Nanoplastics are detected in surface water, yet accurately quantifying their particle number concentrations remains a significant challenge. In this study, we tested the applicability of a gold-labelling method to quantify nanoplastics in natural organic matter (NOM) containing water matrices. Gelatin-coated gold nanoparticles (Au-gel NPs) form conjugates with nanoplastics via electrostatic interaction which produces peak signals which can be translated into particle number concentration using single-particle inductively coupled plasma–mass spectrometry (SP-ICP-MS). We used water samples with various NOM concentrations, with and without the addition of 1 × 107 particle–1 nanoplastics. Our results indicate that nanoplastics in low NOM samples (<1 mg·C L−1) could be successfully quantified. However, in high NOM samples (>15 mg·C L−1), only 13–19% of added nanoplastics were successfully quantified. Further digestion to remove NOM yielded only 10% of spiked nanoplastics. This discrepancy in high NOM samples could likely be attributed to the competition between nanoplastics and NOM existing in the water sample to bind with Au-gel NPs. Our study highlights the suitability of the Au-gel labelling method for quantifying nanoplastics in low NOM water samples. Nevertheless, further optimization, including pre-digestion steps, is essential to apply this method for high NOM water samples effectively.

  • Quantification of nanoplastic with gold-labelling using single-particle ICP-MS.

  • Application of nanoplastic labelling in natural waters.

  • Underestimation of nanoplastic due to competing negatively charged NOM.

  • Inefficient digestion to remove NOM.

It is predicted that around 11% (∼ 19–23 Mt) of the total global plastic waste emission has entered aquatic ecosystems (Borrelle et al. 2020) and this number will potentially increase up to 710 Mt by the year 2040 (Lau et al. 2020). As plastics are hard to degrade, they will eventually accumulate in the environment (da Costa 2018; Mitrano et al. 2021) where it undergoes chemical, physical, or biological weathering and degradation (Lehner et al. 2019). Such processes result in smaller plastic particles, including nanoplastics (Song et al. 2020; Sorasan et al. 2021). Most literature agreed to classify nanoplastics as plastics between 1 nm and 1 μm (da Costa et al. 2016; Gigault et al. 2018; Hartmann et al. 2019; Jakubowicz et al. 2021; Valsesia et al. 2021). Since nanoplastics can be generated from microplastics degradation, the number of particles might significantly increase upon breaking down to nanoscale. Nanoplastics pose a more significant threat to various aspects of ecosystems due to their higher specific surface area, higher particle number concentration, and enhanced reactivity compared to microplastics (Mattsson et al. 2018; Tallec et al. 2019). Detecting nanoplastics in water is thus a crucial tool for establishing baseline data as well as guiding policy decisions to combat nano and/or microplastic pollution. Recently, nanoplastics have been detected in multiple environmental matrices including surface water (Materić et al. 2022c; Xu et al. 2022), wastewater (Xu et al. 2023), soil (Wahl et al. 2021), and wastewater treatment sludge (Ho 2022). To date, methods to quantitatively indicate their size and particle number concentration are still developing (Cai et al. 2021). These methods include microscopic and light scattering techniques such as transmission electron microscopy (TEM) (Chen et al. 2017), scanning electron microscopy (SEM), dynamic light scattering (DLS) (Hernandez et al. 2017), and nanoparticle tracking analysis (NTA) (Hernandez et al. 2019). The disadvantage of all these techniques, however, is that the material composition of the particle has to be determined with additional methods. Techniques based on mass spectrometry have also been developed in recent years and allow us to detect the plastic type and mass. For instance, pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) was sensitive and reliable for identifying nanoplastics with additional preconcentration before analysis (Halle et al. 2017; Zhou et al. 2019; Xu et al. 2023). Nonetheless, the method requires laborious sample preparation (Nguyen et al. 2019). A promising quantification method to differentiate nanoplastics types was successfully tested by thermal desorption-proton transfer reaction-mass spectrometry (TD-PTR-MS) in different environmental samples such as high-altitude snow, surface waters, and seawater without preconcentration, digestion, or separation (Materić et al. 2020, 2022a, 2022b, 2022c). With TD-PTR-MS, plastic type and (low) mass could be accurately determined, but the device could not provide information on particle number concentration. Lastly, a total organic carbon (TOC)-based method was developed to estimate TOC originating from microplastics in sewage (Hong et al. 2021; Li et al. 2022). However, this procedure cannot characterize or quantify individual microplastics. Recently, inductively coupled plasma-mass spectrometry was used to detect microplastics directly by measuring the signal intensity of the carbon-13 isotope (13C+) (Bolea-Fernandez et al. 2020; Laborda et al. 2021). However, background noise from other C-species present in the samples can interfere with the measurement. Another method was developed to aid this issue by indirect measurement of nanoplastics using metallic tagging. The particle number concentration of nanoplastics was measured by binding nanoplastics to gold nanoparticles coated with gelatin (Au-gel NPs), which was then analysed using inductively coupled plasma–mass spectrometry in single-particle mode (SP-ICP-MS) (Jiménez-Lamana et al. 2020). Due to surface oxidation, nanoplastics are expected to have different chemical groups on their surface, such as carboxyl, hydroxyl, and carbonyl (Liu et al. 2019). In this gold-labelling method, carboxylated nanoplastics were used as a nanoplastics model to mimic the oxidized surface of environmental nanoplastics. The method relies on the electrostatic interaction of positively charged Au-gel NPs and negatively charged carboxyl groups on the surface of the nanoplastics. The binding is dictated by the number of carboxyl groups on the surface of nanoplastics (Marigliano et al. 2021). Using 17 nm AuNPs and model polystyrene (PS) with carboxyl groups at the surface, the method successfully detected and determined nanoplastics particle number concentration for a range of spherical model nanoplastics (<1 μm).

With ICP-MS widely available in most analytical laboratories, the gold-gelatin labelling method offers a promising alternative for streamlining nanoplastics monitoring routines in surface water samples, reducing laborious extraction procedures. However, besides environmental nanoplastics, surface water also contains natural organic matter (NOM) (Oriekhova & Stoll 2018). NOM concentration in surface water, especially in the Netherlands, ranges from 2 to >10 mgC L−1 (Beckett & Ranville 2006). Meanwhile, groundwater has a relatively lower NOM concentration at around 0.1–4 mgC L−1 (Regan et al. 2017). Similar to environmental nanoplastics, NOM also has various functional groups on its surface including carboxyl groups (Adusei-Gyamfi et al. 2019). NOM might therefore also bind with gold-gelatin particles when the gold-labelling method is applied to water with high NOM concentration, thereby obscuring binding sites for nanoplastics. It is, therefore, necessary to investigate the method's sensitivity in the presence of NOM. This study aimed to fill that gap and to apply the nanoplastics labelling method to real environmental samples with different NOM concentrations and explore the possibilities and/or limitations of this method for routine measurement of nanoplastics. Tap and bottled water were chosen to represent low NOM concentration, while surface water samples obtained from an urban environment in the western part of the Netherlands were used as high NOM concentration samples.

Reagents and chemicals

Carboxyl-functionalized polystyrene (PS) nanoplastics and gold nanoparticles coated with gelatin (Au-gel NPs) used for this experiment were synthesized as described earlier (Jiménez-Lamana et al. 2020). These nanoparticles were prepared by the nanoparticles Lab of NTNU Norway. The particle number concentrations of nanoplastics and Au-gel NPs, obtained with ZetaView (Particle Metrix), were 3.9 × 1015 particles L−1 and 3.0 × 1013 particles L−1, respectively. The hydrodynamic size of nanoplastics in suspension was measured at 390 nm with a poly dispersivity index (PDI) of 0.06 and Au-gel NPs particles at 56 nm with a PDI of 0.52. Both values were measured with ZetaSizer (Malvern). Furthermore, 50 and 80 nm gold nanoparticles (BBI Solution Crumlin, UK) were used to determine the transport efficiency of SP-ICP-MS. Ultrapure water, obtained with a MilliQ system, was used to dilute all chemicals.

Instrument

A Thermofisher X Series II ICP-MS operated in single-particle mode with gold as the targeted analyte was used throughout the experiment. The dwell time was set at 5 ms and the total acquisition time was 60 s. Thus, a total of 12,000 data signals were obtained per replicate. The transport efficiency (η) in this study was determined using the particle frequency method (Pace et al. 2011) after separating the background noise and particle signals using the criteria of three standard deviations (3σ) above the mean signal as the baseline. Details about η calculation and their potential error are summarized in the Supplementary Information.

Water samples

We used three types of water samples: commercially bottled water from a plastic bottle, tap water of Delft, and environmental water from the Schie canal near Delft, the Netherlands (52.019607, 4.352881). Thereto, 500 mL water was collected from five sampling locations along the canal, approximately 1 km between each sampling point. All samples were collected using an amber glass reagent bottle, previously washed with hydrochloric acid (1:5, HCl: water ratio), and rinsed thoroughly with demineralized water. The DOC of all water samples was measured using a Shimadzu TOC-L Series after filtering the samples (Whatman Spartan 30/0.45 μm RC).

Determination of carboxyl groups

The carboxyl group density at nanoplastics surface was determined using a potentiometric titration method with a Titrino 848, according to Pessoni et al. (2019). 1 mL of nanoplastics stock solution was diluted into 100 mL of ultrapure water, and 60 mL was used for the experiment. The solution was titrated with 50 μL 0.01 M NaOH every 5 min under a constant nitrogen gas supply. Throughout the titration, the pH and conductivity of the solution were measured. The measurement was done until the pH of the sample reached 12.0.

Determination of Au-gel NPs and nanoplastics conjugates particle concentration

Nanoplastics were added to ultrapure water in a concentration range from 7 × 106 to 9 × 107 particles L−1. This range was selected according to the recommended range of particle detection with SP-ICP-MS to ensure the number of peaks in the time scan will not exceed 10% of the maximum number of peaks based on the dwell time, which is between 1 × 106 and 1 × 108 particles L−1 (Pace et al. 2011). Au-gel NPs were added to nanoplastics suspension at the recommended number ratio of 500:1 for labelling translating into sufficiently high ICP-MS signals (Jiménez-Lamana et al. 2020). All suspensions were immediately analysed on the SP-ICP-MS. From the tested nanoplastics concentration range, the ideal nanoplastics-Au-gel NP conjugate concentration was selected for all subsequent tests in other water matrices.

Au-gel NPs and the PS nanoplastics stock were diluted to 50 mL of water samples (bottled, tap, and canal) to obtain the desired particle number concentration based on the previous test. To determine the presence of nanoparticles with a negative charge (e.g. due to existing nanoplastics or NOM), Au-gel NPs with the same concentration were added to the raw sample without adding nanoplastics.

Oxidation nanoplastics in the environment have O-containing functional groups on their surface, such as carboxyl groups (COOH) (Blancho et al. 2021a). The determination of nanoplastics particle number concentration in this study relies on the electrostatic binding between the carboxyl groups present at the surface of nanoplastics and positively charged Au-gel NPs (Jiménez-Lamana et al. 2020; Marigliano et al. 2021). Therefore, when a sample spiked with Au-gel NPs is analysed with SP-ICP-MS, signals are correlated to two different Au sources, namely signals from ‘unbound’ Au-gel NPs and signals from conjugates between nanoplastics and Au-gel NPs. All signals which exceeded the baseline of 3σ above the mean signal were counted as nanoplastics – Au-gel NPs conjugates. Eventually, this concentration is measured as a proxy for nanoplastics concentration. The calculation of particle number concentrations from nanoplastics-Au-gel NPs conjugates is summarized in the Supplementary Information.

Pre-digestion for high NOM water sample

To reduce interference from other negatively charged nanoparticles such as NOM, some of the canal water samples were subjected to Fenton digestion proposed by Cunsolo et al. (2021). Briefly, 100 mL of 0.05 M FeSO4 followed by 100 mL of H2O2 were added to 50 mL of sample in a 500-mL glass beaker. An ice bath was used to regulate the temperature below 50 °C to preserve environmental nanoplastics possibly present in the sample. Then, 2 M NaOH was added drop-by-drop to maintain the pH of the solution around 3.0–4.0 as the optimum pH condition and to avoid the reaction of soluble iron species with H2O2. The digestion process was carried out until no more bubbles were observed. After digestion, the pH of the solution was adjusted to around 7.0 which is the natural pH of the water sample by adding 2 M NaOH drop-by-drop. Also, in this pH range, nanoplastics and Au-gel NPs conjugate were stable according to the previous study (Jiménez-Lamana et al. 2020). Au-gel NPs and nanoplastics were then added to the sample. Results were compared between non-digested and digested canal water samples with and without added nanoplastics.

Determination of nanoplastics in ultrapure water

In ultrapure water, the Au-gel labelling method performed well in the range of 7 × 106 up to 1 × 107 nanoplastics particles L−1. Within this concentration range, the measured concentrations of nanoplastics – Au-gel NPs conjugates (experimental concentrations) were comparable to the added concentration of nanoplastics (theoretical concentration). However, at higher added nanoplastics concentrations, the method underestimated the concentration (Figure S1). The observed trend might be related to the selection of dwell time intervals used in this study. With a dwell time of 5 ms and a total acquisition time of 60 s, the optimum particle number concentration is in the order of 107 particles L−1 (Abad-Álvaro et al. 2016).

The number of Au-gel NPs per nanoplastics can be calculated from the measured data by dividing the mass of Au-gel NP and nanoplastics conjugates measured via signal peaks in SP-ICP-MS by the mass of a single Au-gel NP (Supplementary Information). With a nanoplastics concentration of 1 × 107 particles L−1, our study obtained 230 Au-gel NPs attached per nanoplastics. This number is almost half of the measured number in the previous study, although similar concentrations were used (Jiménez-Lamana et al. 2020). We argue that differences between the two studies are resulting from the lower amount of COOH groups on the surface of our nanoplastics (27 COOH groups per nm2) compared to the previous study (41 COOH groups per nm2). Given that the interaction between carboxyl-modified nanoplastics with positively charged metallic nanoparticles is determined by the amount of COOH groups on the surface of nanoplastics, a lower COOH group availability leads to measuring lower Au-gel NPs attached per nanoplastics. Indeed, a study by Marigliano et al. (2021) also observed that the gold-labelling method was dependent on the surface functionalization of nanoplastics. This result indicated that nanoplastics labelling with Au-gel NPs will also depend on the degree of aging of environmental nanoplastics which may determine the carboxyl group density on the surface (Zhang et al. 2022).

Determination of nanoplastics in water with various NOM concentrations

Based on the concentration range test in ultrapure water (Figure S1), a nanoplastics concentration of 1 × 107 particles L−1 was chosen and added to samples with varying NOM concentrations. DOC concentrations in low NOM samples such as bottled water and tap water were 0.32 ± 0.06 and 1.76 ± 0.03 mg·C L−1, respectively. DOC concentrations in samples with higher NOM content from the Schie canal in Delft ranged between 15.47 ± 0.00 and 19.64 ± 0.14 mg·C L−1 (Figure S2). Particles were detected in all samples even without the addition of nanoplastics, which indicated the presence of negatively charged nano-sized material that binds with Au-gel NPs (Figure 1). Since the method we tested cannot identify the chemical characteristic of the measured particle (i.e. the polymer type) without a combination of other spectroscopic techniques such as FT-IR, Py-GC-MS, or TD-PTR-MS, we identified these particles as Au-gel NPs conjugates throughout the text.
Figure 1

Detected Au-gel NPs-nanoparticles conjugates with (orange) and without (blue) the addition of 1.0 × 107 nanoplastics (green) particles in water with low NOM concentrations (bottled water: 0.32 ± 0.06 mg·C L−1, tap water: 1.76 ± 0.03 mg·C L−1) and high NOM concentrations (Canal water 1–5: 15.47 ± 0.00 mg·C L−1 to 19.64 ± 0.14 mg·C L−1). The stacked bar is the expected conjugate concentration after the addition of nanoplastics. The difference between the stacked bar and orange bar indicates the over/underestimation of nanoplastics content due to the presence of NOM or other nanoparticles. Error bar: standard deviation, n = 3 replicates).

Figure 1

Detected Au-gel NPs-nanoparticles conjugates with (orange) and without (blue) the addition of 1.0 × 107 nanoplastics (green) particles in water with low NOM concentrations (bottled water: 0.32 ± 0.06 mg·C L−1, tap water: 1.76 ± 0.03 mg·C L−1) and high NOM concentrations (Canal water 1–5: 15.47 ± 0.00 mg·C L−1 to 19.64 ± 0.14 mg·C L−1). The stacked bar is the expected conjugate concentration after the addition of nanoplastics. The difference between the stacked bar and orange bar indicates the over/underestimation of nanoplastics content due to the presence of NOM or other nanoparticles. Error bar: standard deviation, n = 3 replicates).

Close modal

The measured number concentrations of Au-gel NPs conjugates (blue bars in Figure 1) were proportional to DOC concentrations. For instance, low particle number concentrations were measured in low NOM, bottled water, and tap water samples, with 1.8 ± 1.5 × 106 and 3.9 ± 1.4 × 106 particles L−1, respectively.

Particles detected in bottled water are unlikely to be related to the presence of NOM in the sample. Another potential source might have been the plastic of the bottle itself. Bigger microplastics have been previously detected in bottled water with particle number concentrations between 14 and 5.42 × 107 particles L−1 (Oßmann et al. 2018; Schymanski et al. 2018; Zuccarello et al. 2019). However, these results should be carefully interpreted due to the sensitivity and size detection of the different analytical techniques used. Nonetheless, particles detected in the bottled water sample in this study should not be neglected, and a dedicated study for chemical identification and quantification in bottled water is required.

Meanwhile, higher particle number concentrations of Au-gel NPs conjugates (6.5 ± 0.4 × 106 particles L−1 up to 1.0 ± 0.1 × 107 particles L−1) were measured in canal water samples (high NOM samples). Negatively charged nano-sized NOM is a ubiquitous constituent of surface water, with carboxyl and phenolic groups as the predominant functional groups (Lodeiro et al. 2020). For instance, carboxyl groups account for 78–90% and 69–82% of all functional groups on the surface of nano-sized NOM, such as humic acid and fulvic acid (Ritchie & Michael Perdue 2003). Having the same charge, it is likely for NOM to adsorb and bind with Au-gel NPs. Unfortunately, SP-ICP-MS cannot distinguish the origin of particles bound to Au-gel NPs, which produced signals above the background and are counted as particle number concentration. Thus, due to interference from any other negatively charged particles in surface water, the measured particle number concentration of Au-gel NPs conjugates might not be an accurate representation of nanoplastics.

We further confirmed this interference by spiking the same water samples with 1 × 107 nanoplastics particles L−1 (green bars), upon which higher particle number concentrations of Au-gel NPs conjugates were measured in all samples (orange bars in Figure 1). In bottled water (DOC of 0.32 ± 0.06 mg·C L−1), the measured concentration of Au-gel NPs conjugates was 1.5 ± 1.5 × 107 particles L−1, with an expected particle number concentration of 1.18 ± 0.1 × 107 particles L−1. The measured concentration of conjugates after nanoplastics spiked (orange bar in Figure 1) was slightly higher than the expected concentration after the addition of nanoplastics; however, this result is still within the acceptable range which was also observed in the study by Marigliano et al. (2021). In bottled water, Au-gel NPs were effectively bound with nanoplastics added to the sample indicating no interference from other negatively charged nanoparticles. However, in tap water (DOC 1.76 ± 0.03 mg·C L−1), the measured concentration after spiking was 9.17 ± 0.56 × 106 particles L−1 with an increase of only 5.23 ± 0.84 × 106 particles L−1 (equal to 52.3%) compared to the samples without the addition of nanoplastics. These results indicate that NOM – even if present only at 1.76 ± 0.03 mg·C L−1 – competed for the labelling agent. As such, only water samples with minimum interference from other negatively charged nanoparticles, i.e. DOC < 1 mg·C L−1 allow successful quantification of nanoplastics particles. In spiked canal water samples with high NOM concentrations, the measured increase in particle numbers was consistently lower than tap water. In those samples, measured concentrations after spiking ranged between 7.89 ± 0.11 × 106 and 1.19 ± 1.24 × 107 particles L−1. The concentration differences before and after spiking nanoplastics resulted in a particle increase of less than 2 × 106 particles L−1. These results were equal to a roughly 13.10–18.72% of the actual spiked nanoplastics concentration of 1 × 10 particles L−1, indicating ineffective binding between Au-gel NP and nanoplastics. NOM has abundant functional groups and could adsorb easily to the surface of nanoparticles (Wang et al. 2022). We argue that the ineffective nanoplastics labelling in samples with high NOM concentration was due to the existing NOM in the samples that potentially contained a higher number of COOH groups. This NOM competed to bind with Au-gel NPs and hindered the interaction between nanoplastics and Au-gel NPs. Indeed, previous studies have observed that interaction between NOM and engineered gold nanoparticle are inevitable (Louie et al. 2013). Consequently, reduced attachment of Au-gel NPs to nanoplastics surface was observed in all samples with high NOM concentration.

In order to reduce the NOM interference, we conducted a Fenton digestion. Following this digestion, the dissolved organic carbon (DOC) in the canal water samples exhibited an 85% reduction, dropping to between 2.76 ± 0.02 and 3.05 ± 0.24 mg L−1 (Figure S3). The addition of nanoplastics to the digested samples resulted in an increase in measured particle concentration compared to the non-digested sample. However, the observed differences in particle concentration remained below the anticipated concentration after nanoplastics spiking as depicted in Figure 2.
Figure 2

Particle number concentrations of conjugates in canal water samples after Fenton digestion with (yellow bar) and without (blue bar) addition of 1 × 107 particles L−1 nanoplastics (green bar). The stacked bar is the expected conjugate concentration after the addition of nanoplastics. The difference between the stacked bar and yellow bar indicates the over/underestimation of nanoplastics content due to the presence of NOM or other nanoparticles. Error bar: standard deviation, n = 3 replicates.

Figure 2

Particle number concentrations of conjugates in canal water samples after Fenton digestion with (yellow bar) and without (blue bar) addition of 1 × 107 particles L−1 nanoplastics (green bar). The stacked bar is the expected conjugate concentration after the addition of nanoplastics. The difference between the stacked bar and yellow bar indicates the over/underestimation of nanoplastics content due to the presence of NOM or other nanoparticles. Error bar: standard deviation, n = 3 replicates.

Close modal

Although we observed an increase, a more optimum digestion procedure is required. The tests show the unsuitability of the proposed method in high NOM waters. While digestion could improve the adsorption of Au-gel NPs to nanoplastics, the remaining NOM after the digestion process still interfered with the conjugation process between the two particles. One possible alternative to aid this issue was demonstrated in a study by Lai et al. (2021) by combining acid digestion and cloud-point extraction. In the mentioned study, a different approach of gold-labelling was used by growing AuNPs directly on the surface of nanoplastics and subsequently quantifying them using SP-ICP-MS. To improve the method's sensitivity, acid digestion with a mixture of 5 mM HNO3 and 40 mM HF was used to remove other environmental matrices, followed by cloud-point extraction to concentrate nanoplastics and improve the Au-labelling efficiency.

We explored the possibility of applying an indirect measurement of nanoplastics via metallic tagging using gelatin-coated gold nanoparticles. Our research showed that both the density of carboxyl group on the surface of nanoplastics and the concentration of NOM in water samples were critical parameters that affected the sensitivity of the gold-labelling method. Based on our findings, the Au-gel labelling method was most effective for quantifying particle number concentrations in waters with very low levels of NOM, such as bottled water and possibly deep groundwater. Furthermore, our study showed the significance of employing a digestion protocol on water samples with high NOM concentration, prior to applying this method. The test results indicate that the proposed method required more extensive sample preparation with surface water that contains high levels of NOM. While Fenton digestion could reduce the interference of NOM, there was still a significant presence of NOM that continued to disrupt the adsorption process between Au-gel NPs and nanoplastics particles. It is essential to conduct a dedicated study aimed at developing a digestion method that effectively removes NOM while preserving the environmental nanoplastics present in the sample. Moreover, the comparison between our results and those of the initial study by Jiménez-Lamana et al. (2020) and Marigliano et al. (2021) highlights the crucial role of achieving an adequate density of COOH (carboxyl) groups on nanoplastics surface to ensure accurate nanoplastics labelling using Au-gel nanoparticles.

An important next step could be to investigate the lower limit of COOH group density at nanoplastics surface for which this method can successfully and accurately quantify nanoplastics in low NOM water samples. Furthermore, a recent study indicates nanoplastics in the environment might have a lower COOH group from weathering than COOH functionalized nanoplastics (Blancho et al. 2021b), hence optimizing this method for environmentally relevant COOH groups on the surface of nanoplastics is another avenue to explore. Lastly, only a combination of the gold-labelling and SP-ICP-MS method with spectroscopic techniques will allow the quantification nanoplastics particles and thus the distinction from other organic particles. In spite of current limitations, further optimization of digestion protocols might allow the use of this method as a screening tool for nanoplastics monitoring in suitable water matrices.

Dušan Materić acknowledges the support from the Dutch Research Council (Nederlandse Organisatie Voor Wetenschappelijk Onderzoek – NWO) projects: ‘Nanoplastics: hormone-mimicking and inflammatory responses?’ (grant number OCENW.XS2.078) and ‘Size distribution of nanoplastics in indoor, urban and rural air’ (grant number OCENW.XS21.2.042).

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Abad-Álvaro
I.
,
Peña-Vázquez
E.
,
Bolea
E.
,
Bermejo-Barrera
P.
,
Castillo
J. R.
&
Laborda
F.
2016
Evaluation of number concentration quantification by single-particle inductively coupled plasma mass spectrometry: Microsecond vs. millisecond dwell times
.
Analytical and Bioanalytical Chemistry
408
(
19
),
5089
5097
.
https://doi.org/10.1007/s00216-016-9515-y
.
Adusei-Gyamfi
J.
,
Ouddane
B.
,
Rietveld
L.
,
Cornard
J. P.
&
Criquet
J.
2019
Natural organic matter-cations complexation and its impact on water treatment: A critical review
.
Water Research
160
,
130
147
.
https://doi.org/10.1016/j.watres.2019.05.064
.
Beckett
R.
&
Ranville
J.
2006
Chapter 17: Natural organic matter
.
Interface Science and Technology
10
(
C
),
299
315
.
https://doi.org/10.1016/S1573-4285(06)80086-4
.
Blancho
F.
,
Davranche
M.
,
Fumagalli
F.
,
Ceccone
G.
&
Gigault
J.
2021a
A reliable procedure to obtain environmentally relevant nanoplastics proxies
.
Environmental Science: Nano
8
(
11
),
3211
3219
.
https://doi.org/10.1039/d1en00395j
.
Blancho
F.
,
Davranche
M.
,
Hadri
H. E.
,
Grassl
B.
&
Gigault
J.
2021b
Nanoplastics identification in complex environmental matrices: Strategies for polystyrene and polypropylene
.
Environmental Science and Technology
55
(
13
),
8753
8759
.
https://doi.org/10.1021/acs.est.1c01351
.
Bolea-Fernandez
E.
,
Rua-Ibarz
A.
,
Velimirovic
M.
,
Tirez
K.
,
Vanhaecke
F.
,
Jambeck, J., Leonard, G. H., Hilleary, M. A., Eriksen, M., Possingham, H. P., De Frond, H., Gerber, L. H., Polidoro, B, Takier, A., Bernard, M., Mallos, N., Barnes, M. & Rochman, N. M.
2020
Detection of microplastics using inductively coupled plasma-mass spectrometry (ICP-MS) operated in single-event mode
.
Journal of Analytical Atomic Spectrometry
35
(
3
),
455
460
.
https://doi.org/10.1039/c9ja00379g
.
Borrelle
S. B.
,
Ringma
J.
,
Law
K. L.
,
Monnahan
C. C.
,
Lebreton
L.
,
McGivern
A.
, Murphy, E., Jambeck, J., Leonard, G. H., Hilleary, M. A., Eriksen, M., Possingham, H. P., De Frond, H., Gerber, L. R., Polidoro, B., Tahir, A., Bernard, M., Mallos, N., Barnes, M. & Rochman, C. M.
2020
Mitigate plastic pollution
.
Science
1518
,
1515
1518
. .
Cai
H.
,
Xu
E. G.
,
Du
F.
,
Li
R.
,
Liu
J.
&
Shi
H.
2021
Analysis of environmental nanoplastics: Progress and challenges
.
Chemical Engineering Journal
410
,
128208
.
https://doi.org/10.1016/j.cej.2020.128208
.
Chen
Q.
,
Gundlach
M.
,
Yang
S.
,
Jiang
J.
,
Velki
M.
,
Yin
D.
&
Hollert
H.
2017
Quantitative investigation of the mechanisms of microplastics and nanoplastics toward Zebrafish larvae locomotor activity
.
Science of the Total Environment
584–585
,
1022
1031
.
https://doi.org/10.1016/j.scitotenv.2017.01.156
.
Cunsolo
S.
,
Williams
J.
,
Hale
M.
,
Read
D. S.
&
Couceiro
F.
2021
Optimising sample preparation for FTIR-based microplastic analysis in wastewater and sludge samples: Multiple digestions
.
Analytical and Bioanalytical Chemistry
413
(
14
),
3789
3799
.
https://doi.org/10.1007/s00216-021-03331-6
.
da Costa
J. P.
2018
Micro- and nanoplastics in the environment: Research and policymaking
.
Current Opinion in Environmental Science and Health
1
,
12
16
.
https://doi.org/10.1016/j.coesh.2017.11.002
.
da Costa
J. P.
,
Santos
P. S. M.
,
Duarte
A. C.
&
Rocha-Santos
T.
2016
(Nano)Plastics in the environment – sources, fates and effects
.
Science of the Total Environment
566–567
,
15
26
.
https://doi.org/10.1016/j.scitotenv.2016.05.041
.
Gigault
J.
,
Halle
A. t.
,
Baudrimont
M.
,
Pascal
P. Y.
,
Gauffre
F.
,
Phi
T. L.
,
Hadri
H. E.
,
Grassl
B.
&
Reynaud
S.
2018
Current opinion: What is a nanoplastics?
Environmental Pollution
235
,
1030
1034
.
https://doi.org/10.1016/j.envpol.2018.01.024
.
Halle
A. T.
,
Jeanneau
L.
,
Martignac
M.
,
Jardé
E.
,
Pedrono
B.
,
Brach
L.
&
Gigault
J.
2017
Nanoplastics in the north Atlantic subtropical gyre
.
Environmental Science and Technology
51
(
23
),
13689
13697
.
https://doi.org/10.1021/acs.est.7b03667
.
Hartmann
N. B.
,
Hüffer
T.
,
Thompson
R. C.
,
Hassellöv
M.
,
Verschoor
A.
,
Daugaard
A. E.
,
Rist
S.
, Karlsson, T., Brennholt, M., Cole, N., Herrling, M. P., Hess, M. C., Ivleva, N. P., Lusher, A. L. & Wagner, M.
2019
Are we speaking the same language? Recommendations for a definition and categorization framework for plastic debris
.
Environmental Science and Technology
53
(
3
),
1039
1047
.
https://doi.org/10.1021/acs.est.8b05297
.
Hernandez
L. M.
,
Yousefi
N.
&
Tufenkji
N.
2017
Are there nanoplastics in your personal care products?
Environmental Science and Technology Letters
4
(
7
),
280
285
.
https://doi.org/10.1021/acs.estlett.7b00187
.
Hernandez
L. M.
,
Xu
E. G.
,
Larsson
H. C. E.
,
Tahara
R.
,
Maisuria
V. B.
&
Tufenkji
N.
2019
Plastic teabags release billions of microparticles and nanoparticles into tea
.
Environmental Science and Technology
53
(
21
),
12300
12310
.
https://doi.org/10.1021/acs.est.9b02540
.
Ho
D. T. K.
2022
Abundance of microplastics in wastewater treatment sludge
.
Journal of Human, Earth, and Future
3
(
1
),
138
146
.
https://doi.org/10.28991/HEF-2022-03-01-010
.
Hong
Y.
,
Oh
J.
,
Lee
I.
,
Fan
C.
,
Pan
S. Y.
,
Jang
M.
,
Park
Y. K.
&
Kim
H.
2021
Total-organic-carbon-based quantitative estimation of microplastics in sewage
.
Chemical Engineering Journal
423
,
130182
.
https://doi.org/10.1016/j.cej.2021.130182
.
Jakubowicz
I.
,
Enebro
J.
&
Yarahmadi
N.
2021
Challenges in the search for nanoplastics in the environment – A critical review from the polymer science perspective
.
Polymer Testing
93
,
106953
.
https://doi.org/10.1016/j.polymertesting.2020.106953
.
Jiménez-Lamana
J.
,
Marigliano
L.
,
Allouche
J.
,
Grassl
B.
,
Szpunar
J.
&
Reynaud
S.
2020
A novel strategy for the detection and quantification of nanoplastics by single particle inductively coupled plasma mass spectrometry (ICP-MS)
.
Analytical Chemistry
92
(
17
),
11664
11672
.
https://doi.org/10.1021/acs.analchem.0c01536
.
Lai
Y.
,
Dong
L.
,
Li
Q.
,
Li
P.
,
Hao
Z.
,
Yu
S.
&
Liu
J.
2021
Counting nanoplastics in environmental waters by single particle inductively coupled plasma mass spectroscopy after cloud-point extraction and in situ labeling of gold nanoparticles
.
Environmental Science and Technology
55
(
8
),
4783
4791
.
https://doi.org/10.1021/acs.est.0c06839
.
Lau, W. W. Y., Shiran, Y., Bailey, R. M., Cook, E., Stuchtey, M. R., Koskella, J., Velis, C. A., Godfrey, L., Boucher, J., Murphy, M. B., Thompson, R. C., Jankowska, E., Castillo, A., Pilditch, T. D., Dixon, B., Koerselman, L., Kosior, E., Favoino, E., Gutberlet, J., Baulch, S., Atreya, M. E., Fischer, D., He, K. K., Petit, M. M., Sumaila, U. R., Neil, E., Bernhofen, M. V., Lawrence, K. & Palardy, J. E.
2020
Evaluating scenarios toward zero plastic pollution
.
Science
369
(
6509
),
1455
1461
.
https://doi.org/10.1126/SCIENCE.ABA9475
.
Lehner
R.
,
Weder
C.
,
Petri-Fink
A.
&
Rothen-Rutishauser
B.
2019
Emergence of nanoplastics in the environment and possible impact on human health
.
Environmental Science and Technology
.
https://doi.org/10.1021/acs.est.8b05512
.
Li
P.
,
Lai
Y.
,
Li
Q.
,
Dong
L.
,
Tan
Z.
,
Yu
S.
,
Chen
Y.
,
Sharma
V. K.
,
Liu
J.
&
Jiang
G.
2022
Total organic carbon as a quantitative index of micro- and nano-plastic pollution
.
Analytical Chemistry
94
(
2
),
740
747
.
https://doi.org/10.1021/acs.analchem.1c03114
.
Liu, J., Zhang, T., Tian, L., Liu, X., Qi, Z., Ma, Y., Ji, R. & Chen, W. 2019 Aging significantly affects mobility and contaminant-mobilizing ability of nanoplastics in saturated loamy sand. Environmental Science and Technology 53 (10), 5805–5815.
Lodeiro
P.
,
Rey-Castro
C.
,
David
C.
,
Achterberg
E. P.
,
Puy
J.
&
Gledhill
M.
2020
Acid-base properties of dissolved organic matter extracted from the marine environment
.
Science of the Total Environment
729
,
138437
.
https://doi.org/10.1016/j.scitotenv.2020.138437
.
Louie
S. M.
,
Tilton
R. D.
&
Lowry
G. V.
2013
Effects of molecular weight distribution and chemical properties of natural organic matter on gold nanoparticle aggregation
.
Environmental Science and Technology
47
(
9
),
4245
4254
.
https://doi.org/10.1021/es400137x
.
Marigliano
L.
,
Grassl
B.
,
Szpunar
J.
,
Reynaud
S.
&
Jiménez-Lamana
J.
2021
Nanoplastics labelling with metal probes: Analytical strategies for their sensitive detection and quantification by Icp mass spectrometry
.
Molecules
26
(
23
).
https://doi.org/10.3390/molecules26237093
.
Materić
D.
,
Kasper-Giebl
A.
,
Kau
D.
,
Anten
M.
,
Greilinger
M.
,
Ludewig
E.
,
Van Sebille
E.
,
Röckmann
T.
&
Holzinger
R.
2020
Micro-and nanoplastics in alpine snow: A new method for chemical identification and (Semi)Quantification in the nanogram range
.
Environmental Science and Technology
54
(
4
),
2353
2359
.
https://doi.org/10.1021/acs.est.9b07540
.
Materić
D.
,
Holzinger
R.
&
Niemann
H.
2022a
Nanoplastics and ultra fine microplastic in the Dutch Wadden Sea – the hidden plastics debris ?
Science of the Total Environment
846
.
https://doi.org/10.1016/j.scitotenv.2022.157371
.
Materić
D.
,
Kjær
H. A.
,
Vallelonga
P.
,
Tison
J. L.
,
Röckmann
T.
&
Holzinger
R.
2022b
Nanoplastics measurements in northern and southern polar ice
.
Environmental Research
208
.
https://doi.org/10.1016/j.envres.2022.112741
.
Materić
D.
,
Peacock
M.
,
Dean
J.
,
Futter
M.
,
Maximov
T.
,
Moldan
F.
,
Röckmann
T.
&
Holzinger
R.
2022c
Presence of nanoplastics in rural and remote surface waters
.
Environmental Research Letters
17
,
054036
.
Mattsson
K.
,
Jocic
S.
,
Doverbratt
I.
&
Hansson
L. A.
2018
Nanoplastics in the aquatic environment
. In:
Microplastic Contamination in Aquatic Environments: An Emerging Matter of Environmental Urgency
.
Elsevier Inc.
https://doi.org/10.1016/B978-0-12-813747-5.00013-8
.
Mitrano
D. M.
,
Wick
P.
&
Nowack
B.
2021
Placing nanoplastics in the context of global plastic pollution
.
Nature Nanotechnology
16
(
5
),
491
500
.
https://doi.org/10.1038/s41565-021-00888-2
.
Nguyen
B.
,
Claveau-Mallet
D.
,
Hernandez
L. M.
,
Xu
E. G.
,
Farner
J. M.
&
Tufenkji
N.
2019
Separation and analysis of microplastics and nanoplastics in complex environmental samples
.
Accounts of Chemical Research
52
(
4
),
858
866
.
https://doi.org/10.1021/acs.accounts.8b00602
.
Oriekhova
O.
&
Stoll
S.
2018
Heteroaggregation of nanoplastics particles in the presence of inorganic colloids and natural organic matter
.
Environmental Science: Nano
5
(
3
),
792
799
.
https://doi.org/10.1039/c7en01119a
.
Oßmann
B. E.
,
Sarau
G.
,
Holtmannspötter
H.
,
Pischetsrieder
M.
,
Christiansen
S. H.
&
Dicke
W.
2018
Small-Sized microplastics and pigmented particles in bottled mineral water
.
Water Research
141
,
307
316
.
https://doi.org/10.1016/j.watres.2018.05.027
.
Pace
H. E.
,
Rogers
N. J.
,
Jarolimek
C.
,
Coleman
V. A.
,
Higgins
C. P.
&
Ranville
J. F.
2011
Determining transport efficiency for the purpose of counting and sizing nanoparticles via single particle inductively coupled plasma mass spectrometry
.
Analytical Chemistry
83
(
24
),
9361
9369
.
https://doi.org/10.1021/ac201952t
.
Pessoni
L.
,
Veclin
C.
,
Hadri
H. E.
,
Cugnet
C.
,
Davranche
M.
,
Pierson-Wickmann
A. C.
,
Gigault
J.
,
Grassl
B.
&
Reynaud
S.
2019
Soap- and metal-free polystyrene latex particles as a nanoplastics model
.
Environmental Science: Nano
6
(
7
),
2253
2258
.
https://doi.org/10.1039/c9en00384c
.
Regan
S.
,
Hynds
P.
&
Flynn
R.
2017
An overview of dissolved organic carbon in groundwater and implications for drinking water safety
.
Hydrogeology Journal
25
(
4
),
959
967
.
https://doi.org/10.1007/s10040-017-1583-3
.
Ritchie
J. D.
&
Michael Perdue
E.
2003
Proton-binding study of standard and reference fulvic acids, humic acids, and natural organic matter
.
Geochimica et Cosmochimica Acta
67
(
1
),
85
96
.
https://doi.org/10.1016/S0016-7037(02)01044-X
.
Schymanski
D.
,
Goldbeck
C.
,
Humpf
H. U.
&
Fürst
P.
2018
Analysis of microplastics in water by micro-Raman spectroscopy: Release of plastic particles from different packaging into mineral water
.
Water Research
129
,
154
162
.
https://doi.org/10.1016/j.watres.2017.11.011
.
Song
Y. K.
,
Hong
S. H.
,
Eo
S.
,
Han
G. M.
&
Shim
W. J.
2020
Rapid production of micro- and nanoplastics by fragmentation of expanded polystyrene exposed to sunlight
.
Environmental Science and Technology
54
(
18
),
11191
11200
.
https://doi.org/10.1021/acs.est.0c02288
.
Sorasan
C.
,
Edo
C.
,
Gonz
M.
,
Legan
F.
,
Rodríguez
A.
&
Rosal
R.
2021
Generation of nanoplastics during the photoageing of low-density polyethylene
.
289
.
https://doi.org/10.1016/j.envpol.2021.117919
.
Tallec
K.
,
Blard
O.
,
González-Fernández
C.
,
Brotons
G.
,
Berchel
M.
,
Soudant
P.
,
Huvet
A.
&
Paul-Pont
I.
2019
Surface functionalization determines behavior of nanoplastics solutions in model aquatic environments
.
Chemosphere
225
,
639
646
.
https://doi.org/10.1016/j.chemosphere.2019.03.077
.
Valsesia
A.
,
Parot
J.
,
Ponti
J.
,
Mehn
D.
,
Marino
R.
,
Melillo
D.
,
Muramoto
S.
,
Verkouteren
M.
,
Hackley
V. A.
&
Colpo
P.
2021
Detection, counting and characterization of nanoplastics in marine bioindicators: A proof of principle study
.
Microplastics and Nanoplastics
1
(
1
),
1
13
.
https://doi.org/10.1186/s43591-021-00005-z
.
Wahl
A.
,
Le Juge
C.
,
Davranche
M.
,
Hadri
H. E.
,
Grassl
B.
,
Reynaud
S.
&
Gigault
J.
2021
Nanoplastics occurrence in a soil amended with plastic debris
.
Chemosphere
262
.
https://doi.org/10.1016/j.chemosphere.2020.127784
.
Wang
X.
,
Liang
D.
,
Wang
Y.
,
Peijnenburg
W. J. G. M.
,
Monikh
F. A.
,
Zhao
X.
,
Dong
Z.
&
Fan
W.
2022
A critical review on the biological impact of natural organic matter on nanomaterials in the aquatic environment
.
Carbon Research
1
(
1
),
1
18
.
https://doi.org/10.1007/s44246-022-00013-5
.
Xu
Y.
,
Ou
Q.
,
Jiao
M.
,
Liu
G.
&
Van Der Hoek
J. P.
2022
Identification and quantification of nanoplastics in surface water and groundwater by pyrolysis gas chromatography-mass spectrometry
.
Environmental Science and Technology
56
(
8
),
4988
4997
.
https://doi.org/10.1021/acs.est.1c07377
.
Xu
Y.
,
Ou
Q.
,
Wang
X.
,
Hou
F.
,
Li
P.
,
van der Hoek
J. P.
&
Liu
G.
2023
Assessing the mass concentration of microplastics and nanoplastics in wastewater treatment plants by pyrolysis gas chromatography-mass spectrometry
.
Environmental Science and Technology
57
(
8
),
3114
3123
.
https://doi.org/10.1021/acs.est.2c07810
.
Zhang
Y.
,
Luo
Y.
,
Yu
X.
,
Huang
D.
,
Guo
X.
&
Zhu
L.
2022
Aging significantly increases the interaction between polystyrene nanoplastics and minerals
.
Water Research
219
,
118544
.
https://doi.org/10.1016/j.watres.2022.118544
.
Zhou
X. X.
,
Hao
L. T.
,
Wang
H. Y. Z.
,
Li
Y. J.
&
Liu
J. F.
2019
Cloud-point extraction combined with thermal degradation for nanoplastics analysis using pyrolysis gas chromatography-mass spectrometry
.
Analytical Chemistry
91
(
3
),
1785
1790
.
https://doi.org/10.1021/acs.analchem.8b04729
.
Zuccarello
P.
,
Ferrante
M.
,
Cristaldi
A.
,
Copat
C.
,
Grasso
A.
,
Sangregorio
D.
,
Fiore
M.
&
Oliveri Conti
G.
2019
Exposure to microplastics (<10 Μm) associated to plastic bottles mineral water consumption: The first quantitative study
.
Water Research
157
,
365
371
.
https://doi.org/10.1016/j.watres.2019.03.091
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

Supplementary data