Chronic exposure to low concentrations of volatile organic compounds (VOCs), such as chlorobenzene, is not being monitored in industrializing countries, although VOC exposure is associated with carcinogenic, organ-toxic, and endocrine-disrupting effects. Current VOC-sensing technologies are inaccessible due to high cost, size, and maintenance or are ineffective due to poor sensitivity or reliability. In particular, marginalized individuals face barriers to traditional prescription VOC treatments due to cost, lack of transportation, and limited access to physicians; thus, alternative treatments are needed. Here, we created a novel cumulative wearable color-changing VOC sensor with a paper-based polydiacetylene sensor array for chlorobenzene. With a single smartphone picture, the sensor displays 14 days of logged chlorobenzene exposure data, interpreted by machine-learning (ML) techniques, including principal component analysis. Further, we explored the efficacy of affordable and accessible treatment options to mitigate a VOC's toxic effects. Vitamin D and sulforaphane are naturally found in cruciferous vegetables, like broccoli, and can be used to treat chlorobenzene-mediated bone degradation. Our platform combines these components into a smartphone app that photographs the sensor's colorimetric data, analyzes the data via ML techniques, and offers accessible treatments based on exposure data.
Keywords: chlorobenzene; chronic exposure; machine learning (ML); osteopenia; smartphone integration; vitamin D; volatile organic compounds (VOCs); wearable sensor.