Austria must recycle more packaging materials. Especially for plastic packaging waste, significant increases are necessary to reach the EU recycling targets for 2025 and 2030. In addition to improving separate collection and introducing a deposit system for specific fractions, the share of plastic packaging in mixed municipal solid waste (MSW) could be utilized. In Austria, about 1.8milliontonnes of mixed MSW are generated. This includes about 110,000 t/a of plastic packaging waste. Most of the mixed MSW (94 %) is sent directly or via residues from pre-treatment, such as mechanical-biological treatment or waste sorting, to waste incineration. While materials such as glass and metals can also be recovered from the bottom ash, combustible materials such as plastics must be recovered before incineration. This work aims to evaluate the recovery potential of plastic packaging waste in mixed MSW with automated waste sorting. For this purpose, two of the largest Austrian waste sorting plants, with a total annual throughput of about 280,000 t/a, were investigated. The investigation included regular sampling of selected output streams and sorting analysis. The results show that the theoretical recovery potential of plastic packaging from these two plants is 6,500 t/a on average. An extrapolation to Austria results in a potential of about 83,000 t/a. If losses due to further treatment, such as sorting and recycling, are considered, about 30,000 t/a of recyclate could be returned to plastic production. This would correspond to an increase in plastic packaging recycling rate from 25 % to 35 %.

The low recycling rate of post-consumer plastic packaging waste (PPW), which is partly due to insufficient separate collection, heterogeneous composition and high levels of contamination, poses a challenge in Austria, where the recycling rate must double in order to meet the target of 55 %. This study analyzes key packaging characteristics of non-beverage plastic bottles influencing recyclability, using Vienna as a case study. Additionally, a net quantity indicator and separate collection rates were calculated. 738 bottles from mixed MSW and 1,159 bottles from separate PPW collection were analyzed. The main polymer's proportion described by the net quantity indicator was higher for bottles from separate collection (69-72 %) than from mixed MSW (58 %), showing that a large share of the foreign materials are residues and dirt, with significantly higher contents in mixed MSW (20 %) than in separate collection (11 %). With a separate collection rate of 19.2 %, the great potential for recycling currently lies in mixed MSW at 4,112 t/yr. Thereof, 46 % is uncolored, 54 % is colored/white and, in terms of material grade, 30 % is food grade. The most common filling volume for PET, PP and HDPE was 0.5 < x ≤ 1.5 L (23-59 %) and the most common decoration technology was label (60-85 %). PET and PP had the highest shares of food-grade bottles (37-46 %), while PP had the highest share of colored bottles (22-31 %). The mechanical recycling potential of bottles depends largely on packaging characteristics, influencing separate collection and also automatic sorting. Harmonized design specifications are therefore crucial for this heterogeneous PPW fraction.

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models.

As of January 2025, all EU member states must implement separate textile collection, but contamination (e.g., dirt, moisture) remains a challenge for sorting. NIR spectroscopy is a promising technology for automated textile sorting, though issues like contaminated materials still hinder its performance. This study showcases how contamination affects the identification of textiles by analyzing samples from residual waste with varying contamination levels. The impact of contamination on the classification accuracy of cotton-rich, polyester-rich, and polycotton textiles was investigated, as well as the resulting spectral differences and the identification of contaminants. Results indicate that moisture presence significantly influences spectral behavior, affecting the classification models' performance. Excluding the most moisture-sensitive wavelengths (1368-1459 nm) improved classification accuracy for polyester-rich samples. However, this exclusion further complicated the already challenging classification of polycotton samples, while cotton-rich accuracy experienced only a slight decline. Consequently, the average accuracy dropped from 83.1% to 78.9%. These findings suggest that excluding moisture-sensitive wavelengths can improve classification accuracy for specific material types (e.g., polyester-rich) but may increase misclassification risk for others (e.g., polycotton and cotton-rich), highlighting the need to tailor models to target fractions. While other contaminants affected classification less strongly, heavier surface contamination may require lowering the classification threshold to limit misclassification risk. This work can aid in improving NIR classification of contaminated post-consumer textiles and thereby support the circular economy of one of the fastest growing waste streams by enabling the recovery of resources that would otherwise be lost.

Mixed municipal solid waste (MSW) of cities and tourist-heavy areas typically contains elevated amounts of recyclable materials. In Austria, numerous material recovery facilities exist for processing this waste; however, they primarily focus on separating metals, neglecting the recovery potential of other recyclables. To evaluate such potential for polyolefins and paper-based materials, two pilot-scale trials were conducted in a model region in Tyrol, Western Austria, accompanied by comprehensive sampling, waste characterisation, and material flow analysis. Pre-concentrates with up to 70% purity were obtained using two stages of near-infrared sorting, although challenges arose due to the presence of textiles and composite materials. This study found that separating polyolefins from mixed MSW could increase recycling rates in the region by up to 16% (absolute). Paper recovery also showed a modest increase. Polyolefin recovery slightly lowered, whereas paper recovery moderately raised the heating value. Recycling such materials is technically feasible, and forthcoming legislative changes are expected to create a market for these materials. Although fundamental questions remain regarding the optimal balance between recycling and refuse-derived fuel, as well as concerns about microbiological or chemical hazards, it can enhance resource efficiency, develop circularity, and aid comparison in regions with similar demographic and tourism characteristics.

The low thickness of plastic films poses a challenge when using near-infrared (NIR) spectroscopy as it affects the spectral quality and classification. This research focuses on offering a solution to the challenge of classifying multilayer plastic film materials with a focus on polyolefin multilayer plastics. It presents the importance of spectral quality on accurate classification. The aim is to demonstrate the suitability of the handheld NIR spectrometer in classifying multilayer polyolefin films and assess the impact of various measuring backgrounds (white tile, Teflon, aluminum, copper, silver, and gold) on classification accuracy in the wavelength range of 1596–2396 nm. Metallic backgrounds have been found to enhance spectral quality and classification accuracy. The classification accuracy was consistently high, ranging from 96.55% to 100%, with aluminum and gold backgrounds yielding the best results in theoretical accuracy. In experimental classification, the accuracy reached 100% when any metallic backgrounds were used. Conversely, Teflon showed a theoretically high accuracy of 96.21% but only achieved an experimental accuracy of 72.2%. These findings suggest that using metallic backgrounds can improve the spectral quality and classification of plastics with low thickness (films) and complex material composition (multilayers).