Published on in Vol 1, No 1 (2017): Jan-Dec
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/8370, first published
.
Journals
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- Govorko M, Fritschi L, Reid A. Accuracy of a mobile app to identify suspect asbestos-containing material in Australian residential settings. Journal of Occupational and Environmental Hygiene 2018;15(8):598 View
- Wu P, Mjörnell K, Mangold M, Sandels C, Johansson T. A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories. Sustainability 2021;13(14):7836 View
- Bloise A, Miriello D. Distinguishing asbestos cement from fiber-reinforced cement through portable µ-Raman spectroscopy and portable X-ray fluorescence. Environmental Monitoring and Assessment 2022;194(10) View
- Wu P, Sandels C, Johansson T, Mangold M, Kristina Mjörnell . Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings. Resources, Conservation and Recycling 2023;199:107253 View