@misc{menger_integration_of_2023, 
author={Menger, F.,Andersson, P.L.,Weiss M.J.},
title={Integration of chemicals market data with suspect screening using in silico tools to identify potential new and emerging risk chemicals},
year={2023},
howpublished = {book part},
doi = {https://doi.org/10.1007/698_2023_1056},
abstract = {Early identification of new and emerging risk chemicals (NERCs) is critical in protecting human and environmental health while chemical invention and production is growing on a global market. Chemicals market data is information on the production, import, and use of chemicals in materials and products. By integration of chemicals market data with suspect screening strategies NERCs could potentially be detected early. In silico tools play an important role in this integration to identify blind spots in current analytical approaches and in identification of the potentially most hazardous chemicals. This chapter starts with a brief presentation of the term “chemicals market data.” The integrated approach is then presented in three steps: (1) Data collection and curation, (2) Scoring, ranking, and filtering, and (3) Suspect screening. Each step is first presented conceptually and then exemplified with use cases from the authors. The use of chemicals market data provides a solid basis for identification of true NERCs with confidence, and true and false negative findings can more confidently be distinguished. Chemicals market data should be provided to authorities and researchers so that early warning systems for NERCs can be installed and analytical blind spots identified and addressed.},
note = {Online available at: \url{https://doi.org/10.1007/698_2023_1056} (DOI). Menger, F.; Andersson, P.; Weiss M.J.: Integration of chemicals market data with suspect screening using in silico tools to identify potential new and emerging risk chemicals. In: Screening of Pollutants in the Environment: Non-target Strategies and Latest Trends, Book Series: The Handbook of Environmental Chemistry. Berlin, Heidelberg: Springer. 2023. DOI: 10.1007/698_2023_1056}}