Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically. 

The environmental impact is a strong incentive for the development of upcycling processes for textile waste. However, toxic chemicals may occur in both brand-new textiles and post-consumer garments, and the chemical transfer in such routes is important to investigate. The present study applied non-target screening and quantification with liquid chromatography/mass spectrometry to follow the fate of hazardous chemicals from post-consumer polycotton garments to a new material, cellulose nanocrystals, in a chemical upcycling utilizing strongly acidic conditions. The majority of hazardous chemicals detected within the process were found to be transferred to a residual of polyester material and not to the enriched cellulose. However, phthalates were found to be mainly attached to the cellulose nanocrystals. The detected total concentration, in this case, was below 5 μg/g, at least 200 times lower than the limit set by the European Union. This indicates the importance of monitoring and controlling the phthalate content in the starting material of the process, i.e., the post-consumer garments. The chemical release into the process waste effluent could be estimated based on water solubility data for chemicals under the applied conditions. Three compounds, the water-repellent substance perfluorooctanesulfonic acid and the dyes Crystal Violet and Victoria Pure Blue, were almost entirely transferred into the process waste effluent. Although the levels detected were very low in the present pilot process, their presence eventually indicates the need for wastewater purification at further upscaling, depending on the exposure and dose in relation to toxicological relevant thresholds. 

Many per- and polyfluoroalkyl substances (PFASs) have been identified in the environment, and some have been shown to be extremely persistent and even toxic, thus raising concerns about their effects on human health and the environment. Despite this, little is known about most PFASs. In this study, the comprehensive database of over 4700 PFAS entries recently compiled by the OECD was curated and the chemical variation was analysed in detail. The analysis revealed 3363 individual PFASs with a huge variation in chemical functionalities and a wide range of mixtures and polymers. A hierarchical clustering methodology was employed on the curated database, which resulted in 12 groups, where only half were populated by well-studied compounds thus indicating the large knowledge gaps. We selected both a theoretical and a procurable training set that covered a substantial part of the chemical domain based on these clusters. Several computational models to predict physicochemical and environmental fate related properties were assessed, which indicated their lack of applicability for PFASs and the urgent need for experimental data for training and validating these models. Our findings indicate reasonable predictions of the octanol-water partition coefficient for a small chemical domain of PFASs but large data gaps and uncertainties for water solubility, bioconcentration factor, and acid dissociation factor predictions. Improved computational tools are necessary for assessing risks of PFASs and for including suggested training set compounds in future testing of both physicochemical and effect-related data. This should provide a solid basis for better chemical understanding and future model development purposes.