Introduction: Exposure to hazardous chemicals released during hairdressing activities from hair care products puts hairdressers at risk of adverse health effects. Safety assessments of hair products are mainly focused on consumers, but exposure for professional hairdressers might be substantially higher. Objective: To identify and assess available research data on inhalation exposures of professional hairdressers. Methods: A systematic search of studies between 1 January 2000 and 30 April 2021 was performed in Medline, Embase, Web of Science and in Cochrane registry, toxicological dossiers of the Scientific Committee on Consumer Safety (SCCS) of the European Commission as well as the German MAK Commission. Studies reporting quantitative data on airborne concentrations of chemicals in the hairdresser’s workplace were considered. The outcome was an airborne concentration of chemicals in the working environment, which was compared, when possible, with current occupational exposure limits (OEL) or guidance levels. Results: In total, 23 studies performed in 14 countries were included. The average number of hairdressing salons per study was 22 (range 1–62). Chemicals most frequently measured were formaldehyde (n = 8), ammonia (n = 5), total volatile organic compounds (TVOC) (n = 5), and toluene (n = 4). More than fifty other chemicals were measured in one to three studies, including various aromatic and aliphatic organic solvents, hydrogen peroxide, persulfate, and particulate matter. Most studies reported environmental air concentrations, while personal exposure was measured only in seven studies. The measured air concentrations of formaldehyde, ammonia, and TVOC exceeded OEL or guidance values in some studies. There was large variability in measuring conditions and reported air concentrations differed strongly within and between studies. Conclusion: Hairdressers are exposed to a wide spectrum of hazardous chemicals, often simultaneously. Airborne concentrations of pollutants depend on salon characteristics such as ventilation and the number of customers but also on used products that are often country- or client-specific. For exposure to formaldehyde, ammonia, and TVOC exceeding OELs or guidance values for indoor air was observed. Therefore, occupational exposure should be taken into account by safety regulations for hair care products.

An individual’s likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures.