Suchergebnisse

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch) ; Solid waste management (SWM) is a significant challenge for the Seychelles. Waste generation, fueled by economic development and tourism, increases steadily, while landfilling continues to be the main disposal path, thus exacerbating the island nation's specific weaknesses. Due to the small scale of the Seychelles economy, there is little capital available to stimulate innovations in SWM and generate the knowledge for setting priorities and guiding SWM action. Students from ETH Zurich and UniSey conducted a transdisciplinary case study (tdCS) to fill this knowledge gap and gain insights into the obstacles and opportunities related to sustainable SWM. The tdCS approach allowed students to gain comprehensive and in-depth knowledge about the SWM system required to set priorities for action and next steps. The government should streamline the different financial frameworks according to a clear principle (e.g., polluter pays principle). Specific biogenic waste streams represent a potential source of energy and fertilizers. Expanding the scope and densifying the network of collection points could help raise recycling rates of other waste fractions. Diverting biogenic waste and recycling more glass, metals, paper, and plastics would also significantly reduce landfilling rates. Regardless of future amounts of waste ending up on landfills, the latter must be reengineered before the surrounding environment suffers major adverse impacts. All these actions imply a government-driven approach which integrates the views of stakeholders and consumers alike.

Abstract
Background
Applying non-target analysis (NTA) in regulatory environmental monitoring remains challenging—instead of having exploratory questions, regulators usually already have specific questions related to environmental protection aims. Additionally, data analysis can seem overwhelming because of the large data volumes and many steps required. This work aimed to establish an open in silico workflow to identify environmental chemical unknowns via retrospective NTA within the scope of a pre-existing Swiss environmental monitoring campaign focusing on industrial chemicals. The research question addressed immediate regulatory priorities: identify pollutants with industrial point sources occurring at the highest intensities over two time points. Samples from 22 wastewater treatment plants obtained in 2018 and measured using liquid chromatography–high resolution mass spectrometry were retrospectively analysed by (i) performing peak-picking to identify masses of interest; (ii) prescreening and quality-controlling spectra, and (iii) tentatively identifying priority "known unknown" pollutants by leveraging environmentally relevant chemical information provided by Swiss, Swedish, EU-wide, and American regulators. This regulator-supplied information was incorporated into MetFrag, an in silico identification tool replete with "post-relaunch" features used here. This study's unique regulatory context posed challenges in data quality and volume that were directly addressed with the prescreening, quality control, and identification workflow developed.

Results
One confirmed and 21 tentative identifications were achieved, suggesting the presence of compounds as diverse as manufacturing reagents, adhesives, pesticides, and pharmaceuticals in the samples. More importantly, an in-depth interpretation of the results in the context of environmental regulation and actionable next steps are discussed. The prescreening and quality control workflow is openly accessible within the R package Shinyscreen, and adaptable to any (retrospective) analysis requiring automated quality control of mass spectra and non-target identification, with potential applications in environmental and metabolomics analyses.

Conclusions
NTA in regulatory monitoring is critical for environmental protection, but bottlenecks in data analysis and results interpretation remain. The prescreening and quality control workflow, and interpretation work performed here are crucial steps towards scaling up NTA for environmental monitoring.