Fate and Characterization Factors of Nanoparticles in Seventeen

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Fate and Characterization Factors of Nanoparticles in Seventeen SubContinental Freshwaters: A Case Study on Copper Nanoparticles Yubing Pu, Feng Tang, Pierre-Michel Adam, bertrand laratte, and Rodica E. Ionescu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b06300 • Publication Date (Web): 29 Jul 2016 Downloaded from http://pubs.acs.org on August 1, 2016

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Environmental Science & Technology

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Fate and Characterization Factors of Nanoparticles in

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Seventeen Sub-Continental Freshwaters: A Case Study on

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Copper Nanoparticles

4 5

Yubing Pu,†,‡ Feng Tang,† Pierre-Michel Adam,† Bertrand Laratte,‡,* and Rodica Elena

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Ionescu†

7 8

†

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Delaunay, Université de Technologie de Troyes, UMR CNRS 6281, 12 Rue Marie-Curie

Laboratoire de Nanotechnologie et d’Instrumentation Optique, Institute Charles

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CS 42060, 10004 Cedex Troyes, France.

11

‡

12

Institute Charles Delaunay, Université de Technologie de Troyes, UMR CNRS 6281, 12

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Rue Marie-Curie CS 42060, 10004 Cedex Troyes, France.

Centre de Recherches et d’Etudes Interdisciplinaires sur le Développement Durable,

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Word count:

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Text: 5900 words

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Figures (including captions): 5 small (1500 word equivalents)

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Total: 7400 words

1 ACS Paragon Plus Environment


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ABSTRACT

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The lack of characterization factors (CFs) for engineered nanoparticles (ENPs) hampers

21

the application of life cycle assessment (LCA) methodology in evaluating the potential

22

environmental impacts of nanomaterials. Here, the framework of USEtox model has been

23

selected to solve this problem. Based on colloid science, a fate model for ENPs has been

24

developed to calculate the freshwater fate factor (FF) of ENPs. We also give the

25

recommendations for using the hydrological data from USEtox model. The functionality

26

of our fate model is proved by comparing our computed results with the reported

27

scenarios in North America, Switzerland and Europe. As a case study, a literature survey

28

of the nano-Cu toxicology values has been performed to calculate the effect factor (EF).

29

Seventeen freshwater CFs of nano-Cu are proposed as recommended values for sub-

30

continental regions. Depending on the regions and the properties of the ENPs, the region

31

most likely to be affected by nano-Cu is Africa (CF of 11.11·103 CTUe, comparative

32

toxic units) and the least likely is north Australia (CF of 3.87·103 CTUe). Furthermore,

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from the sensitivity analysis of the fate model, thirteen input parameters (such as depth of

34

freshwater, radius of ENPs) show vastly different degrees of influence on the outcomes.

35

The characterization of suspended particles in freshwater and the dissolution rate of ENPs

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are two significant factors.

37


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TOC/Abstract art

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1. INTRODUCTION

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Engineered nanoparticles (ENPs) can be defined as a type of man-made particles that

42

are manufactured at the nano-scale.1 Due to the excellent properties of ENPs in various

43

fields (e.g. cosmetic, electronic, biomedical, and environmental),2-5 it is estimated that the

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production rate of ENPs will continue to rise in the coming years.6 The wide applications

45

of ENPs increase their potential risks to human and ecosystem.7,

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impacts of ENPs, numerous in vivo and in vitro experiments were conducted on various

47

organisms, and there is a great deal of evidence demonstrating the toxicity of existing

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ENPs.9-12 However, there are still some difficulties in evaluating the real ecosystem

49

impacts of the products which contain ENPs due to the relatively new features of

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nanotechnology.

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methodology,13, 14 is usually the preferred approach to assess the impacts of a product on

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ecosystem.15-17 Each substance contained in a product has a potential impact on the

53

environment, which can be calculated by multiplying the mass of the substance with its

54

characterization factor (CF). As a key parameter for LCA, CF reflects the relative

55

importance of each component in life-cycle inventories.16,

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chemicals can be calculated using the USEtox model, a scientific consensus

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environmental model recommended by many international organizations (such as the

58

European Commission, World Business Council for Sustainable Development etc.).15, 19,

59

20

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such as insufficient toxicity data,21 unknown bioavailability,22 etc. Among them, one

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major challenge is the difficulty in predicting the residence time of ENPs in ecosystem.

62

Compared to the conventional chemical substances, ENPs show many different fate

Life

cycle

assessment

(LCA),

an

8

To investigate the

international

18

standardized

CFs for conventional

Nevertheless, the application of USEtox model on ENPs may meet many challenges


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behaviors in freshwater due to their nano-specific properties (such as low solubility, low

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vapor pressure, high surface reactivity etc.).23 The degradation process of conventional

65

chemicals is irrelevant for ENPs, because the ENPs will undergo transformation

66

processes (such as aggregation, dissolution and surface transformation) altering their

67

original properties.24 In addition, most conventional chemicals in water form a true

68

solution

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thermodynamically unstable suspensions of particles with high surface energy.25

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Therefore, the fate of ENPs in freshwater is hard to predict by using the prior experience

71

for conventional chemicals. It is necessary to propose a nano-specific fate model and then

72

integrate it into the framework of USEtox model to compute the CFs of ENPs.

of a low-molecular-weight

substance,

while

ENPs

in

water

form

73

In recent years, several fate models considering ENPs as colloids have been established

74

to assess the fate of ENPs in ecosystem.23, 25-27 Although they are considered to be useful

75

for predicting average environmental concentrations of ENPs on a regional or national

76

scale, most of them do not consider spatial heterogeneity.28 Despite the differences in

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freshwater parameters between the sub-continental regions (such as Europe, America and

78

Asia etc.),29 to the best of our knowledge, there are no recommendations for the input

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parameters (e.g. depth and volume of freshwater, depth of sediment, etc.) dependent on

80

different regions. Moreover, some of the models take into account several compartments

81

(such as a combination of air, water and soil),26, 27 which makes the model much more

82

complex. Even for the same compartment (such as freshwater), different models usually

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contain various removal processes of ENPs. For example, the SB4N model

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(SimpleBox4nano) reported by Meesters et al.27 did not consider transformation



85

processes (e.g. hetero-aggregation of ENPs with suspended matter) as removal processes.

86

However, Salieri et al. took hetero-aggregation as a removal process in their model.23

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In addition, fate models are approximations and forecasts of the real processes.30 Many

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input parameters would affect the calculated results, such as the density and

89

concentration of suspended particles in the freshwater, the temperature of the freshwater,

90

the size of ENPs, etc.23, 25 However, it is still unclear which parameters are essential to be

91

focused on or discarded. Thus, a sensitivity analysis for studying the influences of main

92

input parameters on output results has been carried out to ensure the model’s

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practicability and avoid over parameterization.28

94

The aim of this study is to propose an approach for computing the freshwater

95

ecotoxicological CFs of ENPs. The framework of the USEtox model was chosen as the

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basis of the present method. A new fate model for ENPs in freshwater has been

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developed based on the previous multimedia fate models,23, 25-27 introducing the pseudo-

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sedimentation process and taking different transport processes of ENPs into account. The

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hydrological data of one default region and sixteen sub-continental regions are

100

recommended for the fate model. Additionally, the fate model is shown to be applicable

101

by comparisons with previous investigations. In this study, copper nanoparticles (nano-

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Cu) have been chosen as a case study due to their relatively high toxicity (compared with

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nano-Ag, nano-Ni, nano-TiO2, etc.)31 and potential release into ecosystem.32 Seventeen

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ranges of freshwater ecotoxicological CFs of nano-Cu corresponding to one default

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region and sixteen sub-continental regions as well as their recommended values are

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proposed. Furthermore, for each studied region, the relative importance of thirteen input

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parameters in the fate model has been evaluated. The sensitivity analysis results may help


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to decide the priorities of the many parameters in calculating the CFs of ENPs for various

109

worldwide regions.

110 111

2. METHODS

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2.1. Characterization Factor

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The USEtox model proposes an ecotoxicological characterization factor (CF) of a

114

substance for an ecosystem as the following equation:20, 33 = × ×

115

(1)

116

where FF (fate factor, unit: day) is the persistence time of a substance in a particular

117

environment (such as freshwater). The XF (exposure factor) represents the bioavailability

118

of a chemical and can be represented by the fraction of the chemical dissolved in

119

freshwater (dimensionless).33 The calculation of XF needs partition coefficients of the

120

substance. However, because ENPs present as the thermodynamically unstable

121

suspensions in freshwater, the partition coefficients are invalid for ENPs.34 Therefore,

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here XF is set to be 1 to avoid significant errors in ENPs fate predictions. Additionally,

123

the EF (effect factor, PAF·m3·kg-1) reflects the change in the potentially affected fraction

124

(PAF) of species because of the change in substance concentration in freshwater. Thus,

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the characterization factor CF has a unit of PAF·m3·day·kg-1. USEtox defines it as

126

comparative toxic units (CTUe) for ecosystem. To adapt the assessment of the ecosystem

127

impacts for ENPs, the developments of the fate model are discussed below.

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2.2. Fate Model Concept



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The fate model proposed in this paper mainly focuses on the nano-specific behaviors of

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ENPs in freshwater compartment on a continental geographic scale. Since the ENPs are

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usually released into the freshwater through the WWTP (wastewater treatment plant),32 it

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was assume that there is no direct ENPs emissions in sediment. The following behavior

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as shown in Figure 1 is considered as the removal processes after the ENPs are released

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into freshwater: (1) dissolution (denoted as

135

); (2) sedimentation (denoted as

,

and

); (3) transport from continental freshwater to other water compartments: advection

136

(denoted as

). In Figure 1, two different mechanisms of sedimentation for ENPs are

137

described: one is individual ENPs sedimentation of free ENPs (

138

transformation of ENPs with suspended (nano)particles followed by pseudo-

139

sedimentation to the sediment (

140

sedimentation does not reflect the real situation but is calculated theoretically. The

141

transformation can be divided into two parts based on the sizes of suspended

142

(nano)particles. The association of ENPs with larger suspended particles (> 450 nm) is

143

referred to as “attachment”, whereas the hetero-aggregation of ENPs and suspended

144

nanoparticles (< 450 nm) is regarded as “aggregation” (Figure 1).27 In reality, the

145

sedimentation process of aggregated (or attached) ENPs is restricted by the slower

146

process of transformation and its corresponding pseudo-sedimentation.

and

) and another is the

). Here, pseudo-sedimentation means that the

147

Theoretically, the aggregation process can be either homo- or hetero- aggregation.

148

Nevertheless, homo-aggregation is disregarded in this model, which is considered a

149

justified simplification because of the more dominant hetero-aggregation between ENPs

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and suspended particles (such as inorganic colloid particles, biogenic debris, algae and

151

bacteria)35 in freshwater.36 Recently, a fate model of metal oxide nanoparticles


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interpreted hetero-aggregation as a removal process.23 However, in this study, the

153

attached and aggregated ENPs were considered as altered species of the same ENPs.

154 155 156

Figure. 1 The behavior and transport of ENPs in freshwater and sediment. The behaviors of ENPs related to sediment are also shown in Figure 1. The

157

resuspension from sediment to freshwater (denoted as

158

process for freshwater and a removal process for sediment. The burial into the deep

159

sediment (denoted as

160

(denoted as

161

behaviors were taken into account when calculating the residence time of ENPs in

162

sentiment.

163

) is considered as a transport

) and horizontal bed load transfer at the surface of the sediment

) are only been considered as removal processes for sediment. All the three

2.3. Fate Factor Calculation



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It was deduced that the overall kinetics of nanomaterials in water-sediment transport

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should be close to the first order.24 Therefore, the first-order kinetics is applied to

166

estimate all the processes considered in this study, which is considered acceptable.23, 27

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: Dissolution rate constant: Dissolution of ENPs is an important property that

168

transforms the nanoparticulate form to the dissolved form (such as ionic form, smaller

169

form and intermediates).24, 37 Usually, once an ENP has been dissolved it is no longer

170

considered as an ENP.27 Therefore, dissolution of ENPs was often considered as a

171

removal process for freshwater.23, 24 Although it was demonstrated that the application of

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first-order dissolution kinetics for the environmental risk assessment was acceptable,27

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modeling dissolution remains highly speculative.24 The main reasons and more details

174

about dissolution of ENPs could be found in Supporting Information: "Dissolution"

175

section. In practice, as an acceptable simplification, the dissolution rate constant kdiss (s-1)

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of ENPs is assumed to be 0-10-5 s-1 for different ENPs.23, 24

177

,

and

: Sedimentation rate constant: The total rate constant of the

178

sedimentation (ksed) process is calculated as the sum of that for individual ENPs (k1,

179

aggregated ENPs (kagg-sed,

) and attached ENPs (katt-sed,

),

).

180

=

181

As mentioned in Section 2.2, the sedimentation rates of attached ENPs and aggregated

182

ENPs respectively depend on the slower process between transformation and its

183

corresponding pseudo-sedimentation process.

184

= minimum , ;

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= minimum , 10 ACS Paragon Plus Environment

(2)

(3)

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where k2 represents the attachment rate constant; k3 represents the aggregation rate

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constant; k4 and k5 are the pseudo-sedimentation rate constants for aggregated ENPs and

188

attached ENPs respectively. ENPs attached or aggregated to suspended particles are

189

considered as the altered species.

190 191

192

) has been established to quantify the ENPs Fate matrix: A 2 × 2 fate matrix ( transport between the freshwater (w) and sediment (sed) compartments.38 , = ,

,

,

(4)

193

The FFw,w and FFsed,sed respectively describe the residence time (day) of ENPs in

194

freshwater and sediment. The off-diagonal elements represent the transport time (day) of

195

ENPs from water to sediment (FFw,sed) and from sediment to water (FFsed,w). The

196

equals the negative inverse of the rate coefficient matrix ():23, 38

197

− = − = − , ,

,

− ,

(5)

198

Freshwater fate factor: Only the calculation of ENPs removal rate constant for

199

freshwater (kw,w) is shown here, while others are described in Supporting Information.

200

Thus, if not specified, the FF refers to FFw,w. In this study, the total removal rate constant

201

of ENPs in freshwater (kw,w) is expressed as a combination of the rate constants for three

202

removal processes for freshwater compartment: dissolution (kdiss), total sedimentation

203

(ksed) and advection (kadv) as equation 6:

204

, = " #

205

The detailed calculation processes of all rate constants can be found in the Supporting

206

Information (from Equation S1 to S22). The behaviors of colloids in freshwater can be 11 ACS Paragon Plus Environment

(6)


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described based on a combination of fate model and the related input parameters.26, 27

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Even though it is not possible to use the original USEtox fate model to model the fate of

209

ENPs, the hydrological data (e.g. the average temperature and depth of freshwater in

210

Europe etc.) is objective and still usable when considering the situation on a continental

211

or global scale.23 In order to make the fate model more flexible, a group of general input

212

parameters has been recommended as default values based on the database of USEtox

213

model (Table S1 and DEFAULT in Table S2).20 Where USEtox data was not available,

214

parameter values derived from other fate models were preferred.20, 22, 23, 38 Additionally,

215

due to the significant differences of freshwater parameters between sub-continental

216

regions (such as Africa, America, Asia and Europe, etc.), five regional-specific

217

parameters from the USEtox model20, 33 for sixteen different sub-continental regions were

218

also recommended and listed in Table S2. In the calculation of ranges of FFs, these

219

regional-specific parameters were fixed to the recommended values, while other variables

220

followed the same ranges as in sensitivity analysis.

221

2.4. Fate Model Evaluation

222

Ideally, testing the robustness and predictability of the fate model should be performed

223

by comparing the real concentrations of ENPs in freshwater and the predicted

224

environmental concentrations (PECs) calculated by the fate model. However, the real

225

concentrations of ENPs in the ecosystem are rarely reported28 because the detection and

226

quantification of ENPs in complex natural media are still in their infancy.39, 40 Thus, the

227

functionality of fate model was usually tested by comparing the PECs obtained by the

228

proposed model and by the previous reports. For instance, Meesters et al. reworked the


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case of TiO2 in Switzerland to demonstrate their model’s capability for environmental

230

exposure estimations of ENPs.27

231

Although the present fate model does not aim for calculating the PECs of ENPs, it is

232

still capable to do that with emission information of ENPs. Since it was assumed that the

233

freshwater is the only compartment directly receiving the ENPs emissions, the

234

resuspended ENPs are all initially from the freshwater but not sediment. The time-

235

dependent mass concentrations C (kg·m-3) of ENPs can be expressed as the total mass M

236

(kg) in freshwater of volume V (m3) at time t (s) with constant emission E (kg·s-1) as

237

equation 7.27 =

238

$ %

&

= %×'

(,(

1 − * '(,( ×+

(7)

239

Here, it should be noted all the PECs of ENPs are highly dependent on the emission

240

values. Thus, in this study, the PECs of five ENPs (nano-Cu, nano-Ag, nano-TiO2, nano-

241

ZnO and Fullerenes) were calculated using our fate model within the same emission

242

scenarios as previous studies (one in North America, two in Switzerland and two in

243

Europe).27,

244

certain regions based on probabilistic material flow analysis of nano-containning

245

products such as some cosmetics, textiles, paints, etc. The emission values as well as their

246

source and quality were described in Table S3 and its footnote. Then, the total removal

247

rates of ENPs were obtained by present fate model. To better assess the outputs of the

248

present model, the same input values as previous studies (such as radius of ENPs and

249

larger suspended particles in Switzerland, etc.) were also kept to the greatest extent. In

250

the case of no data of relevance reported by the previous studies, the recommended data

40-43

These studies predicted the annual emissions of the related ENPs in



251

(North America: Region W10; Switzerland and Europe: Region W13) in Table S2 were

252

used. Table S4 summarizes the dissolution data and some specific parameters related to

253

North America, Switzerland and Europe. Finally, the outcomes were compared with the

254

previously reported PECs.

255

2.5. Sensitivity Analysis

256

It is noteworthy that some input parameter values vary in different literature resources

257

due to the use of diverse methods, instruments, research objects, etc. Thus, a sensitivity

258

analysis was performed by MATLAB44 to assess the influence of variations of parameter

259

values on the fate factors of ENPs. A total of thirteen input variables were chosen for

260

performing sensitivity analysis. It was assumed that these parameters were independent

261

of each other. The ranges of these input variables were obtained from the USEtox

262

model20 and other literature,25, 27, 35 and listed in Table S1. Other input parameters in our

263

fate model are considered constants.

264

A sensitivity analysis (SA) can be performed by varying each parameter within its range

265

while holding the others constant,45 which was labeled as “Single Parameter SA” in this

266

study. Alternatively, vary each of the model parameters at a time.46 Since it was difficult

267

to vary thirteen parameters within their ranges at a time, the studied parameters were

268

divided into four groups according to their natures. The sensitivity analysis was

269

performed by varying the parameters within minimum and maximum values in one group

270

while holding those in other groups as constants. It was labeled as “Group Parameter SA”.

271

Both methods were used and the detailed method for “Group Parameter SA” was

272

described in Supporting Information (From equation S23).

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2.6. Effect Factor Calculation 14 ACS Paragon Plus Environment

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In USEtox, the effect factor (EF, PAF·m3·kg-1) of a chemical is calculated by the following equation:33 =

276

,.

(8)

./,

277

where HC50 (kg·m-3) represents the hazardous concentration of a chemical at which 50%

278

of the species (in aquatic ecosystem) are exposed to a concentration above their EC50.

279

The HC50 is calculated based on geometric means of single species EC50 data, and the

280

EC50 is the effective concentration of a substance at which 50% of a population (e.g. fish)

281

displays a phenomenon (e.g. immobility, reproduction). When the phenomenon is

282

mortality, EC50 becomes a median lethal concentration (LC50).6,

283

chronic values and the values with endpoint of mortality have priority. Where chronic

284

EC50 values are unavailable, an acute-to-chronic ratio (ACR) of 10 (crustaceans) and 20

285

(fishes) is suggested by USEtox.20, 47 The detailed calculations of HC50 and EFs can be

286

found in equation S24, S25 and Table S6.

21

In USEtox, the

287 288

3. RESULTS AND DISCUSSION

289

3.1. Evaluation of Fate Model

290

The PECs of ENPs in North America and Switzerland from this study and the previous

291

studies are presented in Figure 2 (the numeric values were listed in Table S5). The

292

comparison of the PECs in Europe was shown in Figure S2. As described in Section 2.4,

293

in present study, all the calculations of PECs of ENPs were based on the same

294

background as each scenario (such as ENPs emissions, water volumes, etc.). The

295

concentration of nano-Cu in freshwater was rarely reported. However, Keller and



296

Lazareva estimated the concentrations of nano-Cu as well as other ENPs (such as nano-

297

Ag, nano-TiO2 and nano-ZnO) in San Francisco (SF) Bay wastewater treatment plant

298

(WWTP) effluent.41 In addition, Sun et al. reported the PECs of several ENPs (such as

299

nano-Ag, nano-TiO2, nano-ZnO and Fullerenes) in Switzerland and Europe WWTP

300

effluent.40, 43 Therefore, except for the PECs in freshwater, those in WWTP effluent were

301

also used for comparisons in this study.

302 303

Figure 2. Predicted Environmental Concentrations (PECs, µg·L-1) of five ENPs (nano-Cu,

304

nano-Ag, nano-TiO2, nano-ZnO and Fullerenes) based on present fate model and

305

previous studies. (A) PECs in San Francisco Bay in North America estimated from the

306

study of Keller and Lazareva.41 (B) PECs in Switzerland extracted from two studies:

307

Mueller and Nowack,42 Meesters et al. (bar with *).27 (C) PECs in Switzerland based on


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the predicted emission for the year 2012 by Sun et al.40 WWTP = wastewater treatment

309

plant.

310

In Figure 2A and C, the PECs of ENPs in WWTP effluent calculated by present model

311

are about 1~3 orders of magnitude smaller than the PECs in SF Bay and Switzerland

312

WWTP effluent. The differences are mainly due to the following reason. Keller and

313

Lazareva did not consider the fate of ENPs in water,41 while Sun et al. assumed that the

314

retention time of ENPs in water was 40 days.40 Based on the present fate model, the

315

average retention time of ENPs for SF Bay and Switzerland WWTP effluents were about

316

1.98 and 2.13 days, respectively. It indicates that the sedimentation rates of ENPs in

317

water are much faster than the previous studies assumed. In other words, neglecting the

318

fate of ENPs in water would probably overestimate the PECs of ENPs, in particular for

319

the quick ENPs removal regions. Additionally, the PECs of four ENPs in freshwater of

320

North America were calculated by our fate model and presented in Figure 2A. For the

321

case of nano-Cu, the concentration is predicted to be not higher than 2.04·10-6 µg·L-1 in

322

North America.

323

The PECs of ENPs in Switzerland freshwater reported by Muller and Nowack as well as

324

by Sun et al. are 1~2 orders of magnitude larger than by present model (Figure 2B and C).

325

The reason is as similar as WWTP effluent situation. In addition, as shown in Figure 2B,

326

the PECs of nano-TiO2 provided by present study and by Meesters et al. are within one

327

order of magnitude. Both of the two approaches considered the fate of ENPs by

328

calculating the nanao-specific removal rates. However, dissolution rate of nano-TiO2 was

329

set to be 0 in the study of Meesters et al., while it was a range of 0-10-5 s-1 in the present

330

study. Moreover, several input values and calculation of certain removal processes for the 17 ACS Paragon Plus Environment


331

two fate models were different, such as radius of larger suspended nanoparticles,

332

calculation of the sedimentation rates, etc. This explains why the two approaches lead a

333

difference but not notable in PECs of nano-TiO2.

334

The PECs obtained in this study are time-dependent mass concentrations (equation 6)

335

after 1 year of emission,27 while others are steady state concentrations achieved by

336

directly dividing the emission mass by the volume of freshwater. However, the steady

337

state of ENPs in North America / Switzerland freshwater is reached after about

338

~1.98/~2.13 days. Therefore, even though different concentration calculating methods of

339

PECs were used, it did not have influences on the PECs comparison results.

340

3.2. Effect Factor of nano-Cu

341

When ENPs are released into water, several forms (such as ionic form, smaller form and

342

intermediates) may co-exist because of the dissolution.37, 48 Numerous experiments have

343

demonstrated the differential toxicity between nano particulates and their dissolved ions.

344

For many nanoparticles (such as nano-Ag, nano-ZnO, nano-Ni), the corresponding

345

dissolved metal is more toxic than the nano form.31, 49 For nano-Cu, it was demonstrated

346

that the Cu2+ (in copper nitrate solution) was more toxic than nano-Cu and the smaller

347

nano-Cu was more toxic than the larger one.50 In this study, the toxicity of dissolved

348

ENPs was considered as a part of the ENPs’ total toxicity. (Detailed reasons can be found

349

in Supporting Information: "Dissolution" section).

350

It was recommended that the HC50 calculation should be based on data reflecting the

351

entire ecosystem.51 In practice, the data from laboratory tests with algae (primary

352

producers), crustaceans (primary consumers) and fish (secondary consumers), which

353

represent the three trophic levels of the ecosystem, are preferred.20, 51 However, the EC50 18 ACS Paragon Plus Environment

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354

or LC50 (see Section 2.6) of nano-Cu for algae was rarely reported. Since nano-Cu may

355

oxidized as nano-CuO in freshwater,37 the toxicity values of nano-CuO on algae were

356

applied as preliminary approximation of the toxicity data on algae for nano-Cu. In the

357

case of toxicity data reported on CuO basis, they were converted to Cu basis, because the

358

masses of Cu are the same before and after oxidation. In addition, eleven toxicity values

359

of nano-Cu were collected (two studies for crustaceans and nine for fish) and listed in

360

Table S6. For the same species, the differences between LC50 (or EC50) values are less

361

than one order of magnitude. In USEtox model, the HC50 can be calculated based on the

362

geometric mean of the toxicity values on either species level (GM-SL) or trophic level

363

(GM-TL).51,

364

computing the geometric mean of the toxicity values related to each trophic level (such as

365

fish) and then calculating the geometric mean of the three trophic levels. In this study, the

366

EFs computed based on GM-SL and GM-TL are 2326.31 and 5185.17 PAF·m3·kg-1

367

respectively. The approach relying on the trophic level has been claimed as better

368

practice to calculate HC50 and further FF. It avoids the disadvantages caused by the

369

unequal distribution of EC50 in three trophic levels.52 Thus, the EF value of 5185.17

370

PAF·m3·kg-1 for nano-Cu was used to calculate the freshwater characterization factors of

371

nano-Cu.

52

The HC50 calculation of GM-TL was conducted by respectively

372

3.3. Fate Factors of nano-Cu

373

Theoretically, the same type ENPs in the background freshwater can have influences on

374

the FFs of ENPs by accelerating the homo-aggregation process between ENPs. However,

375

as mentioned in Section 2.2, the proposed fate model disregarded the homo-aggregation

376

because the concentration of suspended particles is several orders of magnitude higher 19 ACS Paragon Plus Environment


377

than that of ENPs in freshwater (e.g. 1010 suspended particles m-3 versus 108 nano-TiO2

378

particles m-3).25, 32 It is unlikely that the same type ENPs collide frequently with each

379

other. Therefore, the initial release concentration of ENPs in freshwater was not taken

380

into account in present fate model. Nevertheless, due to the potential increases of ENPs

381

concentrations in ecosystem,43 the consideration of ENPs background concentrations

382

would be recommended once ENPs have comparable amount with suspended particles.

383

The nano-Cu density of 8940 kg·m-3 was used to calculate the FFs of nano-Cu. For

384

each region, the difference between the minimum and maximum possible FFs reflects the

385

range of FFs of nano-Cu. Figure 3 shows the recommended values and the ranges of fate

386

factors of nano-Cu in one default region and sixteen sub-continental regions. When all

387

the input parameters have the biggest negative contribution to the removal processes (e.g.

388

kdiss = 0), the maximum possible FFs can be reached (from 34.0 days for W8 region up to

389

2844.3 days for W6 region). In contrast, when all the input parameters have the biggest

390

positive contribution to the removal processes (e.g. kdiss = 10-5 s-1), the minimum possible

391

FFs range from ~10-3 to ~10-1 day. It means that nano-Cu will be removed very quickly

392

or even disappear instantly from the freshwater. The FF ranges are so large that,

393

sometimes, they may not properly predict the real situation due to the large ranges of

394

input parameters. Here, the ranges of FFs just represent there are the possibilities to reach

395

such values under extreme conditions.


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396 397

Figure 3. The ranges and the recommended values of fate factors for nano-Cu in one

398

default region and sixteen sub-continental regions. (The further details about the

399

countries within each region can be found in the study of Shaked.53)

400

In the following calculation of CFs, the recommended FFs (shown in Figure 3) were

401

used. A low persistence of nano-Cu in the freshwater (~ 0.75 day) is observable in

402

Australia regions (W3 and W4), while high FF values (~2.14 day) occur in Africa (W5

403

and W6). The removal rate of nano-Cu from freshwater for W3 and W5 regions is shown

404

as an example in Figure S3 to explain the differences of FFs. Those for other regions can

405

be found in Figure S4. The transport rate of nano-Cu from freshwater to sediment (kw,sed)

406

for W3 region is more than 15 times faster than that for W3 region (2.07·10-5 versus



407

1.35·10-6 s-1), while the inverse transport rate (ksed,w) for W3 region is less than half of

408

that for W5 region (3.32·10-9 versus 7.80·10-9 s-1). It’s the apparent cause of longer

409

residence time in freshwater of W5 region than W3 region.

410

Usually, fate model and the hydrological parameters are combined together to predict

411

the behaviors of ENPs in freshwater. In this study, a fate model considering all the

412

possible behaviors of ENPs in freshwater has been proposed. That is to say, no matter in

413

which freshwater, the ENPs must follow all or parts of the behaviors described in this fate

414

model. Then, the input hydrological data becomes the only difference between different

415

regions. Therefore, the sub-continental differences in freshwater parameters were

416

regarded as the primary contributing factors to the different FF values. In the example

417

shown in Figure S3, the sedimentation process of nano-Cu in W3 region is ~15 times

418

faster than in W5 region because the depth of freshwater in W3 region is about 15 times

419

smaller than that in W5 region (3 versus 46 m). Conversely, the resuspension process in

420

W3 region is ~2 times slower than W5 region because the resuspension rate in W3 region

421

is ~2 times smaller than in W5 region (9.95·10-11 versus 2.34·10-10 m·s-1).

422

3.4. Characterization Factors of nano-Cu

423

Based on the XF as well as the calculated EF and FFs of nano-Cu, the recommended

424

global nano-Cu CFs were obtained and are depicted in Figure 4. In addition, the

425

minimum (best scenario) and maximum (worst scenario) CFs of nano-Cu for each region

426

are also listed in Table S7. Each region has its own recommended CF value of nano-Cu.

427

These CFs values can be used in the future LCA, when assessing products containing

428

nano-Cu. Although this map lacks some precision, it can still show a general view of

429

nano-Cu CFs in the world. In the future, we propose to develop a more detailed map. The 22 ACS Paragon Plus Environment

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Page 23 of 36


430

regions which are geographically close to one another have similar CFs of nano-Cu. The

431

differences of CFs between each region are expressed by different colors and the deeper

432

the color on the map the larger is the CF of nano-Cu for the related region. Moreover, the

433

color of this map can also represent the length of persistence time of nano-Cu in the

434

freshwater.

435 436

Figure 4. World map of the nano-Cu characterization factors (CFs), with histograms

437

indicating the variation between the numerical values (Units: 103 CTUe) of seventeen

438

worldwide regions (one default region and sixteen sub-continental regions). The source

439

of the underlying map was from Mapchart website.54

440

Figure 4 indicates that the potential influences of nano-Cu on organisms may vary from

441

one region to another. This is mainly because of the big differences in FFs between each

442

sub-continental region. The region most likely to be affected by nano-Cu is Africa with a

443

maximum CF of 11.11·103 CTUe. In contrast, nano-Cu may have less potential impact in 23 ACS Paragon Plus Environment


444

north Australia with a minimum CF of approximate 3.87·103 CTUe. In the case of an

445

unknown region, the CF value of 5.96·103 CTUe is proposed for the DEFAULT region.

446

This value is a small one among all the studied regions, which indicates the impacts of

447

nano-Cu to the ecosystem would probably be underestimated for the unknown regions.

448

Additionally, it should be noted that the CFs proposed in this study are classified as

449

“indicative” by USEtox model because the relatively high uncertainty in addressing fate,

450

exposure and effects of metal or inorganic chemical.20

451

3.5. Sensitivity Analysis of Fate Model

452

The CFs and FFs of nano-Cu have large variation ranges because of the uncertain input

453

parameters of the fate model. Also the PECs calculated by our fate model have many

454

uncertainties, which is similar to other fate predictions.27 There are three main reasons.

455

Firstly, it is difficult to know the exact emission rates of the ENPs in a given region.42, 55

456

Secondly, there is an absence of knowledge about how the physicochemical properties of

457

ENPs affect the reactions (e.g. attachment, aggregation behavior) with the environment.

458

Thirdly, the many uncertainties of the complex natural ecosystem may result in a large

459

range of input parameter values, which obviously affect the outcomes. Fortunately, the

460

first difficulty should not cause a problem for the calculation of CF in this study, because

461

the outcome of the fate model is not the PECs, but the FF, which has no relationship with

462

the emission rates of ENPs. However, the other two problems still exist and are difficult

463

to solve directly and completely. In order to ameliorate this situation, a sensitivity

464

analysis was performed. By identifying the relative importance of the variables in our

465

model, the more sensitive parameters may then receive more attention in the future, while

466

the parameters having low influence on the outcome would not be so urgent. 24 ACS Paragon Plus Environment

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467

It is well known that the more accurate the input data is, the closer to the reality the

468

predicted FF becomes. Therefore, when calculating the freshwater FFs of ENPs, the best

469

choice is to use the local input data related to the real situation. However, if time, budget,

470

or other resources are limited, which parameter should be paid more attention to? The

471

relative importance of the input parameters in Figure 5 can give some indications (the

472

numeric values are listed in Table S8). In addition, the results from “Single Parameter SA”

473

are described in Figure S5 with the values listed in Table S9. These two methods

474

produced similar results. The relative importance of the thirteen studied parameters

475

presents big differences. For all the regions, the radius of larger suspended particles (rLSP)

476

has the first or second highest relative importance, while the number concentration of

477

suspended nanoparticles (C3) and Twater have no influence on the FFs. It indicates that the

478

rLSP needs to be paid more attention. In contrast, even though use the recommended

479

values of C3 and Twater by this study, rather than the values obtained by actual detections,

480

the calculated FFs should not have big differences.



481 482

Figure. 5 The relative importance of eleven parameters in four groups by method “Group

483

Parameter SA”. Another three parameters (i.e. Depthsed, C3 and Twater), with relative

484

importance approximating to 0, are not shown in the histogram. SPM is the abbreviation

485

of suspended particulate matter. A brief description of the sub-continental regions is

486

listed in Figure 3 and further details can be found in the study by Shaked.53

487

The same parameter may have different relative importance in different regions. The

488

dissolution rate (kdiss) of ENPs in some regions (e.g. W9 and W10), especially in W5 and 26 ACS Paragon Plus Environment

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Page 27 of 36


489

W6 regions, has much higher relative importance than other parameters. However, it has

490

much lower relative importance in DEFAUTL, W3, W4 as well as W14 regions.

491

Interestingly, the former four regions are just the most likely to be affected regions, while

492

the latter four regions are the least likely (see Figure 4). The reason may be that the

493

dissolution process becomes more influential when ENPs stay longer in the freshwater.

494

In addition, the sensitivity analysis indicates that there are big differences of importance

495

between the parameters even for those in the same group. Each group has not only

496

significant parameters but also less important parameters. Thus, we recommend focusing

497

on the more important parameters in each group rather than all the parameters in one

498

group.

499

Due to the limitation of the current theory or characterization method, there are some

500

assumptions in this study, such as neglecting the sedimentation of dissolved ENPs,

501

disregarding the homo-aggregation process, fixing the values of attachment and

502

aggregation efficiencies, etc. These assumptions may bring uncertainties to fate factors

503

(FFs) of ENPs. In addition, the significant impact of input parameters on FF calculations

504

was observed from the sensitivity analysis. As an important input parameter for fate

505

model, the radius of larger suspended particles in freshwater was treated as one value in

506

present fate model. However, in the real situation, the suspended particles have a size

507

distribution,25, 35 which should be considered in future studies. The exposure factor (XF)

508

was provisionally set as 1 due to the unavailable partition coefficients of ENPs.

509

Nevertheless, as an important factor in CF calculation, XF may have a big influence on

510

the final CF values. Therefore, further investigations of XF are needed. The effect factor

511

(EF) can also be influenced by many factors in complex ecosystem such as natural 27 ACS Paragon Plus Environment


512

organic matter, heavy metal ions etc.56 Furthermore, the calculated EF of nano-Cu was

513

partly based on toxicity values of nano-CuO. It can be updated once the toxicity values of

514

nano-Cu on algae are available. All the above limitations may affect the accuracy of our

515

approach and the absolute values of the final proposed CFs of nano-Cu. However, despite

516

having defects, the method proposed in this study for calculating the CFs of ENPs as well

517

as the sensitivity analysis is promising and effective. As a case study, the recommended

518

CFs for nano-Cu could also be used in the future when evaluating the ecosystem impacts

519

of products containing nano-Cu.

520


Page 28 of 36

Page 29 of 36


521

ASSOCIATED CONTENT

522

Supporting Information

523

Additional information on dissolution, fate model equations, recommendations for input

524

parameters, fate model evaluation, toxicity data for EF calculation, ranges of CFs and

525

sensitivity analysis. This material is available free of charge via the Internet at

526

http://pubs.acs.org.

527

AUTHOR INFORMATION

528

Corresponding Author

529

* Bertrand Laratte

530

Phone: (33) 03 51 59 11 31; Fax: (33) 03 25 71 76 98

531

E-mail: [email protected]

532

Notes

533

The authors declare no competing financial interest.

534

ACKNOWLEDGMENTS

535

Yubing PU kindly thanks the China Scholarship Council (CSC) for his PhD scholarship

536

in France.



537 538 539

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