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The search and extraction of experimental ecotoxicological information is often a tedious work. A good and comprehensive data source is the US EPA ECOTOX Knowledgebase. It contains more than 1 million data points for almost 13,000 chemicals and 14,000 single species. However, for a high-throughput hazard assessment, it is not possible to extract all relevant data of the online database. The purpose of REcoTox is to extract the relevant information and to aggregate the data based on the user criteria out of the entire database ASCII files. REcoTox is a semi-automated, interactive workflow to process US EPA ECOTOX Knowledgebase entire database ASCII files to extract and process ecotoxicological data relevant (but not restricted) to the eco toxicity groups algae, crustaceans, and fish in the aquatic domain. The focus is aquatic ecotoxicity and the unit of the retrieved data is mg/L. The document is the vignette document to train people on the usage of REcoTox.

Presentation given on an online non-target screening cross-working group meeting of the NORMAN Association on 07 November 2023.

Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.

There is increasing awareness that chemical pollution of freshwater systems with complex mixtures of chemicals from domestic sources, agriculture and industry may cause a substantial chemical footprint on water organisms, pushing aquatic ecosystems outside the safe operating space. The present study defines chemical footprints as the risk that chemicals or chemical mixtures will have adverse effects on a specific group of organisms. The aim is to characterise these chemical footprints in European streams based on a unique and uniform screening of more than 600 chemicals in 445 surface water samples, and to derive site- and compound-specific information for management prioritisation purposes. In total, 504 pesticides, biocides, pharmaceuticals and other compounds have been detected, including frequently occurring and site-specific compounds with concentrations up to 74 µg/L. Key finding is that three-quarter of the investigated sites in 22 European river basins exceed established thresholds for chemical footprints in freshwater, leading to expected acute or chronic impacts on aquatic organisms. The largest footprints were recorded on invertebrates, followed by algae and fish. More than 70 chemicals exceed thresholds of chronic impacts on invertebrates. For all organism groups, pesticides and biocides were the main drivers of chemical footprints, while mixture impacts were particularly relevant for invertebrates. No clear significant correlation was found between chemical footprints and the urban discharge fractions, suggesting that effluent-specific quality rather than the total load of treated wastewater in the aquatic environment and the contribution of diffuse sources, e.g. from agriculture, determine chemical footprints.

The transformation to digital manufacturing has become increasingly critical for companies to remain competitive and achieve efficient manufacturing processes. However, manufacturing operations are often plagued by suboptimal allocation of resources, which can lead to higher costs and lower productivity. Digitalization has the potential to address these challenges by enabling real-time data monitoring, reducing quality costs, and improving product quality. Previous studies have shown that digital manufacturing can improve the efficiency of manufacturing processes and lead to productivity increases in organizations. However, despite these advantages, many digital innovation projects in manufacturing fall short of their initial ambitions, often resulting in incremental improvements to an existing manufacturing system. This is partly due to the challenges faced by manufacturing companies in quantifying the added value versus the costs of digitization technologies. Therefore, the objective of this paper is to propose an adaptive solution approach that addresses the need of aiding the decision process in selecting and assessing digital technologies to reduce wastage in manufacturing processes. The approach combines the 'Makigami' methodology, an 'Activity Diagram' (AD) modelling methodology, and a simplified 'Flow Chart', representing an aggregated view of the more detailed AD via a custom modelling schema, into one coherent framework. We further introduce the 'Methods-Misallocation-Measure' (3M-Graph) framework, which maps methods onto elements of wastage and misallocation, and subsequently assigns potential countermeasures. This tripartite mapping facilitates the identification of wastage during process analysis, the allocation of digital optimization measures and eases the assessment of cost effectiveness. The proposed approach aims to improve process efficiency and reduce wastage in manufacturing through digitalization. We conduct a case study of the approach and its

Innovation in mass spectrometry (MS) and the rapidly increasing throughput and sensitivity of MS instrumentation require adaptations and innovations in data processing tools. Here, we introduce MZmine 3, a scalable MS data analysis platform that supports hybrid datasets from various instrumental setups, including liquid and gas chromatography (LC and GC)–MS, ion mobility spectrometry (IMS)–MS and MS imaging. In particular, the integration of IMS–MS imaging and LC–IMS–MS datasets provides opportunities for spatial metabolomics analyses with increased annotation confidence.

Increasing production and use of chemicals and awareness of their impact on ecosystems and humans has led to large interest for broadening the knowledge on the chemical status of the environment and human health by suspect and non-target screening (NTS). To facilitate effective implementation of NTS in scientific, commercial and governmental laboratories, as well as acceptance by managers, regulators and risk assessors, more harmonisation in NTS is required. To address this, NORMAN Association members involved in NTS activities have prepared this guidance document, based on the current state of knowledge. The document is intended to provide guidance on performing high quality NTS studies and data interpretation while increasing awareness of the promise but also pitfalls and challenges associated with these techniques. Guidance is provided for all steps; from sampling and sample preparation to analysis by chromatography (liquid and gas—LC and GC) coupled via various ionisation techniques to high-resolution tandem mass spectrometry (HRMS/MS), through to data evaluation and reporting in the context of NTS. Although most experience within the NORMAN network still involves water analysis of polar compounds using LC–HRMS/MS, other matrices (sediment, soil, biota, dust, air) and instrumentation (GC, ion mobility) are covered, reflecting the rapid development and extension of the field. Due to the ongoing developments, the different questions addressed with NTS and manifold techniques in use, NORMAN members feel that no standard operation process can be provided at this stage. However, appropriate analytical methods, data processing techniques and databases commonly compiled in NTS workflows are introduced, their limitations are discussed and recommendations for different cases are provided. Proper quality assurance, quantification without reference standards and reporting results with clear confidence of identification assignment complete the guidance together with a gloss

Increasing production and use of chemicals and awareness of their impact on ecosystems and humans has led to large interest for broadening the knowledge on the chemical status of the environment and human health by suspect and non-target screening (NTS). To facilitate effective implementation of NTS in scientific, commercial and governmental laboratories, as well as acceptance by managers, regulators and risk assessors, more harmonisation in NTS is required. To address this, NORMAN Association members involved in NTS activities have prepared this guidance document, based on the current state of knowledge. The document is intended to provide guidance on performing high quality NTS studies and data interpretation while increasing awareness of the promise but also pitfalls and challenges associated with these techniques. Guidance is provided for all steps; from sampling and sample preparation to analysis by chromatography (liquid and gas—LC and GC) coupled via various ionisation techniques to high-resolution tandem mass spectrometry (HRMS/MS), through to data evaluation and reporting in the context of NTS. Although most experience within the NORMAN network still involves water analysis of polar compounds using LC–HRMS/MS, other matrices (sediment, soil, biota, dust, air) and instrumentation (GC, ion mobility) are covered, reflecting the rapid development and extension of the field. Due to the ongoing developments, the different questions addressed with NTS and manifold techniques in use, NORMAN members feel that no standard operation process can be provided at this stage. However, appropriate analytical methods, data processing techniques and databases commonly compiled in NTS workflows are introduced, their limitations are discussed and recommendations for different cases are provided. Proper quality assurance, quantification without reference standards and reporting results with clear confidence of identification assignment complete the guidance together with a gloss