Natasha
Stoudmann
,
Bernd
Nowack
and
Claudia
Som
*
Empa – Swiss Federal Laboratories for Materials Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, CH-9014 St. Gallen, Switzerland. E-mail: claudia.som@empa.ch
First published on 15th July 2019
The many appealing properties of nanocellulose have led to increasing interest in the material from research and industry over the past years, with the material showing potential for both industrial and consumer goods. This will unavoidably lead to increasing release of nanocellulose into the environment. Although nanocellulose is largely regarded as non-toxic, knowledge gaps surrounding its impacts on the environment and human health still exist and data remains scarce. This study aimed to quantitatively assess the environmental risk of nanocellulose by characterizing both environmental exposure and hazard. Firstly, a probabilistic species sensitivity distribution (PSSD) was developed to assess the hazard by calculating the predicted no effect concentration (PNEC) of the surface water compartment, resulting in a PNEC of 7.8 mg l−1 (mean value of the distribution). Secondly, the dynamic probabilistic material flow assessment (DPMFA) method was employed to assess the exposure by quantifying the predicted environmental concentration (PEC), using the European Union as a system boundary, and 2000 to 2025 as timeframe. This resulted in a PEC in surface water of 0.23 μg l−1 in 2015 and 2.37 μg l−1 in 2025 (mean values). The PEC and PNEC distributions allowed for the calculation of the risk characterization ratio (RCR). Results show an RCR of 6.9 × 10−5 in 2015, and 7.1 × 10−4 in 2025, implying that under the chosen assumptions there is no present or future environmental risk surrounding nanocellulose within the surface water compartment, even assuming a compound annual growth rate of 19% for nanocellulose production in upcoming years.
Environmental significanceIncreasing nanomaterials in commercial products necessarily leads to increasing concentrations of these materials within the environment. Quantifying the risk this may pose at an early stage of material development is therefore useful. Nanocellulose is still in early stages of commercialization, but production volumes are expected to grow significantly in the upcoming years. This study aimed to quantify the environmental risk surrounding this novel material while accounting for expected future production growth. Results from this work contribute to the growing body of environmental risk assessments of nanomaterials, and open the way for the safe commercialization of nanocellulose-containing products. |
Three categories of NC exist: cellulose nanocrystals (CNC), cellulose nanofibers (CNF), and bacterial nanocellulose (BNC). The possibility of controlling its biofabrication make BNC particularly appealing to the biomedical industry.6 In addition to being produced in different ways, these three types also vary in their physico-chemical properties, from size to crystallinity.7 Most of the NC being developed for commercial purposes is in the form of CNF, with CNC patents largely being owned by universities and public institutions.8,9 Currently, many NC-based applications are at the pilot scale or going through industry trials, with a first few applications having already entered the market.10 It is expected that many applications will reach commercialization within the next 5–10 years.11 Forecasts regarding NC production volumes vary substantially, but are overall optimistic.12 The anticipated increase in NC production and use will unavoidably lead to increased release into the environment and to increased human exposure to the material at various product life stages.
At the nanoscale, the behavior and physicochemical properties of materials change, giving them new qualities, but also having the potential to negatively impact human health and the environment.13–16 Although NC is largely considered non-toxic, as is its bulk form, ecotoxicity studies remain scarce and knowledge gaps remain. A number of reviews surrounding the material's characterization and toxicity have been published in recent years.15,17–19 Their overarching conclusions are that uncertainties are still present, preventing a straightforward judgement to be made regarding health and safety issues surrounding the material. Shatkin and Kim20 undertook a life cycle risk assessment of NC by identifying exposure scenarios and evaluating its toxicity. The resulting health and safety roadmap allows for a qualitative overview of the material, with the authors highlighting the existence of remaining data gaps and research needs. Endes et al.15 reviewed the current knowledge of the impacts of NC on human and environmental health. The authors identified wide divergences between study findings, and pointed out the current lack of chronic, low dose, and repeated exposure studies.
There are currently no methods allowing to directly measure concentrations of engineered nanomaterials (ENMs) in the environment. Using a modelling approach is therefore necessary.21 Such a top-down procedure allows environmental concentrations to be estimated based on demand volumes and other input data.22–24 This approach has been used to assess the environmental risk of a number of ENMs, including nano-SiO2, nano-Ag, nano-TiO2, nano-ZnO, carbon nanotubes (CNTs), fullerenes, nano-Au, and nano-CeO2.25–29 Our study follows a similar structure as that of Wang et al.28 who looked at the environmental release and risks of nano-SiO2 through the use of probabilistic material flow and environmental risk assessment models. By using such an approach, this study aims to contribute towards filling some of the remaining gaps surrounding NC by quantitatively assessing the environmental risk of the material. Due to limited information and data availability, we consider all forms of NC, without differentiating between CNC, CNF, and BNC.
The probabilistic nature of the MFA allows the uncertainties surrounding input parameters (demand volume, allocation to product categories and transfer coefficients) to be taken into account by treating these inputs as probability distributions. Two types of distributions were applied to the various data sources, namely triangular and trapezoidal, as explained in detail in Sun et al.33 Following the studies by Sun et al. and Gottschalk et al.,33,34 we used two degree of belief (DoB) values to evaluate the reliability of data. These were 80% for high reliability data, from peer-reviewed studies and market reports, and 20% for data from presentations, reports, and other sources giving little methodological detail. These DoB translated into different sized samples of the corresponding parameter in the Monte Carlo simulation, used to account for the uncertainties through a stochastic approach.33,35
The model is made up of various technical and environmental compartments, from and into which NC flows. The four environmental compartments of the model are ‘air’, ‘surface water’, ‘subsurface water’ and ‘soil’ (divided into ‘natural and urban soils’ and ‘sludge treated soils’). The seven technical compartments are ‘production’, ‘manufacturing’, and ‘consumption’ (‘PCM’), ‘sewage treatment plants’ (‘STP’), ‘waste incineration plants’ (‘WIP’), ‘landfills’, ‘reprocessing’, ‘elimination’, and ‘export’. Within our model, ‘export’ represents export of nanocellulose-containing waste from Europe. We do not consider product imports or exports, as we assume that the use phase of all products takes place within the system boundary. The ‘reprocessing’ compartment represents recycling and further conversion of the products and material within the system, and ‘elimination’ represents the destruction and loss of the material through combustion while in the WIP.
Three sources were used as NC production volume input in the model. These varied significantly in terms of forecasted volumes, but as there is high uncertainty regarding current and future production, all three were considered, with various DoBs being attributed to each. Values from Future Markets9 were given a DoB of 80%. The two sources from the report by Miller,36 which presents NC market forecasts from various companies and institutes, were given a DoB of 20%, as the report does not detail how these volumes were reached. We only used the forecasts in the report by Miller36 which specified a time horizon, not just potential volume. When the volume of only a single year was given, we extrapolated past and future volumes by assuming a compound annual growth rate (CAGR) of 19% between 2015 and 2025.37 We did not use the forecast made by RISI for 2025,12 as the estimated 450000 tons per year of global production was deemed unrealistic by expert judgement. Although the study by Cowie et al.38 estimates global and United States NC production volumes through market projections, their study was focused on volume potential, without considering a specific timeline or year in which these projected volumes might be reached. We therefore did not consider the volumes discussed in their study, although did consider the shares of NC attributed to various product categories that were identified. Furthermore, although data regarding the capacity of NC production plants were available in various studies, they were considered unreliable as estimations of actual production volumes, as these facilities are not currently producing to maximum capacity.
European volumes were scaled from global volumes based on Europe's current and projected NC market share. According to Future Markets,9 Europe currently represents 33% of the global NC market. This share is projected to increase to 34% by 2027, and we assume that this would already be the case by 2025. No information regarding past NC production volumes were found. We therefore assumed demand to be correlated with trends in patents and scientific publications. Accordingly, production was set to 0 tons in the year 2000 (earliest considered year in this study), following a linear growth between 2000 and 2015,8,39 with a CAGR of 21.3%.
How much and where an ENM is released will depend on the product in which it is incorporated and its matrix, rather than on the ENM itself.40–42 The attribution of nanocellulose to product categories is therefore a crucial step in modelling its later release. Nanocellulose is still in early stages of commercialization, with the majority produced going into research and development (R&D).2,9 To account for this, we treated product category shares dynamically, as the share of nanocellulose going into R&D will slowly decrease relatively over time. All other product categories and their shares were developed based on a literature search of nanocellulose applications, where the maximal, minimal and mean shares were used to develop the distribution for each product categories.9,36,38 Assuming a linear growth, by 2025, 58% of produced nanocellulose will be used for R&D purposes, down from 75% in 2015. The coefficient of variation of the R&D product category was set to 50%, to account for the high uncertainty surrounding this value.22
Information regarding release of nanocellulose to various environmental and technical compartments, required to determine transfer coefficients, was extremely limited. We therefore used transfer coefficients from studies that looked at the release of ENMs with similar properties and having similar product categories as those identified for NC, for example CNT and nano-SiO2 (e.g.,ref. 29, 31 and 43). The same was the case for the release schedule from product categories, as no studies regarding the release of NC from products or NC-containing materials were identified in the literature.
Allocations of product categories to solid waste categories were based on the study by Adam and Nowack.44 These waste categories include mixed municipal solid waste (MMSW), packaging waste, waste electrical and electronic equipment (WEEE), textile waste, and construction and demolition waste (CDW). Four additional categories were incorporated into the model to fit NC product category requirements, namely automotive waste, aerospace and aviation waste, medical waste, and paper waste. The fate of these waste categories, either ending up in WIP, landfilling, WWTP, reprocessing, or export, was determined following the methodology in Adam and Nowack,44 using waste collection and treatment data from reports and the Eurostat database.45 As there is no NC-specific sewage treatment plant (STP) removal efficiency study available to our knowledge, we used the data from Sun et al.33 for CNT, where the results from STP removal studies were treated with various DoBs, depending on the size of the studies (full-scale STP, pilot STP, or laboratory experiments).
Modelling of the stocks and flows of NC through its life cycle allowed us to quantify the flows into the environment. The surface water compartment was used for the risk assessment undertaken in this study, therefore its predicted environmental concentration (PEC) was calculated. To determine the PEC of the surface water compartment required for the risk characterization ratio calculation, the mass of NC of a specific year within an environmental compartment is divided by the EU volume of that compartment, as described in detail by Sun et al.33 A PEC distribution was developed by dividing each value making up the mass distribution within the surface water compartment by the compartment volume.
We applied a probabilistic SSD,46 useful when working with small datasets as it produces a sensitivity distribution for each species rather than using a single toxicity endpoint. The compiled endpoints were converted into chronic no effect concentrations (NOEC), using two assessment factors. The time assessment factor (AFt) extrapolated acute studies into chronic estimations, and the dose-effect assessment factor (AFe) converted various endpoints (e.g. LC50, EC50) into NOECs.47 All these sensitivity distributions together form the species sensitivity distribution for an entire environmental compartment. The 5th percentile of the resulting PSSD was used as predicted no effect concentration (PNEC), as per European Chemicals Agency (ECHA) guidelines.47 We ran 10000 iterations, resulting in a PNEC distribution rather than a single value. Further detail regarding the methodology can be found in Coll et al. and Wigger et al.27,48
RCR = PEC/PNEC |
An RCR greater than 1 indicates that a risk does exist within the environmental compartment under consideration (surface water, soil, etc.), and that further risk management actions are required. An RCR below 1 indicates that there is no risk under the considered study conditions. In light of the probabilistic approach taken in this study, an RCR distribution, rather than a single value, was calculated by dividing all values of the PEC distribution by all values of the PNEC distribution.27,28
We assumed that 100% of nanocellulose produced in 2000 went into R&D, and that this share decreases linearly to 2025, using the 2015 shares mentioned above as a reference. Fig. 2 shows the mean share of each product category. The minimal, mean and maximal values used for the distributions can be found in Table S2.†
For ease of reading, we merged the categories representing under 3% of applications in 2025 into an ‘other’ category in Fig. 2. These are ‘aerogels’, ‘rheology modifiers’, ‘printed and flexible electronics’, ‘rubber and tire additives’, and ‘colorants’. However, each of these categories was treated independently in the model, with specific lifespans, release dynamics and EoL fate. The ‘medical and healthcare’ category, within ‘other’, was further subcategorized in the model, as the types of applications identified in the literature were quite varied, with different EoL and waste management implications. It is composed of ‘drug delivery’, ‘medical implants’, ‘tissue engineering’, ‘wound dressings’, and ‘lateral flow immunoassay labels’. The share of the ‘medical and healthcare’ category was evenly distributed between these five subcategories, as no detailed information regarding each application was found.
Priority (share of total NC application, 2015) | Product categories | Ref. | Use release | Use release duration (years) | Use release schedule | Allocation after use release | EoL release | Lifetime distribution (normal) (σ = stdev) | Allocation to waste categories | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
X | Y1 | Y2 | Y3 | Y4 | … | Waste water | Air | Surface water | Soil | 1-X | CDW | MMSW | WEEE | TextW | PackW | PaperW | MedW | AviW | AutoW | |||||
75.0% | R&D | Sun et al. 2014,33 R&D, CNT and fullerenes | 0.05 | 1 | 1 | 1 | 0.95 | Y1 = 1.0 | 1 | |||||||||||||||
8.9% | Paper and board | Sun et al. 2016,31 Paper, nano-TiO2 | 0 | 1 | Mean = 5; 3σ = 24 | 1 | ||||||||||||||||||
4.5% | Packaging | Sun et al. 2016,31 Plastics, nano-TiO2 | 0.03 | 8 | 1/8 | 1 | 0.97 | Mean = 8; 3σ = 5 | 1 | |||||||||||||||
3.4% | Filtration market | Sun et al. 2016,31 Filter, nano-TiO2 | 0.3 | 8 | 1/8 | 0.8 | 0.2 | 0.7 | Mean = 8; 3σ = 8 | 1 | ||||||||||||||
2.1% | Textiles | Sun et al. 2016,31 Textiles, nano-TiO2 | 0.03 | 3 | 0.5 | 0.3 | 0.2 | 0.8 | 0.2 | 0.97 | Mean = 3; 3σ = 2 | 1 | ||||||||||||
1.4% | Automotive | Sun et al. 2016,31 Automotive, CNT | 0 | 1 | Mean = 12; 3σ = 5 | 1 | ||||||||||||||||||
1.4% | Construction and building | Sun et al. 2016,31 Cement, nano-TiO2 | 0.01 | 80 | 0.9 | 1/79 | 1 | 0.99 | Mean = 80; 3σ = 20 | 1 | ||||||||||||||
1.0% | Coatings and films | Sun et al. 2016,31 Coatings, nano-TiO2 | 0.35 | 10 | 0.9 | 0.1*(1/9) | 0.8 | 0.1 | 0.1 | 0.65 | Mean = 10; 3σ = 5 | 0.5 | 0.167 | 0.333 | ||||||||||
0.7% | Rheology modifiers | Sun et al. 2016,31 Cosmetics, nano-TiO2 | 0.95 | 2 | 0.9 | 0.1 | 0.9 | 0.1 | 0.05 | Y1 = 0.9, Y2 = 0.1 | 1 | |||||||||||||
0.3% | Aerogels | Sun et al. 2016,31 Composites, CNT | 0.01 | 7 | 1/7 | 0.2 | 0.8 | 0.99 | Mean = 7; 3σ = 3 | 1 | ||||||||||||||
0.3% | Drug delivery | Giese et al. 2018,29 Medicine, nano-Ag | 0.9 | 1 | 1 | 1 | 0.1 | Y1 = 1.0 | 0.743 | 0.256 | ||||||||||||||
0.2% | Medical implants | Wang et al. 2018,43 Dental, nano-SiO2 | 0 | 1 | Mean = 8; 3σ = 5 | 1 | ||||||||||||||||||
0.2% | Tissue engineering | Wang et al. 2018,43 Dental, nano-SiO2 | 0 | 1 | Mean = 8; 3σ = 5 | 1 | ||||||||||||||||||
0.2% | Wound dressings | Sun 2015, Medtech,26 nano-Ag | 0.05 | 1 | 1 | 1 | 0.95 | Y1 = 1.0 | 1 | |||||||||||||||
0.2% | Lateral flow immunoassay labels | Sun 2015,26 Medtech, nano-Ag | 0.05 | 1 | 1 | 1 | 0.95 | Y1 = 1.0 | 1 | |||||||||||||||
0.2% | Rubber and tire additives | Wang et al. 2018,43 Car tire, nano-SiO2 | 0.13 | 4 | 1/4 | 0.01 | 0.99 | 0.87 | Mean = 4, 3σ = 2 | 1 | ||||||||||||||
0.2% | Colorants | Sun et al. 2016,31 Food, nano-TiO2 | 0.9 | 1 | 1 | 1 | 0.1 | Y1 = 1.0 | 1 | |||||||||||||||
0.2% | Printed and flexible electronics | Sun et al. 2016,31 Electronics, nano-TiO2 | 0.3 | 8 | 1/8 | 1 | 0.7 | Mean = 8; 3σ = 8 | 1 | |||||||||||||||
0.0% | Aerospace and aviation | Sun et al. 2016,31 Aerospace, CNT | 0 | 1 | Mean = 20; 3σ = 5 | 1 |
For example, all NC from the ‘paper and board’ product category is released at the products' EoL, with there being no use-release for this category. Its EoL-release is split between 82% going to ‘reprocessing’ (mean percentage of distribution), and the remaining 18% going to mixed municipal solid waste (MMSW), ending up in ‘landfilling’ and ‘WIP’. Another example is the ‘textile’ application, where 3% of the materials is released during the 3 year average use-phase of the product. Of this 3%, 50% is released into ‘wastewater’ and ‘air’ in the first year, 30% in the second and 20% during the third. The remaining 97% is released at the products' EoL, with close to a third being sorted, and the remained going to MMSW (‘landfill’ and ‘WIP’).
Fig. 3 Inflow of nanocellulose to various stocks and sinks in 2015 and 2025 in Europe. ‘ST soil’ represents sludge-treated soil. |
The mass flows of NC in 2015 and 2021 and the links between the various compartments are represented in Fig. 4. The model input flow is the production volume, in tons per year. The largest share of this flow is directly released to the various technical compartments. Just under a third of the inflow to ‘production’, ‘manufacturing’, and ‘consumption’ (PMC) enters the ‘in-use stock’, which will be released during later time-periods, as certain considered products have lifetimes of over one year. In both 2015 and 2025, the volume of NC in the ‘in-use’ stock is almost equal to the yearly volume being produced. Detailed flow results can be found in Table S4.†
The ‘landfill’ compartment represents by far the largest sink within the system boundary, having accumulated over 46000 t by 2025. Most NC entering ‘WIP’ will end up in the ‘elimination’, which has accumulated over 53000 t by 2025. Similarly to the ‘in-use stock’, flows to the three technical sink compartments (‘landfill’, ‘reprocessing’ and ‘on-site treatment’) show high accumulation rates of NC compared to yearly inflows. This is also true for the flows into the environmental sink compartments (‘soils’, ‘surface water’ and ‘air’), although the flows are smaller than those to technical compartments. This temporal consideration of past production and release dynamics greatly affects the volumes of NC within a compartment.
Certain applications are likely to release NC into wastewater during their use period, for example ‘textiles’, ‘rheology modifiers’, or ‘construction and building’ materials, as well as the aforementioned ‘R&D’ category. The largest share of NC entering the ‘wastewater’ compartment will flow into the ‘sewage treatment plant’ and end up in ‘soils’, ‘WIP’ or ‘landfills’. The remainder either goes into the ‘on-site treatment’ compartment, or exits the system to the ‘subsurface water’ or ‘surface water’ sinks. Lastly, materials leaving the system through ‘export’ are shares of WEEE, automotive waste, and textile waste that will be further processed outside of the system boundary.
The PSSD was build by combining the sensitivity distributions of the eight species (Fig. 6). Taking the 5th percentile of the PSSD allowed us to calculate the predicted no effect concentration (PNEC) probability density distribution (Fig. S1†), made up of 10000 values from the 10000 PSSD iterations. The mean value of the PNEC distribution is 7.69 mg l−1, the mode 2.11 mg l−1, Q15 1.86 mg l−1, and Q85 14.08 mg l−1.
Fig. 6 Probabilistic species sensitivity distribution (PSSD) of nanocellulose in freshwater, based on NOEC values. Yellow points represent single endpoint concentration values. |
The risk characterization ratio (RCR) distributions for 2015 and 2025 were computed by dividing all the values of the PEC distribution by all the values of the PNEC distribution (Fig. S2a and S2b†). In order to determine single RCR values, the mean, mode, median, Q15 and Q85 values were calculated (Table 2).
Mean | Mode | Median | Q 15 | Q 85 | |
---|---|---|---|---|---|
2015 | 6.9 × 10−5 | 2.0 × 10−5 | 3.4 × 10−5 | 1.1 × 10−5 | 1.2 × 10−4 |
2025 | 7.1 × 10−4 | 2.6 × 10−4 | 3.4 × 10−4 | 1.0 × 10−4 | 1.2 × 10−3 |
Current and future production volumes of NC remain highly uncertain. Although using a probabilistic approach does allow to account for this high uncertainty, having more sources would increase the robustness of the model. The three sources used as input data for our model varied substantially, and the lack of current and past production data means that any forecast needs to be treated with caution.12 On the other hand, the high volumes used as input in this model allow to represent a ‘worst case scenario’, with high production volumes leading to high concentrations in technical and environmental compartments, and therefore also a ‘worst case’ RCR. As our model is based on data from 2015, a qualitative comparison of 2019 predictions and its current status show the rate of commercialization is likely overestimated in the production data sources used. Although information regarding the current status of nanocellulose-based products on the market was difficult to come by, informal interviews with experts in the field corroborated out assumption about overestimated 2019 volumes, as many applications are still at pilot scale, and the predicted growth in production not yet having been observed.
In 2015, three quarters of produced NC was allocated to R&D, as many applications are only expected to reach commercialization in the upcoming years. This strongly influences the flows of the material within the system, as the NC in the R&D category was treated as having a lifespan of one year, with 95% of it being released at its EoL into mixed municipal solid waste (MMSW), and 5% being released into wastewater during its use phase. As applications reach commercialization over the years, thereby reducing the share of R&D, the stocks and flows will likely significantly change. As more products reach commercialization, the flows into sorting and reprocessing will likely grow, as many of the more prominent product categories have high recycling rates, for example paper and board, packaging, and textiles. Our model currently treats reprocessing as a sink. However, a next step would be to make this more realistic by detailing the fate of NC entering this compartment, and identifying in which environmental or technical compartments it may end up.
We were only able to compute a PSSD for the freshwater compartment due to missing data availability for soils and sediments. The choice of Kovacs et al.50 to focus on aquatic species for an in-depth ecotoxicity study of NC was justified by the wastewater stream having the highest potential for accidental release, with releases to soils and air considered improbable on a broad scale. However, the results from the DPMFA in this study show significant flows of NC to both natural and urban soils and sludge treated soils, and particularly important accumulations in the latter via wastewater treatment plants. There is also release directly from the use phase of products, due to applications such as rubber and tire additives and rheology modifiers, which are likely to grow in the future. The study by Vikman et al.54 found cellulose nanofibers (CNF) based packaging products to be biodegradable and compostable, and the ecotoxicological test performed in a compost environment did not show toxicity. However, more ecotoxicity tests looking at species in the soil compartment would be useful to develop a PSSD and quantitatively assess the risk within this compartment. Additional studies looking at the freshwater compartment would also help strengthen our results. According to European requirements,47 a minimum of ten species from eight taxonomic group are necessary to create a robust SSD. The PSSD in this study was based on eight species covering seven taxonomic groups. Although still allowing to produce useful results, these could be reinforced with additional aquatic ecotoxicological NC endpoints.
The PEC values reported in our model do not include any environmental fate processes such as degradation or agglomeration and sedimentation. We are therefore not providing real environmental concentrations but “release concentrations”, quantifying the total amount of NC that ends up in an environmental compartment. By considering environmental fate processes, environmental fate models for nanomaterials such as SimpleBox4Nano55 or NanoFate56 are able to predict distribution and concentrations in different environmental compartments.57 These fate models use the environmental release data provided by material flow models such as the one used in our current work. The PEC values provided by our model constitute a worst-case assessment by not considering potential degradation or agglomeration of particles. Within a prospective assessment, such an approach is justified, especially considering the fact that the exposure concentrations are many orders of magnitude smaller than the PNEC. Therefore reducing the PEC will even further decrease the risk characterization ratio.
A validation of the modelled released amounts or PEC values by measurements is not possible at the moment. This issue is not specific to NC but hampers all nanomaterial exposure assessments. Nowack et al.21 have discussed in detail that the results currently provided by analytical methods are not yet specific to engineered nanomaterials or are just not sensitive enough to reach realistic environmental concentrations. The available measurements cannot currently be used to validate the results of modeling studies, but they can provide orthogonal information to get a complete understanding of the presence of natural and engineered nanoparticles in the environment.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c9en00472f |
This journal is © The Royal Society of Chemistry 2019 |