RESUMEN
Primary aromatic amines (PAAs) are a class of hazardous substances where many compounds are classified as carcinogen, mutagen, and reproduction toxin (CMR). PAAs can be taken up by dermal exposure. In the polyurethane industry, a valid and trustworthy method for the quantification of PAAs in the presence of isocyanates that could interfere is of great interest, especially on workplaces where a regular contact to PAAs cannot be excluded. The aim of this work is the development, validation, and verification of a novel sampling device to quantify selectively the PAA load on work surfaces. We describe the synthesis of Cell-ßALA-PEMSA analytical papers and their characterization by infrared spectroscopy and thermogravimetric analysis. The recovery of TDA and MDA spiked on these filters is satisfactory. An excellent selectivity of Cell-ßALA-PEMSA papers towards PAAs in the presence of isocyanates of almost 100% was found by wipe tests of amine/isocyanate contaminated surfaces. First positive field tests were achieved at certain areas in a Polyurethane Technical Application Department where surface contamination with PAAs was expected, and the Cell-ßALA-PEMSA analytical papers were superior to an established method of surface sampling. However, recovery of these amines from surfaces shows a large variability, and more work is required to address influencing surface properties.
Asunto(s)
Exposición Profesional , Poliuretanos , Humanos , Aminas/química , Sustancias Peligrosas , IsocianatosRESUMEN
Measured data are generally preferred to modelled estimates of exposure. Grouping and read-across is already widely used and accepted approach in toxicology, but an appropriate approach and guidance on how to use existing exposure measurement data on one substance and work situation for another substance and/or work situation is currently not available. This study presents a framework for an extensive read-across of existing worker inhalable exposure measurement data. This framework enables the calculation of read-across factors based on another substance and/or work situation by first evaluating the quality of the existing measurement data and then mapping its similarity or difference with another substance and/or work situation. The system of read-across factors was largely based on the determinants in ECETOC TRA and ART exposure models. The applicability of the framework and its proof of principle were demonstrated by using five case studies. In these case studies, either the 75th percentiles of measured exposure data was observed to lie within the estimated 90% confidence intervals from the read-across approach or at least with the increase in the geometric mean of measured exposure, geometric mean of estimated exposure also increased. Testing and re-evaluation of the present framework by experts in exposure assessment and statistics is recommended to develop it further into a tool that can be widely used in exposure assessment and regulatory practices.