Sanitär- und Kommunaltechnik; Umwelttechnik
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Introduction: Effective disinfection is crucial to maintain hygiene and to prevent the spread of infections. Phenotypic heterogeneity in disinfection survival (i.e. tolerance) may result in failure of disinfection, which in turn may foster the evolution of resistance to both disinfectants and antibiotics. However, the consequences of phenotypic heterogeneity for disinfection outcome and resistance evolution are not well understood. Goal: This study investigates the impact of phenotypic heterogeneity on the survival and evolution of Escherichia coli during disinfection with six commonly used substances. Furthermore, the consequences of evolved disinfectant tolerance for antibiotic resistance evolution are studied. Materials & Methods: The extent of population heterogeneity during disinfection is derived by determining time-kill kinetics and analysis with mathematical modelling. The link between population heterogeneity and evolvability of disinfectant tolerance was assessed by laboratory evolution experiments under periodic disinfection. The ability of disinfectant tolerant strains to evolve antibiotic resistance is assessed by serial transfer experiments with increasing concentrations of different antibiotics and by whole genome sequencing. Results: Multi-modal time-kill kinetics in three of the six disinfectants suggest the presence of disinfectant-tolerant subpopulations (i.e. persister cells). Importantly, the ability and extent to evolve population-wide tolerance under periodic disinfection is related with the presence of persister cells and the level of phenotypic heterogeneity during disinfection. Interestingly, the probability of high-level resistance evolution to certain antibiotics is attenuated in disinfectant tolerant strains as compared to the sensitive ancestor. Whole-genome sequencing reveals epistatic interactions between disinfectant tolerance and antibiotic resistance mutations, preventing access to canonical evolutionary paths to resistance. Summary: Our findings suggest that phenotypic heterogeneity can facilitate disinfection survival and the evolution of population wide tolerance, which can impact future antibiotic resistance evolution.
Any surface in the environment acts as hotspot for microbial attachment and activity. These biofilms represent the interface between humans and the environment.
While in the past biofilms were often seen as disturbance, we now start to understand the enormous potential of beneficial biofilms. They can be used in a broad range of applications and are sources for new microorganisms and traits. After all, biofilms represent a great example for a collaborative lifestyle.
This presentation provides an introduction and intermediate results of the Horizon-Europe STAR4BBS project.It focuses on how to maximize the potential of Sustainability Certification Schemes (SCS) and labels to support a successful transition to a sustainable bio-based economy by assessing their effectiveness, robustness, and credibility.
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).