Sanitär- und Kommunaltechnik; Umwelttechnik
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The Microplastic Microbiome
(2024)
Microplastics represent man-made and newly emerging surfaces in our ecosystems, where they interact with microorganisms. The ecosystem in focus of this presentation will be the aquatic environment. It will be portrayed, which microorganisms use microplastics as a habitat, how environmental factors shape this colonization, and why the biodegradation of plastics in the ocean is an overall unlikely process. We will also discuss whether potentially pathogenic microorganisms use microplastics as a raft. Finally, possible adaptation mechanisms of plastic-colonizing microorganisms will be presented, such as the production of photoreactive molecules. The microplastic microbiome has a large potential to harbor so far unknown species with curious traits, representing an exciting research topic for the future.
We introduce a passive smart dust concept as a novel solution for environmental monitoring. Utilizing chemical reagents like colorimetric indicators and other chemosensors, these particles detect varying environmental conditions. We developed paper-based sensors that are both cost-effective and eco-friendly. In practical tests, these sensors, dispersed over a designated area, successfully identified hazardous substances by changing their color when exposed to acids or bases. This color change was remotely detectable using a drone-mounted color camera. The data thus obtained was processed through specialized software, accurately pinpointing areas of contamination. This method proves the efficacy and scalability of passive smart dust technology for real-time, environmentally sustainable remote sensing of hazardous materials
Use/distribution of cost-effective and biocompatible pieces of cellulose paper as passive optical chemosensors and readout by the camera system.
Modification of the paper surface using various chemically sensitive dyes (indicators) provides an optically detectable reaction and conclusions about the target substances and precise location.
Revolutionizing our polymer industry for adaption to a sustainable carbon circular economy has become one of today’s most demanding challenges. Exploiting renewable resources to replace fossil-fuel—based plastics with biopolymers such as poly(lactic acid) (PLA) is inevitable while using waste streams as a raw material resource at least is promising. When it comes to using PLA as technical polymer, its high flammability must be addressed by flame retardants compatible with the thermoplastic processing of PLA and its compostability. This study proposes microalgae enriched with phosphorus from wastewater (P-Algae) as an elegant way towards a kind of sustainable organophosphorus flame retardant. The concept is demonstrated by investigating the processing, pyrolysis, flammability, and fire behavior of PLA/P-Algae, while varying the P-Algae content and comparing P-Algae with four alternative bio-fillers (phosphorylated lignin, biochar, thermally treated sewage sludge, and metal phytate) with different P-contents as meaningful benchmarks.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models.
Per- and polyfluoroalkyl substances (PFAS) were classified as a group of highly concerning chemicals over the last decades. Because of the high persistence of PFAS, their previous use led to contamination of the environment and human population. Due to ongoing use and incomplete remediation or destruction methods, the PFAS background in the environment is growing continuously.
The presence of ultrashort PFAS (with ≤3 carbon atoms) in the environment is often overlooked despite their contributions to PFAS levels. These compounds may directly contaminate the environment and arise from degradation of polyfluorinated compounds as well as incomplete PFAS destruction in certain industrial processes. Nonetheless, few approaches for targeted analysis of ultrashort PFASs have been developed, except for trifluoroacetic acid (TFA). Beside liquid (LC-MS/MS), supercritical fluid (SFC-MS/MS), and ion chromatography (IC-MS)-based systems, gas chromatography coupled with mass spectrometry (GC-MS) is a promising method for detection and quantification of ultrashort PFAS.
The goal of this study was to develop a simple headspace GC-MS method for the quantification of ultrashort perfluorocarboxylic acids (PFCAs) and polyfluorinated alcohols (PFOHs) in water samples. In contrast to PFOHs, functionalization of PFCAs was required for quantification. This was done by esterification with methanol at 80 °C, which can be carried out directly in the headspace GC-MS system.
Moreover, several parameters were optimized to achieve a low limit of quantification (LOQ) for the analytes used: i) The ratio of the aqueous solution, methanol, and available gas phase within the analysis vessel, ii) the concentration of additional acid in esterification mixtures of PFCAs, iii) shaking frequency and iv) shaking time before analysis. After optimizing the procedure, we were able to quantify ultrashort PFCAs and PFOHs. Thus, our developed headspace GC-MS method has the potential to be used as an alternative target analysis for ultrashort-chain PFCAs and PFOHs in various water samples (groundwater, wastewater).
Monitoring of microplastics in food matrices is crucial to determinate the human exposure. By direct ingestion microplastics could be released in the food during the production, through packaging and by consumer’s use. The absence of standard methods to quantify and detect different size range and type of microplastics has led to difficult and time consuming procedural steps, poor accuracy and lack of comparability. In this work, matrix characterization and laboratory experiments were used to investigate the efficiency of sample preparation in milk powder. This information is crucial to compile a standard procedure for sample preparation and digestion of common milk powder to detect different particle sizes and types of polymers. Charaterisation is done by TGA and TOC measurements.
Following the recommendation of the European Chemicals Agency, on 25 September 2023 the European Commission passed a comprehensive new regulation to reduce emissions of microplastics (MP) into the environment, which includes the sale and use of intentionally added MP.1,2 This also applies to the application of synthetic rubber granulate infill in artificial turf systems and will ultimately have an impact on recreational sports. In Germany, rubber granulate made of ethylene-propylene-diene-monomer rubber (EPDM) is currently predominantly used. So far, there is no sufficient database for estimating MP emissions from artificial turf pitches into the environment and thus their relevance as a source of MP pollution.3 This topic is controversially discussed due to the complexity of sampling and analytics. To close this research gap, this project has the goal to determine mass balances for the emissions of MP from artificial turf pitches to allow an estimation of the amount of MP released per artificial turf pitch and year.
Within this study, MP emissions of three artificial turf scenarios at different time states (unaged, artificially aged and real-time aged) are compared: the past (old turf: fossil based, synthetic infill), present (most commonly installed in Europe: fossil based, EPDM infill), and the future (turf with recycled gras fibres, no synthetic infill). To simulate the outdoor weathering during the lifespan of an artificial turf of approx. 15 years, brand-new artificial turf and EPDM rubber granulate were accelerated aged by means of UV weathering and mechanical stress. Potential MP emissions into surface and groundwater are simulated by lysimeter and shake experiments. MP mass contents are subsequently determined by Thermal Extraction Desorption Gas Chromatography/Mass Spectrometry. Using special microfilter crucibles allows the estimation of the particle sizes of the emitted MP, which is a fundamental requirement for an assessment of potential health hazards for humans.