Probabilistic Material Flow Analysis of Seven Commodity Plastics in Europe

Kawecki, Delphine; Scheeder, Paul R W; Nowack, Bernd

doi:10.1021/acs.est.8b01513

Cited by 163 publications

(109 citation statements)

References 44 publications

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“…To improve the quality of the model and the accuracy of the results, scientists need to use lower cutoffs when sampling microplastics and to test more secondary microplastics for ecotoxicity. Moreover, the main investigated polymer types in ecotoxicological studies represent only a small fraction of the polymers that flow through our society (Kawecki et al ), and data are missing on some of the most polluted rivers such as the Amazon and Ganges.…”

Section: Discussionmentioning

confidence: 99%

Toward an ecotoxicological risk assessment of microplastics: Comparison of available hazard and exposure data in freshwaters

Adam

Tong

Nowack

2019

Enviro Toxic and Chemistry

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141

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Microplastics have been detected in freshwaters all over the world in almost all samples, and ecotoxicological studies have shown adverse effects of microplastics on organisms. However, no risk assessment of microplastics has been performed specifically in freshwater so far. The aim of the present study was therefore to review all exposure and ecotoxicity data available for microplastics in freshwaters and to perform a preliminary probabilistic risk assessment. The exposure probability distribution was based on 391 concentrations measured in Asia, Europe, and North America. Because exposure data are mainly available in particle number-based metrics but results from hazard studies are mostly mass-based, the hazard results were converted into particle number concentrations. A statistical analysis of the hazard data showed that there was no significant influence of particle shape or type of polymer on the no-observed-effect concentration. The predicted-no-effect concentration (PNEC) was calculated as the fifth percentile of the probabilistic species sensitivity distribution, based on 53 values from 14 freshwater species, to have a mode of 7.4 Â 10 5 particles Á m À3 (25th and 75th quantiles of 6.1 Â 10 5 and 1.3 Â 10 6 particles Á m À3 , respectively). The exposure probability distribution was divided by the PNEC probability distribution to calculate risk characterization ratios (RCRs), with modes of 1.3 Â 10 À6 in North America, 3.3 Â 10 À6 in Europe, and 4.6 Â 10 À3 in Asia. Probability distributions associated with the RCRs showed that ecological risks cannot be entirely excluded in Asia, where 0.4% of the RCR values were above 1. Environ Toxicol Chem 2019;38:436-447. C 2018

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Section: Discussionmentioning

confidence: 99%

Toward an ecotoxicological risk assessment of microplastics: Comparison of available hazard and exposure data in freshwaters

Adam

Tong

Nowack

2019

Enviro Toxic and Chemistry

Self Cite

141

119

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“…Because our CVs represent the maximum spread of triangular distributions, we used a factor of 2.45 to convert them into standard deviations and use error propagation (Eqn. , based on Kawecki et al ).

{CV}_{NOEC, CNT} = \sqrt{{true(\frac{{CV}_{DP}}{2.45} true)}^{2} + {true(\frac{{CV}_{{UF}_{t}}}{2.45} true)}^{2} + {true(\frac{{CV}_{{UF}_{dd}}}{2.45} true)}^{2}} \times 2.45 .

…”

Section: Methodsmentioning

confidence: 99%

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Wigger

Kawecki

Nowack

et al. 2019

Integr Envir Assess & Manag

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The calculation of a species sensitivity distribution (SSD) is a commonly accepted approach to derive the predicted no‐effect concentration (PNEC) of a substance in the context of environmental risk assessment. The SSD approach usually is data demanding and incorporates a large number of ecotoxicological values from different experimental studies. The probabilistic SSD (PSSD) approach is able to fully consider the variability between different exposure conditions and material types, which is of great importance when constructing an SSD for any chemical, especially for nanomaterials. The aim of our work was to further develop the PSSD approach by implementing methods to better consider the uncertainty and variability of the input data. We incorporated probabilistic elements to consider the uncertainty associated with uncertainty factors by using probability distributions instead of single values. The new PSSD method (named “PSSD+”) computes 10 000 PSSDs based on a Monte Carlo routine. For each PSSD calculated, the hazardous concentration for 5% of species (HC5) was extracted to provide a PNEC distribution based on all data available and their associated uncertainty. The PSSD+ approach also includes the option to consider a species weighting according to a typically constituted biome. We applied this PSSD+ approach to a previously published data set on C nanotubes and Ag nanoparticles. The evaluation of the uncertainty factor distributions and species weighting have shown that the proposed PSSD method is robust with respect to the calculation of the PNEC value. Furthermore, we demonstrated that the PSSD+ can handle both small and more comprehensive data sets because the PNEC distributions are a close representation of the data available. Finally, the sensitivity testing toward data set variations showed that the maximum variation of the mean PNEC was of a factor of about 2, so that the method is relatively insensitive to missing data points as long as the most sensitive species is included. Integr Environ Assess Manag 2020;16:211–222. © 2019 SETAC

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“…PET (C 10 H 8 O 4 ) n ) is the most abundant thermoplastic due to its characteristics of existing in different forms such as a one-dimensional fibre, 2D films, and 3D bottles and containers [18,19]. It is the thirdmost consumed polymer in Europe after PP and LDPE, and the most widely used plastic in the packaging industry [20][21][22]. The global consumption and demand for PET reached 60 Mt by 2011 and is increasing by 4.5% each year [23].…”

Section: Introductionmentioning

confidence: 99%

Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring

et al. 2020

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Background: Recycling the ever-increasing plastic waste has become an urgent global concern. One of the most convenient methods for plastic recycling is pyrolysis, owing to its environmentally friendly nature and its intrinsic properties. Understanding the pyrolysis process and the degradation mechanism is crucial for scale-up and reactor design. Therefore, we studied kinetic modelling of the pyrolysis process for one of the most common plastics, polyethylene terephthalate (PET). The focus was to better understand and predict PET pyrolysis when transitioning to a low carbon economy and adhering to environmental and governmental legislation. This work aims at presenting for the first time, the kinetic triplet (activation energy, pre-exponential constant, and reaction rate) for PET pyrolysis using the differential iso-conversional method. This is coupled with the in-situ online tracking of the gaseous emissions using mass spectrometry. Results: The differential iso-conversional method showed activation energy (E a) values of 165-195 kJ mol −1 , R 2 = 0.99659. While the ASTM-E698 method showed 165.6 kJ mol −1 and integral methods such as Flynn-Wall and Ozawa (FWO) (166-180 kJ mol −1). The in-situ Mass Spectrometry results showed the gaseous pyrolysis emissions, which are C 1 hydrocarbons and H-O-C=O along with C 2 hydrocarbons, C 5-C 6 hydrocarbons, acetaldehyde, the fragment of O-CH=CH 2 , hydrogen, and water. Conclusions: From the obtained results herein, thermal predictions (isothermal, non-isothermal and step-based heating) were determined based on the kinetic parameters. They can be used at numerous scale with a high level of accuracy compared with the literature.

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Probabilistic Material Flow Analysis of Seven Commodity Plastics in Europe

Cited by 163 publications

References 44 publications

Toward an ecotoxicological risk assessment of microplastics: Comparison of available hazard and exposure data in freshwaters

Toward an ecotoxicological risk assessment of microplastics: Comparison of available hazard and exposure data in freshwaters

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring

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