The automotive industry, at 9.7 %, drives the third largest demand for virgin plastics after the packaging and construction industries in Europe. While increasing the recycling rates for the end of life (EoL) of vehicles is important, it is also imperative to close the loop by increasing the use of post-consumer recycled (PCR) plastics. The utilization of PCR materials remains largely constrained due to olfactory limitations in interior applications and due to technical and surface performance limitations in exterior applications. Novel and scalable manufacturing technologies like co-injection molding and in-label molding can improve the surface performance whilst increasing the share of PCR materials. This also addresses the new draft on EU-Regulation on circularity requirements for vehicle design which is expected to come into effect by the year 2032. The use of PCR materials, however, creates the issue of EoL allocation in the life cycle assessment of the primary and the recycled materials between their consecutive life cycles. There are multiple EoL allocation approaches like the cut-off approach, the avoided burdens approach, the Nordic LCA guidelines recommended 50/50 approaches, the EU-JRC PEF recommended circular footprint formula, and other market-price based allocation approaches. These approaches allocate the burdens of recycling and disposal of the materials differently between its consecutive lifecycles, thus affecting the overall LCA results for the recycled material. PCR polypropylene used to manufacture automotive carrosserie, specifically the bumpers, is used as a case-study to demonstrate how value choices in the selection of EoL approach can affect the overall life cycle assessment results. Results indicate that using disparate allocation approaches could potentially yield divergent outcomes, i.e., the inconsistent use of allocation approaches in different life cycles of materials might lead to double counting or complete avoidance of environmental impacts.
We used ten representative semivolatile organic compounds (SVOCs) to investigate how changes in temperature and particle concentration affect future concentrations and partitioning of SVOCs in the gas phase, particulate phase, and on surfaces in the indoor environment. Using quantum mechanical methods and quantitative structure-activity-relationship (QSAR) tools, accurate temperature-dependent octanol/air partition coefficients (K OA) and vapor pressures (P L) of the subcooled liquid were calculated. Under the pessimistic greenhouse gas emissions scenario SSP5-8.5 as projected by the Intergovernmental Panel on Climate Change (IPCC), an annual shift in the SVOC equilibrium concentration between gas phase, particle phase, and surface by up to 30% is expected until 2100. The SSP5-8.5 scenario leads to higher SVOC emission rates and changes in the organic film thickness on surfaces. However, since primarily annual averages are considered, these temperature-related changes are small. Nevertheless, a model calculation for the emission rate of DINCH, taking into account extreme daily indoor temperatures, was performed. Since many developments up to 2100 can only be estimated, simplifying assumptions were made for emission rates, film thickness, and other parameters.
Environmental surveys are essential tools to investigate the impact of pollutants on the living and non-living environment. Their data often form a basis to derive recommendations for preventive measures protecting health and ecosystems and identify the need for political action. Therefore, representative environmental data sets need to be free of systematic artifacts; their statistical structure should be explored and understood as good as possible. The German Environmental Survey (GerES) is a nationwide study conducted at unregular intervals. The data collected within the GerES V (2014-2017) campaign is important for recording and assessing pollutants in households with children and adolescents. Due to its sampling characteristics, GerES claims to be representative of the population in Germany and, with its standardized measurements and sampling protocols, as well as the selection of sampling points, provides a prime example of a study on the statistical nature of pollutants concentrations. In this work data sets from 19 pollutants in indoor air and house dust were selected from the GerES V pool. The parameters obtained from descriptive statistics were compared with the modeled data of a lognormal probability function and a lognormal cumulative density function. Confidence intervals were calculated using a bootstrap method. Monte-Carlo simulations were used to quantify uncertainties in estimators of theoretical distribution assumptions and to investigate the influence of classifying data into equidistant intervals (bins) on nonlinear regression analysis with respect to data count and bin width. The results of our study provide better insight into the general statistical nature of environmental observations, enabling a more reliable assessment of the parameters derived from the data.
Determining formaldehyde emissions is a key component of source control for wood-based panels. The non-linear WKI_2 equation, a modified form of the Andersen equation, is frequently used to deduce material-related emission parameters from measured test chamber concentrations and boundary conditions such as temperature (T), relative humidity (RH), air exchange (AC), and loading (L). The coefficients of the WKI_2 equation are determined using non-linear least-squares algorithms to ensure that the calculated steady-state concentrations correspond as closely as possible to the experimental data. Since the WKI_2 equation had previously only been verified for particleboard, the question arose whether it could also be used to describe the formaldehyde emissions of other wood-based panels. Therefore, a supplementary experimental dataset with medium-density fiberboard and plywood was provided and the statistical analysis showed very good agreement with the dataset for particleboard. Non-linear regression analysis is often sophisticated, as the six coefficients of the WKI_2 equation are partially correlated and the fitting procedure can be sensitive to the initial values. An alternative approach is neural network regression. In general, the advantage of machine learning lies in the recognition of non-linear patterns, which are difficult to represent in classical models without explicit physical assumptions, and in its greater robustness against measurement errors. However, neural networks require training to identify the optimal number/combination of hidden layers and neurons for the specific problem. The comparison conducted in this work has shown that both approaches, model function and machine learning, offer great potential for more precise and reliable emission predictions.
Clothing can act as a barrier and a source of skin exposure to chemicals due to reversible accumulation on and within textile fibers. The partition coefficient quantifies the equilibrium relationship between textile and air for a specific chemical and is a key parameter in models estimating dermal exposure. Here, textiles composed of natural and/or synthetic fibers were exposed for 26 days in environmental test chambers under different climatic conditions to semi-volatile organic compounds (SVOCs) that have similar, but not identical, physical and chemical properties. The nine textiles tested included a single fiber type or blends of cotton, polyester, nylon, linen and/or elastane. The seven SVOCs are found commonly indoors and in consumer products: 4-t-octylphenol (4t-OP),4-nonylphenol (4-NP); di-n-butyl adipate (DnBA); galaxolide (HHCB); di-n-butyl phthalate (DnBP); benzophenone-3 (BP-3) and 3-(4'-methylbenzylidene)camphor (4-MBC). The chamber air concentrations and masses accumulated on textiles were measured and the mass based (Km, m3/g), area based (Ka, m) and volume based (Kv, dimensionless) partition coefficients were calculated. Partition coefficients among all chemicals were generally lower for polyester and higher for cotton and blends. A hierarchical cluster analysis combined with fiber specific matrix analysis showed that, across the SVOCs tested, the partition coefficients for nylon/elastane were ~ 7 to 70 times higher than for jeans cotton, while the partition coefficient for jeans cotton were ~ 2 to 7 time higher than for polyester. Km, Ka and Kv were lowest for HHCB and highest for 4-NP, DnBP and 4-MBC. However, chemical-textile partition coefficients were not statistically correlated with respect to physical and environmental properties of the SVOCs. The values were statistically the same for different chamber air concentrations of the chemicals tested, and from 24 °C to 33 °C there was only a weak reduction in the partition coefficients.
