Plastic has been identified as an emerging contaminant in aquatic and terrestrial ecosystems. Uncertainties remain concerning the amounts present in the environment and the main responsible sources. In this study, the emissions of macro- and microplastics have been mapped for seven polymers in Switzerland. The modeling is based on a complete analysis of the flows from production and use to end-of-life using probabilistic material flow analysis. We estimate that 94 ± 34 g/capita/year of low-density polyethylene, 98 ± 50 g/cap/a of high-density polyethylene, 126 ± 43 g/cap/a of polypropylene, 24 ± 13 g/cap/a of polystyrene, 16 ± 12 g/cap/a of expanded polystyrene, 65 ± 36 g/cap/a of polyvinyl chloride, and 200 ± 120 g/cap/a of polyethylene terephthalate enter the Swiss environment. All polymers combined, 540 ± 140 and 73 ± 14 g/cap/a are emitted into soil as macroplastics and microplastics, respectively, and 13.3 ± 4.9 and 1.8 ± 1.1 g/cap/a are emitted into freshwater as macroplastics and microplastics, respectively. The leading emission pathway is littering for both terrestrial and aquatic environments. Construction, agriculture, and pre- and postconsumer processes cause important emissions of microplastics into soils, and postconsumer processes, textiles, and personal care products release most of the microplastics into waters. Because mass flows into soils are predicted to be 40 times larger than those into waters, more attention should be placed on this compartment. Our work also highlights the importance of referring to specific polymers instead of just “plastics”.

The omnipresence of plastics in our lives and their ever-increasing application range continuously raise the requirements for the monitoring of environmental and health impacts related to both plastics and their additives. We present a static probabilistic material flow analysis of seven polymers through the European and Swiss anthropospheres to provide a strong basis for exposure assessments of polymer-related impacts, which necessitates that the plastic flows from production to use and finally to waste management are well-understood. We consider seven different polymers, chosen for their popularity and application variety: low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (PP), polystyrene (PS), expanded polystyrene (EPS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET). We include synthetic textile products and consider trade flows at various stages of the life cycle, thus achieving a complete overview of the consumption for these polymers. In Europe, the order of consumption is PP > LDPE > PET > HDPE > PVC > PS > EPS. Textile products account for 42 ± 3% of the consumption of PET and 22 ± 4% of PP. Incineration is the major waste management method for HDPE, PS, and EPS. No significant difference between landfilling and incineration for the remaining polymers is found. The highest recycling share is found for PVC. These results can serve as a basis for a detailed assessment of exposure pathways of plastics or their additives in the environment or exposure of additives on human health.

Revealing the delicate balance between protonation/deprotonation and surface/bulk partitioning of organic acids and bases in aqueous solutions.

No abstract

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