. Moreover, as shown by the HC50 values obtained, bacteria were also one of the most endangered taxonomic groups (together with algae) at relatively high nTiO2 concentrations (0.5 mg/L). The species with the highest number of ecotoxicological data and at the same time showing the highest variability in ecotoxicological endpoints is D. magna. Daphnia magna is widely used to conduct ecotoxicological tests for various reasons, such as: 1) there are several standardized protocols for this species; 2) the animal has a short life cycle that allows different types of test to be conducted (e.g., inhibition of reproduction, chronic effects on offspring); 3) it is relatively
easy to breed the animal in the laboratory; 4) costs and execution time of the test are contained; and 5) the species is one of the most representative for the freshwater environment. Moreover, in the dataset we used D. magna was tested under different test conditions, thus resulting in the highest variability in ecotoxicological endpoints (as shown in Figure 4G). A similar pattern (i.e., highest presence, highest variability in ecotoxicological response) is shown by some of the most sensitive species identified (V. fischeri for bacteria, P. subcapitata for algae, and X. laevis for vertebrates). Aquatic environment: saltwater
Both log-normal and log-empirical SSD and n-SSWD curves were built for saltwater species (Figure 5). In the present study both curves were constructed using all available data because the application of geometric mean and minimum operators at the species level resulted in only 6 data (i.e., 6 species), a number that was not sufficient to build the traditional SSD curve. (According to Electricite de France [32], the minimum acceptable dataset is 10–15.) For the same reason it was not
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Figure 1. Log-normal (A) and log-empirical (B) traditional species sensitivity distribution (SSD) curves for freshwater compartment. Wm.lg ¼ mean value; wsd.lg ¼ standard deviation value; R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value; Prod. ¼ primary producers, Cons. 1 ¼ primary consumers; Cons. 2 ¼ secondary consumers.
possible to build specific curves for different trophic levels and taxonomic groups. The log-normal SSD obtained has a high R2 (close to 0.9) as well as a high KSpvalue (higher than 0.1), whereas the lognormal n-SSWD curve has a lower R2 (but still acceptable) and an unacceptable KSpvalue. Table 6. Hazard concentration (HC5) and (HC50) values for log-normal and log-empirical species sensitivity distribution and n-SSWD curves and HC5 valuea for the freshwater compartment followed by HC5 and HC50 values for the log-normal and log-empirical n-SSWD curve for the different trophic levels and taxonomic groups in the freshwater compartment Log-normalb
Log-empiricalb
HC5 (mg/L)
HC50 (mg/L)
HC5 (mg/L)
HC50 (mg/L)
Freshwater compartment SSD n-SSWD Gottschalk et al. [11]
0.02 NR NA
0.57 NR NA
0.02 0.02 0.06151 (empirical median value)
0.54 0.22 NA
Freshwater trophic levels Primary producers Primary consumers Secondary consumers
NR NR NR
NR NR NR
0.04 0.00 0.08
0.16 0.99 1.12
Freshwater taxonomic groups Algae Bacteria Invertebrates Vertebrates
NR 0.00 NR NR
NR 0.14 NR NR
0.07 0.01 0.00 0.07
0.21 0.08 0.96 1.23
a
Calculated by Gottschalk et al. [11]. Best estimate (confidence interval of 50%). HCx ¼ hazard concentration for x% of species; SSD ¼ species sensitivity distribution; n-SSWD ¼ nano-specific species sensitivity weighted distribution; NR ¼ not reliable; NA ¼ not available. b
From these curves slightly different HC5 values were derived, ranging from 0.02 mg/L to 0.05 mg/L for SSD (log-normal and log-empirical curves, respectively), and to 0.06 mg/L for n-SSWD (Supplemental Data, Table S2.1). The HC50 values were even more different, with the n-SSWD estimation (0.13 mg/L) being more conservative than the SSD results (0.23 mg/L and 0.37 mg/L for log-empirical and lognormal curves, respectively; Supplemental Data, Table S2.1). Taking into consideration the calculated NOECs for all the species at the different trophic levels, it can be seen that primary producers and primary consumers are equally sensitive, with very close minimum calculated NOEC values (0.07 and 0.04, respectively). However, at higher concentrations, the primary producer trophic level is the most sensitive, with median and maximum calculated NOEC values (0.09 mg/L and 0.70 mg/L, respectively) being lower than the corresponding values for the primary consumers (0.57 mg/L and 9.49 mg/L, respectively). Similar results were obtained by applying the geometric mean at the species level, although they were more conservative (0.07 mg/L, 0.14 mg/L, and 0.26 mg/L as the minimum, median, and maximum values for primary producers compared with 0.68, 0.71, and 0.73 mg/L, respectively, for primary consumers). Because primary producers are represented only by algae species and primary consumers are represented only by invertebrate species, the algae taxonomic group was the most sensitive, with Thalassiosira pseudonana, Dunaliella tertiolecta, and Skeletonema costatum being the more sensitive species. To conclude, because of the paucity of available data, it was not possible, in the present study, to draw robust conclusions on the most sensitive trophic level, taxonomic group, or species. This highlights the need to conduct further ecotoxicological tests on different species (belonging to the 3 trophic levels) for the saltwater environmental compartment. Validation of environmental quality criteria and risk estimation. For saltwater it was not possible to validate our results because no previous studies were available that
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Figure 2. Log-normal (A) and log-empirical (B) nano-specific species sensitivity weighted distribution (n-SSWD) curves for freshwater compartment. Wm. lg ¼ mean value; wsd.lg ¼ standard deviation value; R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value; Prod. ¼ primary producers; Cons. 1 ¼ primary consumers; Cons. 2 ¼ secondary consumers.
addressed n-TiO2 environmental quality standards. Moreover, PEC values were not available, thus preventing any risk estimation for this environmental compartment. Terrestrial environment
Both log-normal and log-empirical SSD and n-SSWD curves were built for terrestrial species (Supplemental Data, Figure S1.1). Both curves were also constructed using all
available data because the application of geometric mean and minimum operators at the species level resulted in only 6 data (i.e., 6 species), which is not sufficient to build the traditional SSD curve. For the same reason it was not possible to build specific curves for different trophic levels and taxonomic groups. The SSD and SSWD curves obtained, including 19 data, were characterized by an inadequate data fitting (R2 < 0.9 and
Figure 3. Log-normal (A–C) and log-empirical (D–F) nano-specific species sensitivity weighted distribution (n-SSWD) curves for the different trophic levels in freshwater compartment. (A,D) Prod. ¼ primary producers (green). (B,E) Cons. 1 ¼ primary consumers (yellow). (C,F) Cons. 2 ¼ secondary consumers (red). Wm.lg ¼ mean value; wsd.lg ¼ standard deviation value; R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value.
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Figure 4. Log-normal (A–D) and log-empirical (E–H) nano-specific species sensitivity weighted distribution (n-SSWD) curves for the different taxonomic groups in freshwater compartment. & ¼ primary producers; D ¼ primary consumers; ^ ¼ secondary consumers. (A,E) Algae: SKE ¼ Skeletonema costatum; SCE ¼ Scenedesmus sp.; PSE ¼ Pseudokirchneriella subcapitata; KAR ¼ Karenia brevis; DES ¼ Desmodesmus subspicatus; CHLO ¼ Chlorella sp.; CHLA ¼ Chlamydomonas reinhardtii. (B,F) Bacteria: VFI ¼ Vibrio fischeri; MIC ¼ 11 microbial species; ECO ¼ Escherichia coli; AVA ¼ Anabaena variabilis. (C,G) Invertebrates: TPL ¼ Thamnocephalus platyurus; OLA ¼ Oryzias latipes; HAT ¼ Hydra attenuata; GFO ¼ Gammarus fossarum; DPU ¼ Daphnia pulex; DPS ¼ Daphnia similis; DMA ¼ Daphnia magna; CSP ¼ Chydorus sphaericus; CDU ¼ Ceriodaphnia dubia. (D,H) Vertebrates: XLA ¼ Xenopus laevis; PPR ¼ Pimephales promelas; OMY ¼ Onchorynchus mykiss; DRE ¼ Danio rerio.
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Figure 5. Log-normal (A) and log-empirical (B) species sensitivity distribution (SSD) and log-normal (C) and log-empirical (D) nano-specific species sensitivity weighted distribution (n-SSWD) for the saltwater compartment. Wm.lg ¼ mean value; wsd.lg ¼ standard deviation value, R2 ¼ multiple R-square coefficient; KSpvalue ¼ Komogorov–Smirnov test value; Prod. ¼ primary producers, Cons. 1 ¼ primary consumers.
KSPvalue < 0.1) and therefore they were judged not reliable. As a consequence, instead of considering the extrapolated HC5 and HC50 values (reported in Supplemental Data, Table S2.2), we decided to use calculated NOEC values for n-TiO2 toxicity for the terrestrial compartment. The whole dataset of calculated NOEC values ranges from 0.0112 mg/kg to 200 mg/kg, with a median value of 100 mg/kg. All the available data are for a few species (6) belonging to the trophic levels of primary producers (1 species), primary consumers (3 species), and secondary consumers (2 species). Most of the data (68%) are for the same species (Porcellio
scaber) and show a high variability in ecotoxicological response. This variability is explained by the fact that the tests conducted on P. scaber used different testing procedures, type of n-TiO2 (e.g., particles size, purity), exposure pathway (e.g., ingestion, dermal contact), exposure time (e.g., chronic, acute test), and pretreatment of the sample. By applying the European Chemicals Agency guidance on information requirements and chemical safety assessment, Chapter R.10 [27] (using the assessment factors given for the terrestrial compartment), it was possible to estimate a predicted no-effect concentration (PNEC) of 0.00112 mg/kg.
Ecological risk assessment of engineered nanomaterials
Taking into consideration the calculated NOEC values for the different trophic levels, it can be observed that the most sensitive trophic level is primary producers, as the minimum, median, and maximum calculated NOEC values (all equal to 0.0112 mg/kg) are lower than the corresponding values for the primary (10 mg/ kg, 200 mg/kg, and 200 mg/kg, respectively) and secondary consumers (0.80 mg/kg, 100 mg/kg, and 100 mg/kg, respectively). Almost the same results are obtained when the geometric mean is applied at the species level to primary producers (0.0112 mg/kg as the minimum, median, and maximum values), primary consumers (10 mg/kg, 105 mg/kg, and 200 mg/kg, respectively), and secondary consumers (0.80 mg/kg, 30 mg/kg, and 100 mg/kg, respectively). When all data are broken down according to taxonomic group, among primary consumers bacteria are more sensitive (10 mg/kg for the minimum, median, and maximum NOEC) than fungi (200 mg/kg for the minimum, median, and maximum NOEC), primary producers are represented only by plants (0.0112 mg/kg for the minimum, median, and maximum NOEC), and secondary consumers are represented only by invertebrates. Invertebrates had the second lowest minimum calculated NOEC value (0.80 mg/kg), but the median (30 mg/kg) and maximum (100 mg/kg) values were higher than those for bacteria. However, because for both plants and bacteria only 1 calculated NOEC is available, no reliable conclusions can be drawn as to which is the most sensitive taxonomic group. To conclude, because of the paucity of available data, it was not possible to draw a robust conclusion on the most sensitive trophic level, taxonomic group, or species. As for the saltwater compartment, this highlights the need to conduct further ecotoxicological tests on different species (belonging to the 3 different trophic levels) for the terrestrial compartment. Validation of environmental quality criteria and risk estimation. Because of the limited data available, validation of the results was not possible: neither Gottschalk et al. [12] nor we in the present study have been able to calculate reliable HC5 values for n-TiO2 in the terrestrial environmental compartment. It was only possible to carry out a risk estimation by comparing the calculated PNEC with the available PECs for soils and sludge-treated soil in Europe proposed by Gottschalk et al. [18] (0.00445 mg/kg and 0.31 mg/kg upper percentile 0.85 estimation, respectively). In terms of the PEC value for soil, comparison with the calculated PNEC (0.00112 mg/L) resulted in a hazard quotient of 3.97, thus suggesting a relevant risk (in contrast to Gottschalk et al. [12], whose results showed no risk occurring in this environmental compartment). In terms of the PEC value for sludge-treated soil, comparison with the same PNEC resulted in an extremely high hazard quotient (276.77), indicating a greater possible risk for this environmental compartment than that given by Gottschalk et al. [12]. However, to draw more robust conclusions and eventually confirm these results, probabilistic distributions of both exposure and effect concentrations should be used. CONCLUSIONS
Although conventional SSD and n-SSWD did not show substantial differences in the environmental quality criteria estimation, the proposed n-SSWD approach appeared to be a valuable tool for ecological risk assessment because it allows better evaluation of endpoint variability and therefore identification of more sensitive trophic levels and taxonomic groups. The inclusion of a weighting coefficient composed of species relevance, trophic level abundance, and data quality weighting criteria allowed us to make use of all the available
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ecotoxicological data and at the same time to account for their relevance (in representing the environmental compartment studied) as well as their reliability. Moreover, the adoption of the method proposed by Card and Magnuson [20] allowed us to assess the quality of nanotoxicity studies by supplementing the Klimisch score [31] with a quantitative estimation of whether the physicochemical characterization provided for the tested nanomaterial is adequate to fully understand and trust the ecotoxicological value to be used in the ecological risk assessment. In our dataset, information was missing in several studies on the shape of the particles, the surface charge, and the surface chemistry (including composition and reactivity), thus significantly reducing the reliability of those data and therefore their weight in the ecological risk assessment. Application of the available ecotoxicological dataset highlighted that n-TiO2 poses marginal risks to organisms exposed to freshwater (mainly primary consumers), and unacceptable risks to organisms exposed to sludge-treated soil (hazard quotient ¼ 19.39), soil (hazard quotient ¼ 276.77), and STP effluent (26% potentially affected fraction). Moreover, it can be expected that use of the complete distributions of the PEC values provided by Sun et al. [26] will result in significantly higher risks for the freshwater environmental compartment. For the saltwater environmental compartment, because PEC values are currently not available, it was not possible to perform a risk estimation. However, to confirm or refine the results we obtained from the present study, this approach should be tested with a more complete dataset, once it is available. Moreover, to further test the performance of the proposed approach, it should be applied to other ENMs (e.g., CuO). The n-SSWD approach demonstrates the benefits derived from integrating a quantitative data quality evaluation (which is crucial given the unreliable datasets resulting from nonstandardized testing and analytical methodologies) into a model for nano-ecological risk assessment. However, substantial expert input is required for its application, which can introduce potential biases, thus increasing uncertainty in the analysis. Therefore we recommend that further work should focus on characterizing the uncertainties stemming from input data, expert judgment, and model performance. SUPPLEMENTAL DATA
Background on SSD and SSWD. Tables S1–S2. Figure S1. (135 KB DOC). Acknowledgment—The present study received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreements 263215 (MAnaging RIsks of Nanomaterials [MARINA]) and 604305 (Sustainable Nanotechnologies [SUN]). The authors acknowledge K. Rasmussen, S. Gottardo, and J. Riego Sintes (Joint Research Centre, Ispra, Italy) for valuable comments and revisions to the first version of the paper. Data availability—Data, associated metadata, and calculation tools are included in the Supplemental Data accompanying the present study. REFERENCES 1. Hristozov DR, Gottardo S, Critto A, Marcomini A. 2012. Risk assessment of engineered nanomaterials: A review of available data and approaches from regulatory perspective. Nanotoxicology 6:880–898. 2. British Standards Institution. 2007. PAS136 terminology for nanomaterials. [cited 2013 February 13]. Available from: http://www. bsiglobal.com
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